aroma.light/0000755000175000017500000000000014147402522012612 5ustar nileshnilesharoma.light/DESCRIPTION0000644000175000017500000000266514136064722014335 0ustar nileshnileshPackage: aroma.light Version: 3.24.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Authors@R: c( person("Henrik", "Bengtsson", role = c("aut", "cre", "cph"), email = "henrikb@braju.com"), person("Pierre", "Neuvial", role = "ctb"), person("Aaron", "Lun", role = "ctb")) Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. License: GPL (>= 2) biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues LazyLoad: TRUE Encoding: latin1 git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_14 git_last_commit: d0f8f2b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 NeedsCompilation: no Packaged: 2021-10-26 20:49:22 UTC; biocbuild Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson aroma.light/man/0000755000175000017500000000000014136047216013370 5ustar nileshnilesharoma.light/man/print.SmoothSplineLikelihood.Rd0000644000175000017500000000172514136047216021447 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % print.SmoothSplineLikelihood.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{print.SmoothSplineLikelihood} \alias{print.SmoothSplineLikelihood} \alias{SmoothSplineLikelihood.print} \alias{print,SmoothSplineLikelihood-method} \title{Prints an SmoothSplineLikelihood object} \description{ Prints an SmoothSplineLikelihood object. A SmoothSplineLikelihood object is returned by \code{\link{likelihood.smooth.spline}()}. } \usage{ \method{print}{SmoothSplineLikelihood}(x, digits=getOption("digits"), ...) } \arguments{ \item{x}{Object to be printed.} \item{digits}{Minimal number of significant digits to print.} \item{...}{Not used.} } \value{ Returns nothing. } \author{Henrik Bengtsson} \keyword{internal} \keyword{methods} aroma.light/man/backtransformPrincipalCurve.Rd0000644000175000017500000001345714136047216021374 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % backtransformPrincipalCurve.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{backtransformPrincipalCurve} \alias{backtransformPrincipalCurve} \alias{backtransformPrincipalCurve.numeric} \alias{backtransformPrincipalCurve.matrix} \title{Reverse transformation of principal-curve fit} \description{ Reverse transformation of principal-curve fit. } \usage{ \method{backtransformPrincipalCurve}{matrix}(X, fit, dimensions=NULL, targetDimension=NULL, ...) \method{backtransformPrincipalCurve}{numeric}(X, ...) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} containing data to be backtransformed.} \item{fit}{An MxL principal-curve fit object of class \code{principal_curve} as returned by \code{\link{fitPrincipalCurve}}(). Typically \eqn{L = K}, but not always. } \item{dimensions}{An (optional) subset of of D dimensions all in [1,L] to be returned (and backtransform).} \item{targetDimension}{An (optional) index specifying the dimension in [1,L] to be used as the target dimension of the \code{fit}. More details below.} \item{...}{Passed internally to \code{\link[stats]{smooth.spline}}.} } \value{ The backtransformed NxK (or NxD) \code{\link[base]{matrix}}. } \details{ Each column in X ("dimension") is backtransformed independently of the others. } \section{Target dimension}{ By default, the backtransform is such that afterward the signals are approximately proportional to the (first) principal curve as fitted by \code{\link{fitPrincipalCurve}}(). This scale and origin of this principal curve is not uniquely defined. If \code{targetDimension} is specified, then the backtransformed signals are approximately proportional to the signals of the target dimension, and the signals in the target dimension are unchanged. } \section{Subsetting dimensions}{ Argument \code{dimensions} can be used to backtransform a subset of dimensions (K) based on a subset of the fitted dimensions (L). If \eqn{K = L}, then both \code{X} and \code{fit} is subsetted. If \eqn{K <> L}, then it is assumed that \code{X} is already subsetted/expanded and only \code{fit} is subsetted. } \examples{ # Consider the case where K=4 measurements have been done # for the same underlying signals 'x'. The different measurements # have different systematic variation # # y_k = f(x_k) + eps_k; k = 1,...,K. # # In this example, we assume non-linear measurement functions # # f(x) = a + b*x + x^c + eps(b*x) # # where 'a' is an offset, 'b' a scale factor, and 'c' an exponential. # We also assume heteroscedastic zero-mean noise with standard # deviation proportional to the rescaled underlying signal 'x'. # # Furthermore, we assume that measurements k=2 and k=3 undergo the # same transformation, which may illustrate that the come from # the same batch. However, when *fitting* the model below we # will assume they are independent. # Transforms a <- c(2, 15, 15, 3) b <- c(2, 3, 3, 4) c <- c(1, 2, 2, 1/2) K <- length(a) # The true signal N <- 1000 x <- rexp(N) # The noise bX <- outer(b,x) E <- apply(bX, MARGIN=2, FUN=function(x) rnorm(K, mean=0, sd=0.1*x)) # The transformed signals with noise Xc <- t(sapply(c, FUN=function(c) x^c)) Y <- a + bX + Xc + E Y <- t(Y) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit principal curve # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit principal curve through Y = (y_1, y_2, ..., y_K) fit <- fitPrincipalCurve(Y) # Flip direction of 'lambda'? rho <- cor(fit$lambda, Y[,1], use="complete.obs") flip <- (rho < 0) if (flip) { fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda } L <- ncol(fit$s) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Backtransform data according to model fit # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Backtransform toward the principal curve (the "common scale") YN1 <- backtransformPrincipalCurve(Y, fit=fit) stopifnot(ncol(YN1) == K) # Backtransform toward the first dimension YN2 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1) stopifnot(ncol(YN2) == K) # Backtransform toward the last (fitted) dimension YN3 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=L) stopifnot(ncol(YN3) == K) # Backtransform toward the third dimension (dimension by dimension) # Note, this assumes that K == L. YN4 <- Y for (cc in 1:L) { YN4[,cc] <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1, dimensions=cc) } stopifnot(identical(YN4, YN2)) # Backtransform a subset toward the first dimension # Note, this assumes that K == L. YN5 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1, dimensions=2:3) stopifnot(identical(YN5, YN2[,2:3])) stopifnot(ncol(YN5) == 2) # Extract signals from measurement #2 and backtransform according # its model fit. Signals are standardized to target dimension 1. y6 <- Y[,2,drop=FALSE] yN6 <- backtransformPrincipalCurve(y6, fit=fit, dimensions=2, targetDimension=1) stopifnot(identical(yN6, YN2[,2,drop=FALSE])) stopifnot(ncol(yN6) == 1) # Extract signals from measurement #2 and backtransform according # the the model fit of measurement #3 (because we believe these # two have undergone very similar transformations. # Signals are standardized to target dimension 1. y7 <- Y[,2,drop=FALSE] yN7 <- backtransformPrincipalCurve(y7, fit=fit, dimensions=3, targetDimension=1) stopifnot(ncol(yN7) == 1) stopifnot(cor(yN7, yN6) > 0.9999) } \seealso{ \code{\link{fitPrincipalCurve}}() } \keyword{methods} aroma.light/man/fitIWPCA.Rd0000644000175000017500000001256314136047216015234 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % fitIWPCA.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{fitIWPCA} \alias{fitIWPCA} \alias{fitIWPCA.matrix} \title{Robust fit of linear subspace through multidimensional data} \description{ Robust fit of linear subspace through multidimensional data. } \usage{ \method{fitIWPCA}{matrix}(X, constraint=c("diagonal", "baseline", "max"), baselineChannel=NULL, ..., aShift=rep(0, times = ncol(X)), Xmin=NULL) } \arguments{ \item{X}{NxK \code{\link[base]{matrix}} where N is the number of observations and K is the number of dimensions (channels). } \item{constraint}{A \code{\link[base]{character}} string or a \code{\link[base]{numeric}} value. If \code{\link[base]{character}} it specifies which additional constraint to be used to specify the offset parameters along the fitted line; If \code{"diagonal"}, the offset vector will be a point on the line that is closest to the diagonal line (1,...,1). With this constraint, all bias parameters are identifiable. If \code{"baseline"} (requires argument \code{baselineChannel}), the estimates are such that of the bias and scale parameters of the baseline channel is 0 and 1, respectively. With this constraint, all bias parameters are identifiable. If \code{"max"}, the offset vector will the point on the line that is as "great" as possible, but still such that each of its components is less than the corresponding minimal signal. This will guarantee that no negative signals are created in the backward transformation. If \code{\link[base]{numeric}} value, the offset vector will the point on the line such that after applying the backward transformation there are \code{constraint*N}. Note that \code{constraint==0} corresponds approximately to \code{constraint=="max"}. With the latter two constraints, the bias parameters are only identifiable modulo the fitted line. } \item{baselineChannel}{Index of channel toward which all other channels are conform. This argument is required if \code{constraint=="baseline"}. This argument is optional if \code{constraint=="diagonal"} and then the scale factor of the baseline channel will be one. The estimate of the bias parameters is not affected in this case. Defaults to one, if missing. } \item{...}{Additional arguments accepted by \code{\link{iwpca}}(). For instance, a N \code{\link[base]{vector}} of weights for each observation may be given, otherwise they get the same weight. } \item{aShift, Xmin}{For internal use only.} } \value{ Returns a \code{\link[base]{list}} that contains estimated parameters and algorithm details; \item{a}{A \code{\link[base]{double}} \code{\link[base]{vector}} \eqn{(a[1],...,a[K])}with offset parameter estimates. It is made identifiable according to argument \code{constraint}. } \item{b}{A \code{\link[base]{double}} \code{\link[base]{vector}} \eqn{(b[1],...,b[K])}with scale parameter estimates. It is made identifiable by constraining \code{b[baselineChannel] == 1}. These estimates are independent of argument \code{constraint}. } \item{adiag}{If identifiability constraint \code{"diagonal"}, a \code{\link[base]{double}} \code{\link[base]{vector}} \eqn{(adiag[1],...,adiag[K])}, where \eqn{adiag[1] = adiag[2] = ... adiag[K]}, specifying the point on the diagonal line that is closest to the fitted line, otherwise the zero vector. } \item{eigen}{A KxK \code{\link[base]{matrix}} with columns of eigenvectors. } \item{converged}{\code{\link[base:logical]{TRUE}} if the algorithm converged, otherwise \code{\link[base:logical]{FALSE}}. } \item{nbrOfIterations}{The number of iterations for the algorithm to converge, or zero if it did not converge. } \item{t0}{Internal parameter estimates, which contains no more information than the above listed elements. } \item{t}{Always \code{\link[base]{NULL}}.} } \details{ This method uses re-weighted principal component analysis (IWPCA) to fit a the model \eqn{y_n = a + bx_n + eps_n} where \eqn{y_n}, \eqn{a}, \eqn{b}, and \eqn{eps_n} are vector of the K and \eqn{x_n} is a scalar. The algorithm is: For iteration i: 1) Fit a line \eqn{L} through the data close using weighted PCA with weights \eqn{\{w_n\}}. Let \eqn{r_n = \{r_{n,1},...,r_{n,K}\}} be the \eqn{K} principal components. 2) Update the weights as \eqn{w_n <- 1 / \sum_{2}^{K} (r_{n,k} + \epsilon_r)} where we have used the residuals of all but the first principal component. 3) Find the point a on \eqn{L} that is closest to the line \eqn{D=(1,1,...,1)}. Similarly, denote the point on D that is closest to \eqn{L} by \eqn{t=a*(1,1,...,1)}. } \author{Henrik Bengtsson} %examples "fitMultiIWPCA.matrix.Rex" \seealso{ This is an internal method used by the \code{\link{calibrateMultiscan}}() and \code{\link{normalizeAffine}}() methods. Internally the function \code{\link{iwpca}}() is used to fit a line through the data cloud and the function \code{\link{distanceBetweenLines}}() to find the closest point to the diagonal (1,1,...,1). } \keyword{methods} \keyword{algebra} aroma.light/man/normalizeQuantileRank.Rd0000644000175000017500000000713514136047216020204 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeQuantileRank.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeQuantileRank} \alias{normalizeQuantileRank} \alias{normalizeQuantileRank.numeric} \alias{normalizeQuantileRank.list} \alias{normalizeQuantile} \alias{normalizeQuantile.default} \title{Normalizes the empirical distribution of one of more samples to a target distribution} \usage{ \method{normalizeQuantileRank}{numeric}(x, xTarget, ties=FALSE, ...) \method{normalizeQuantileRank}{list}(X, xTarget=NULL, ...) \method{normalizeQuantile}{default}(x, ...) } \description{ Normalizes the empirical distribution of one of more samples to a target distribution. The average sample distribution is calculated either robustly or not by utilizing either \code{weightedMedian()} or \code{weighted.mean()}. A weighted method is used if any of the weights are different from one. } \arguments{ \item{x, X}{a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N or a \code{\link[base]{list}} of length N with \code{\link[base]{numeric}} \code{\link[base]{vector}}s. If a \code{\link[base]{list}}, then the \code{\link[base]{vector}}s may be of different lengths.} \item{xTarget}{The target empirical distribution as a \emph{sorted} \code{\link[base]{numeric}} \code{\link[base]{vector}} of length \eqn{M}. If \code{\link[base]{NULL}} and \code{X} is a \code{\link[base]{list}}, then the target distribution is calculated as the average empirical distribution of the samples.} \item{ties}{Should ties in \code{x} be treated with care or not? For more details, see "limma:normalizeQuantiles".} \item{...}{Not used.} } \value{ Returns an object of the same shape as the input argument. } \section{Missing values}{ Missing values are excluded when estimating the "common" (the baseline). Values that are \code{\link[base]{NA}} remain \code{\link[base]{NA}} after normalization. No new \code{\link[base]{NA}}s are introduced. } \section{Weights}{ Currently only channel weights are support due to the way quantile normalization is done. If signal weights are given, channel weights are calculated from these by taking the mean of the signal weights in each channel. } \examples{ # Simulate ten samples of different lengths N <- 10000 X <- list() for (kk in 1:8) { rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]] size <- runif(1, min=0.3, max=1) a <- rgamma(1, shape=20, rate=10) b <- rgamma(1, shape=10, rate=10) values <- rfcn(size*N, a, b) # "Censor" values values[values < 0 | values > 8] <- NA X[[kk]] <- values } # Add 20\% missing values X <- lapply(X, FUN=function(x) { x[sample(length(x), size=0.20*length(x))] <- NA x }) # Normalize quantiles Xn <- normalizeQuantile(X) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions") } \author{ Adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 \& 2006. Original code by Ben Bolstad at Statistics Department, University of California. } \seealso{ To calculate a target distribution from a set of samples, see \code{\link{averageQuantile}}(). For an alternative empirical density normalization methods, see \code{\link{normalizeQuantileSpline}}(). } \keyword{methods} \keyword{nonparametric} \keyword{multivariate} \keyword{robust} aroma.light/man/plotMvsAPairs.Rd0000644000175000017500000000273614136047216016433 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % plotMvsAPairs.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{plotMvsAPairs} \alias{plotMvsAPairs} \alias{plotMvsAPairs.matrix} \title{Plot log-ratios/log-intensities for all unique pairs of data vectors} \description{ Plot log-ratios/log-intensities for all unique pairs of data vectors. } \usage{ \method{plotMvsAPairs}{matrix}(X, Alab="A", Mlab="M", Alim=c(0, 16), Mlim=c(-1, 1) * diff(Alim), pch=".", ..., add=FALSE) } \arguments{ \item{X}{NxK \code{\link[base]{matrix}} where N is the number of observations and K is the number of channels.} \item{Alab,Mlab}{Labels on the x and y axes.} \item{Alim,Mlim}{Plot range on the A and M axes.} \item{pch}{Plot symbol used.} \item{...}{Additional arguments accepted by \code{\link[graphics]{points}}.} \item{add}{If \code{\link[base:logical]{TRUE}}, data points are plotted in the current plot, otherwise a new plot is created.} } \details{ Log-ratios and log-intensities are calculated for each neighboring pair of channels (columns) and plotted. Thus, in total there will be K-1 data set plotted. The colors used for the plotted pairs are 1, 2, and so on. To change the colors, use a different color palette. } \value{ Returns nothing. } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/robustSmoothSpline.Rd0000644000175000017500000001006514136047216017544 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % robustSmoothSpline.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{robustSmoothSpline} \alias{robustSmoothSpline.default} \alias{robustSmoothSpline} \title{Robust fit of a Smoothing Spline} \usage{ \method{robustSmoothSpline}{default}(x, y=NULL, w=NULL, ..., minIter=3, maxIter=max(minIter, 50), method=c("L1", "symmetric"), sdCriteria=2e-04, reps=1e-15, tol=1e-06 * IQR(x), plotCurves=FALSE) } \description{ Fits a smoothing spline robustly using the \eqn{L_1} norm. Currently, the algorithm is an \emph{iterative reweighted smooth spline} algorithm which calls \code{smooth.spline(x,y,w,...)} at each iteration with the weights \code{w} equal to the inverse of the absolute value of the residuals for the last iteration step. } \arguments{ \item{x}{a \code{\link[base]{vector}} giving the values of the predictor variable, or a \code{\link[base]{list}} or a two-column \code{\link[base]{matrix}} specifying \code{x} and \code{y}. If \code{x} is of class \code{smooth.spline} then \code{x$x} is used as the \code{x} values and \code{x$yin} are used as the \code{y} values.} \item{y}{responses. If \code{y} is missing, the responses are assumed to be specified by \code{x}.} \item{w}{a \code{\link[base]{vector}} of weights the same length as \code{x} giving the weights to use for each element of \code{x}. Default value is equal weight to all values.} \item{...}{Other arguments passed to \code{\link[stats]{smooth.spline}}.} \item{minIter}{the minimum number of iterations used to fit the smoothing spline robustly. Default value is 3.} \item{maxIter}{the maximum number of iterations used to fit the smoothing spline robustly. Default value is 25.} \item{method}{the method used to compute robustness weights at each iteration. Default value is \code{"L1"}, which uses the inverse of the absolute value of the residuals. Using \code{"symmetric"} will use Tukey's biweight with cut-off equal to six times the MAD of the residuals, equivalent to \code{\link[stats]{lowess}}.} \item{sdCriteria}{Convergence criteria, which the difference between the standard deviation of the residuals between two consecutive iteration steps. Default value is 2e-4.} \item{reps}{Small positive number added to residuals to avoid division by zero when calculating new weights for next iteration.} \item{tol}{Passed to \code{\link[stats]{smooth.spline}} (R >= 2.14.0).} \item{plotCurves}{If \code{\link[base:logical]{TRUE}}, the fitted splines are added to the current plot, otherwise not.} } \value{ Returns an object of class \code{smooth.spline}. } \examples{ data(cars) attach(cars) plot(speed, dist, main = "data(cars) & robust smoothing splines") # Fit a smoothing spline using L_2 norm cars.spl <- smooth.spline(speed, dist) lines(cars.spl, col = "blue") # Fit a smoothing spline using L_1 norm cars.rspl <- robustSmoothSpline(speed, dist) lines(cars.rspl, col = "red") # Fit a smoothing spline using L_2 norm with 10 degrees of freedom lines(smooth.spline(speed, dist, df=10), lty=2, col = "blue") # Fit a smoothing spline using L_1 norm with 10 degrees of freedom lines(robustSmoothSpline(speed, dist, df=10), lty=2, col = "red") legend(5,120, c( paste("smooth.spline [C.V.] => df =",round(cars.spl$df,1)), paste("robustSmoothSpline [C.V.] => df =",round(cars.rspl$df,1)), "standard with s( * , df = 10)", "robust with s( * , df = 10)" ), col = c("blue","red","blue","red"), lty = c(1,1,2,2), bg='bisque') } \seealso{ This implementation of this function was adopted from \code{\link[stats]{smooth.spline}} of the \pkg{stats} package. Because of this, this function is also licensed under GPL v2. } \author{Henrik Bengtsson} \keyword{smooth} \keyword{robust} aroma.light/man/Non-documented_objects.Rd0000644000175000017500000000221714136047216020251 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % 999.NonDocumentedObjects.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{Non-documented objects} \alias{Non-documented objects} \title{Non-documented objects} % Plot functions \alias{lines.XYCurveFit} % Matrix operations \alias{rowAverages} \alias{rowAverages.matrix} % Simple linear-algebra \alias{projectUontoV} \alias{scalarProduct} \alias{tr} % Miscellaneous statistical functions \alias{likelihood} \alias{predict.lowess} \description{ This page contains aliases for all "non-documented" objects that \code{R CMD check} detects in this package. Almost all of them are \emph{generic} functions that have specific document for the corresponding method coupled to a specific class. Other functions are re-defined by \code{setMethodS3()} to \emph{default} methods. Neither of these two classes are non-documented in reality. The rest are deprecated methods. } \keyword{documentation} \keyword{internal} aroma.light/man/findPeaksAndValleys.Rd0000644000175000017500000000514614136047216017554 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % findPeaksAndValleys.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{findPeaksAndValleys} \alias{findPeaksAndValleys} \alias{findPeaksAndValleys.density} \alias{findPeaksAndValleys.numeric} \title{Finds extreme points in the empirical density estimated from data} \description{ Finds extreme points in the empirical density estimated from data. } \usage{ \method{findPeaksAndValleys}{density}(x, tol=0, ...) \method{findPeaksAndValleys}{numeric}(x, ..., tol=0, na.rm=TRUE) } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} containing data points or a \code{\link[stats]{density}} object.} \item{...}{Arguments passed to \code{\link[stats]{density}}. Ignored if \code{x} is a \code{\link[stats]{density}} object.} \item{tol}{A non-negative \code{\link[base]{numeric}} threshold specifying the minimum density at the extreme point in order to accept it.} \item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are dropped, otherwise not.} } \value{ Returns a \code{\link[base]{data.frame}} (of class 'PeaksAndValleys') containing of "peaks" and "valleys" filtered by \code{tol}. } \examples{ layout(matrix(1:3, ncol=1)) par(mar=c(2,4,4,1)+0.1) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A unimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x1 <- rnorm(n=10000, mean=0, sd=1) x <- x1 fit <- findPeaksAndValleys(x) print(fit) plot(density(x), lwd=2, main="x1") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x2 <- rnorm(n=10000, mean=4, sd=1) x3 <- rnorm(n=10000, mean=8, sd=1) x <- c(x1,x2,x3) fit <- findPeaksAndValleys(x) print(fit) plot(density(x), lwd=2, main="c(x1,x2,x3)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with clear separation # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x1b <- rnorm(n=10000, mean=0, sd=0.1) x2b <- rnorm(n=10000, mean=4, sd=0.1) x3b <- rnorm(n=10000, mean=8, sd=0.1) x <- c(x1b,x2b,x3b) # Illustrating explicit usage of density() d <- density(x) fit <- findPeaksAndValleys(d, tol=0) print(fit) plot(d, lwd=2, main="c(x1b,x2b,x3b)") abline(v=fit$x) } \author{Henrik Bengtsson} \seealso{ This function is used by \code{\link{callNaiveGenotypes}}(). } \keyword{methods} \keyword{internal} aroma.light/man/plotDensity.Rd0000644000175000017500000000373314136047216016203 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % plotDensity.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{plotDensity} \alias{plotDensity} \alias{plotDensity.list} \alias{plotDensity.data.frame} \alias{plotDensity.matrix} \alias{plotDensity.numeric} \alias{plotDensity.density} \title{Plots density distributions for a set of vectors} \description{ Plots density distributions for a set of vectors. } \usage{ \method{plotDensity}{data.frame}(X, ..., xlab=NULL) \method{plotDensity}{matrix}(X, ..., xlab=NULL) \method{plotDensity}{numeric}(X, ..., xlab=NULL) \method{plotDensity}{list}(X, W=NULL, xlim=NULL, ylim=NULL, xlab=NULL, ylab="density (integrates to one)", col=1:length(X), lty=NULL, lwd=NULL, ..., add=FALSE) } \arguments{ \item{X}{A single of \code{\link[base]{list}} of \code{\link[base]{numeric}} \code{\link[base]{vector}}s or \code{\link[stats]{density}} objects, a \code{\link[base]{numeric}} \code{\link[base]{matrix}}, or a \code{\link[base]{numeric}} \code{\link[base]{data.frame}}.} \item{W}{(optional) weights of similar data types and dimensions as \code{X}.} \item{xlim,ylim}{\code{\link[base]{character}} \code{\link[base]{vector}} of length 2. The x and y limits.} \item{xlab,ylab}{\code{\link[base]{character}} string for labels on x and y axis.} \item{col}{The color(s) of the curves.} \item{lty}{The types of curves.} \item{lwd}{The width of curves.} \item{...}{Additional arguments passed to \code{\link[stats]{density}}, \code{\link[graphics]{plot}}(), and \code{\link[graphics]{lines}}.} \item{add}{If \code{\link[base:logical]{TRUE}}, the curves are plotted in the current plot, otherwise a new is created.} } \seealso{ Internally, \code{\link[stats]{density}} is used to estimate the empirical density. } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/normalizeFragmentLength.Rd0000644000175000017500000002413214136047216020507 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeFragmentLength.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeFragmentLength} \alias{normalizeFragmentLength.default} \alias{normalizeFragmentLength} \title{Normalizes signals for PCR fragment-length effects} \description{ Normalizes signals for PCR fragment-length effects. Some or all signals are used to estimated the normalization function. All signals are normalized. } \usage{ \method{normalizeFragmentLength}{default}(y, fragmentLengths, targetFcns=NULL, subsetToFit=NULL, onMissing=c("ignore", "median"), .isLogged=TRUE, ..., .returnFit=FALSE) } \arguments{ \item{y}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length K of signals to be normalized across E enzymes.} \item{fragmentLengths}{An \code{\link[base]{integer}} KxE \code{\link[base]{matrix}} of fragment lengths.} \item{targetFcns}{An optional \code{\link[base]{list}} of E \code{\link[base]{function}}s; one per enzyme. If \code{\link[base]{NULL}}, the data is normalized to have constant fragment-length effects (all equal to zero on the log-scale).} \item{subsetToFit}{The subset of data points used to fit the normalization function. If \code{\link[base]{NULL}}, all data points are considered.} \item{onMissing}{Specifies how data points for which there is no fragment length is normalized. If \code{"ignore"}, the values are not modified. If \code{"median"}, the values are updated to have the same robust average as the other data points. } \item{.isLogged}{A \code{\link[base]{logical}}.} \item{...}{Additional arguments passed to \code{\link[stats]{lowess}}.} \item{.returnFit}{A \code{\link[base]{logical}}.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of the normalized signals. } \section{Multi-enzyme normalization}{ It is assumed that the fragment-length effects from multiple enzymes added (with equal weights) on the intensity scale. The fragment-length effects are fitted for each enzyme separately based on units that are exclusively for that enzyme. \emph{If there are no or very such units for an enzyme, the assumptions of the model are not met and the fit will fail with an error.} Then, from the above single-enzyme fits the average effect across enzymes is the calculated for each unit that is on multiple enzymes. } \section{Target functions}{ It is possible to specify custom target function effects for each enzyme via argument \code{targetFcns}. This argument has to be a \code{\link[base]{list}} containing one \code{\link[base]{function}} per enzyme and ordered in the same order as the enzyme are in the columns of argument \code{fragmentLengths}. For instance, if one wish to normalize the signals such that their mean signal as a function of fragment length effect is constantly equal to 2200 (or the intensity scale), the use \code{targetFcns=function(fl, ...) log2(2200)} which completely ignores fragment-length argument 'fl' and always returns a constant. If two enzymes are used, then use \code{targetFcns=rep(list(function(fl, ...) log2(2200)), 2)}. Note, if \code{targetFcns} is \code{\link[base]{NULL}}, this corresponds to \code{targetFcns=rep(list(function(fl, ...) 0), ncol(fragmentLengths))}. Alternatively, if one wants to only apply minimal corrections to the signals, then one can normalize toward target functions that correspond to the fragment-length effect of the average array. } \examples{ # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Example 1: Single-enzyme fragment-length normalization of 6 arrays # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Number samples I <- 9 # Number of loci J <- 1000 # Fragment lengths fl <- seq(from=100, to=1000, length.out=J) # Simulate data points with unknown fragment lengths hasUnknownFL <- seq(from=1, to=J, by=50) fl[hasUnknownFL] <- NA # Simulate data y <- matrix(0, nrow=J, ncol=I) maxY <- 12 for (kk in 1:I) { k <- runif(n=1, min=3, max=5) mu <- function(fl) { mu <- rep(maxY, length(fl)) ok <- !is.na(fl) mu[ok] <- mu[ok] - fl[ok]^{1/k} mu } eps <- rnorm(J, mean=0, sd=1) y[,kk] <- mu(fl) + eps } # Normalize data (to a zero baseline) yN <- apply(y, MARGIN=2, FUN=function(y) { normalizeFragmentLength(y, fragmentLengths=fl, onMissing="median") }) # The correction factors rho <- y-yN print(summary(rho)) # The correction for units with unknown fragment lengths # equals the median correction factor of all other units print(summary(rho[hasUnknownFL,])) # Plot raw data layout(matrix(1:9, ncol=3)) xlim <- c(0,max(fl, na.rm=TRUE)) ylim <- c(0,max(y, na.rm=TRUE)) xlab <- "Fragment length" ylab <- expression(log2(theta)) for (kk in 1:I) { plot(fl, y[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) ok <- (is.finite(fl) & is.finite(y[,kk])) lines(lowess(fl[ok], y[ok,kk]), col="red", lwd=2) } # Plot normalized data layout(matrix(1:9, ncol=3)) ylim <- c(-1,1)*max(y, na.rm=TRUE)/2 for (kk in 1:I) { plot(fl, yN[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) ok <- (is.finite(fl) & is.finite(y[,kk])) lines(lowess(fl[ok], yN[ok,kk]), col="blue", lwd=2) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Example 2: Two-enzyme fragment-length normalization of 6 arrays # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(0xbeef) # Number samples I <- 5 # Number of loci J <- 3000 # Fragment lengths (two enzymes) fl <- matrix(0, nrow=J, ncol=2) fl[,1] <- seq(from=100, to=1000, length.out=J) fl[,2] <- seq(from=1000, to=100, length.out=J) # Let 1/2 of the units be on both enzymes fl[seq(from=1, to=J, by=4),1] <- NA fl[seq(from=2, to=J, by=4),2] <- NA # Let some have unknown fragment lengths hasUnknownFL <- seq(from=1, to=J, by=15) fl[hasUnknownFL,] <- NA # Sty/Nsp mixing proportions: rho <- rep(1, I) rho[1] <- 1/3; # Less Sty in 1st sample rho[3] <- 3/2; # More Sty in 3rd sample # Simulate data z <- array(0, dim=c(J,2,I)) maxLog2Theta <- 12 for (ii in 1:I) { # Common effect for both enzymes mu <- function(fl) { k <- runif(n=1, min=3, max=5) mu <- rep(maxLog2Theta, length(fl)) ok <- is.finite(fl) mu[ok] <- mu[ok] - fl[ok]^{1/k} mu } # Calculate the effect for each data point for (ee in 1:2) { z[,ee,ii] <- mu(fl[,ee]) } # Update the Sty/Nsp mixing proportions ee <- 2 z[,ee,ii] <- rho[ii]*z[,ee,ii] # Add random errors for (ee in 1:2) { eps <- rnorm(J, mean=0, sd=1/sqrt(2)) z[,ee,ii] <- z[,ee,ii] + eps } } hasFl <- is.finite(fl) unitSets <- list( nsp = which( hasFl[,1] & !hasFl[,2]), sty = which(!hasFl[,1] & hasFl[,2]), both = which( hasFl[,1] & hasFl[,2]), none = which(!hasFl[,1] & !hasFl[,2]) ) # The observed data is a mix of two enzymes theta <- matrix(NA_real_, nrow=J, ncol=I) # Single-enzyme units for (ee in 1:2) { uu <- unitSets[[ee]] theta[uu,] <- 2^z[uu,ee,] } # Both-enzyme units (sum on intensity scale) uu <- unitSets$both theta[uu,] <- (2^z[uu,1,]+2^z[uu,2,])/2 # Missing units (sample from the others) uu <- unitSets$none theta[uu,] <- apply(theta, MARGIN=2, sample, size=length(uu)) # Calculate target array thetaT <- rowMeans(theta, na.rm=TRUE) targetFcns <- list() for (ee in 1:2) { uu <- unitSets[[ee]] fit <- lowess(fl[uu,ee], log2(thetaT[uu])) class(fit) <- "lowess" targetFcns[[ee]] <- function(fl, ...) { predict(fit, newdata=fl) } } # Fit model only to a subset of the data subsetToFit <- setdiff(1:J, seq(from=1, to=J, by=10)) # Normalize data (to a target baseline) thetaN <- matrix(NA_real_, nrow=J, ncol=I) fits <- vector("list", I) for (ii in 1:I) { lthetaNi <- normalizeFragmentLength(log2(theta[,ii]), targetFcns=targetFcns, fragmentLengths=fl, onMissing="median", subsetToFit=subsetToFit, .returnFit=TRUE) fits[[ii]] <- attr(lthetaNi, "modelFit") thetaN[,ii] <- 2^lthetaNi } # Plot raw data xlim <- c(0, max(fl, na.rm=TRUE)) ylim <- c(0, max(log2(theta), na.rm=TRUE)) Mlim <- c(-1,1)*4 xlab <- "Fragment length" ylab <- expression(log2(theta)) Mlab <- expression(M==log[2](theta/theta[R])) layout(matrix(1:(3*I), ncol=I, byrow=TRUE)) for (ii in 1:I) { plot(NA, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, main="raw") # Single-enzyme units for (ee in 1:2) { # The raw data uu <- unitSets[[ee]] points(fl[uu,ee], log2(theta[uu,ii]), col=ee+1) } # Both-enzyme units (use fragment-length for enzyme #1) uu <- unitSets$both points(fl[uu,1], log2(theta[uu,ii]), col=3+1) for (ee in 1:2) { # The true effects uu <- unitSets[[ee]] lines(lowess(fl[uu,ee], log2(theta[uu,ii])), col="black", lwd=4, lty=3) # The estimated effects fit <- fits[[ii]][[ee]]$fit lines(fit, col="orange", lwd=3) muT <- targetFcns[[ee]](fl[uu,ee]) lines(fl[uu,ee], muT, col="cyan", lwd=1) } } # Calculate log-ratios thetaR <- rowMeans(thetaN, na.rm=TRUE) M <- log2(thetaN/thetaR) # Plot normalized data for (ii in 1:I) { plot(NA, xlim=xlim, ylim=Mlim, xlab=xlab, ylab=Mlab, main="normalized") # Single-enzyme units for (ee in 1:2) { # The normalized data uu <- unitSets[[ee]] points(fl[uu,ee], M[uu,ii], col=ee+1) } # Both-enzyme units (use fragment-length for enzyme #1) uu <- unitSets$both points(fl[uu,1], M[uu,ii], col=3+1) } ylim <- c(0,1.5) for (ii in 1:I) { data <- list() for (ee in 1:2) { # The normalized data uu <- unitSets[[ee]] data[[ee]] <- M[uu,ii] } uu <- unitSets$both if (length(uu) > 0) data[[3]] <- M[uu,ii] uu <- unitSets$none if (length(uu) > 0) data[[4]] <- M[uu,ii] cols <- seq_along(data)+1 plotDensity(data, col=cols, xlim=Mlim, xlab=Mlab, main="normalized") abline(v=0, lty=2) } } \author{Henrik Bengtsson} \references{ [1] H. Bengtsson, R. Irizarry, B. Carvalho, and T. Speed, \emph{Estimation and assessment of raw copy numbers at the single locus level}, Bioinformatics, 2008. \cr } \keyword{nonparametric} \keyword{robust} aroma.light/man/normalizeDifferencesToAverage.Rd0000644000175000017500000000423714136047216021621 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeDifferencesToAverage.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeDifferencesToAverage} \alias{normalizeDifferencesToAverage} \alias{normalizeDifferencesToAverage.list} \title{Rescales channel vectors to get the same average} \description{ Rescales channel vectors to get the same average. } \usage{ \method{normalizeDifferencesToAverage}{list}(x, baseline=1, FUN=median, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} \code{\link[base]{list}} of length K.} \item{baseline}{An \code{\link[base]{integer}} in [1,K] specifying which channel should be the baseline. The baseline channel will be almost unchanged. If \code{\link[base]{NULL}}, the channels will be shifted towards median of them all.} \item{FUN}{A \code{\link[base]{function}} for calculating the average of one channel.} \item{...}{Additional arguments passed to the \code{avg} \code{\link[base]{function}}.} } \value{ Returns a normalized \code{\link[base]{list}} of length K. } \examples{ # Simulate three shifted tracks of different lengths with same profiles ns <- c(A=2, B=1, C=0.25)*1000 xx <- lapply(ns, FUN=function(n) { seq(from=1, to=max(ns), length.out=n) }) zz <- mapply(seq_along(ns), ns, FUN=function(z,n) rep(z,n)) yy <- list( A = rnorm(ns["A"], mean=0, sd=0.5), B = rnorm(ns["B"], mean=5, sd=0.4), C = rnorm(ns["C"], mean=-5, sd=1.1) ) yy <- lapply(yy, FUN=function(y) { n <- length(y) y[1:(n/2)] <- y[1:(n/2)] + 2 y[1:(n/4)] <- y[1:(n/4)] - 4 y }) # Shift all tracks toward the first track yyN <- normalizeDifferencesToAverage(yy, baseline=1) # The baseline channel is not changed stopifnot(identical(yy[[1]], yyN[[1]])) # Get the estimated parameters fit <- attr(yyN, "fit") # Plot the tracks layout(matrix(1:2, ncol=1)) x <- unlist(xx) col <- unlist(zz) y <- unlist(yy) yN <- unlist(yyN) plot(x, y, col=col, ylim=c(-10,10)) plot(x, yN, col=col, ylim=c(-10,10)) } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/aroma.light-package.Rd0000644000175000017500000001300314136047216017452 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % 999.package.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{aroma.light-package} \alias{aroma.light-package} \alias{aroma.light} \docType{package} \title{Package aroma.light} \encoding{latin1} \description{ Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. } \section{Installation}{ To install this package, see \url{https://bioconductor.org/packages/release/bioc/html/aroma.light.html}. } \section{To get started}{ For scanner calibration: \enumerate{ \item see \code{\link{calibrateMultiscan}}() - scan the same array two or more times to calibrate for scanner effects and extended dynamical range. } To normalize multiple single-channel arrays all with the same number of probes/spots: \enumerate{ \item \code{\link{normalizeAffine}}() - normalizes, on the intensity scale, for differences in offset and scale between channels. \item \code{\link{normalizeQuantileRank}}(), \code{\link{normalizeQuantileSpline}}() - normalizes, on the intensity scale, for differences in empirical distribution between channels. } To normalize multiple single-channel arrays with varying number probes/spots: \enumerate{ \item \code{\link{normalizeQuantileRank}}(), \code{\link{normalizeQuantileSpline}}() - normalizes, on the intensity scale, for differences in empirical distribution between channels. } To normalize two-channel arrays: \enumerate{ \item \code{\link{normalizeAffine}}() - normalizes, on the intensity scale, for differences in offset and scale between channels. This will also correct for intensity-dependent affects on the log scale. \item \code{\link{normalizeCurveFit}}() - Classical intensity-dependent normalization, on the log scale, e.g. lowess normalization. } To normalize three or more channels: \enumerate{ \item \code{\link{normalizeAffine}}() - normalizes, on the intensity scale, for differences in offset and scale between channels. This will minimize the curvature on the log scale between any two channels. } } \section{Further readings}{ Several of the normalization methods proposed in [1]-[7] are available in this package. } \section{How to cite this package}{ Whenever using this package, please cite one or more of [1]-[7]. } \section{Wishlist}{ Here is a list of features that would be useful, but which I have too little time to add myself. Contributions are appreciated. \itemize{ \item At the moment, nothing. } If you consider to contribute, make sure it is not already implemented by downloading the latest "devel" version! } \author{Henrik Bengtsson, Pierre Neuvial, Aaron Lun} \section{License}{ The releases of this package is licensed under GPL version 2 or newer. NB: Except for the \code{robustSmoothSpline()} method, it is alright to distribute the rest of the package under LGPL version 2.1 or newer. The development code of the packages is under a private licence (where applicable) and patches sent to the author fall under the latter license, but will be, if incorporated, released under the "release" license above. } \references{ Some of the reference below can be found at \url{https://www.aroma-project.org/publications/}.\cr [1] H. Bengtsson, \emph{Identification and normalization of plate effects in cDNA microarray data}, Preprints in Mathematical Sciences, 2002:28, Mathematical Statistics, Centre for Mathematical Sciences, Lund University, 2002.\cr [2] H. Bengtsson, \emph{The R.oo package - Object-Oriented Programming with References Using Standard R Code}, In Kurt Hornik, Friedrich Leisch and Achim Zeileis, editors, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20-22, Vienna, Austria. \url{http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/} \cr [3] H. Bengtsson, \emph{aroma - An R Object-oriented Microarray Analysis environment}, Preprints in Mathematical Sciences (manuscript in preparation), Mathematical Statistics, Centre for Mathematical Sciences, Lund University, 2004.\cr [4] H. Bengtsson, J. Vallon-Christersson and G. \enc{Jnsson}{Jonsson}, \emph{Calibration and assessment of channel-specific biases in microarray data with extended dynamical range}, BMC Bioinformatics, 5:177, 2004. \cr [5] Henrik Bengtsson and Ola \enc{Hssjer}{Hossjer}, \emph{Methodological Study of Affine Transformations of Gene Expression Data}, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100. \cr [6] H. Bengtsson, R. Irizarry, B. Carvalho, and T. Speed, \emph{Estimation and assessment of raw copy numbers at the single locus level}, Bioinformatics, 2008. \cr [7] H. Bengtsson, A. Ray, P. Spellman and T.P. Speed, \emph{A single-sample method for normalizing and combining full-resolutioncopy numbers from multiple platforms, labs and analysis methods}, Bioinformatics, 2009. \cr [8] H. Bengtsson, P. Neuvial and T.P. Speed, \emph{TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays}, BMC Bioinformatics, 2010, 11:245. [PMID 20462408] \cr } \keyword{package} aroma.light/man/normalizeCurveFit.Rd0000644000175000017500000002166514136047216017341 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeCurveFit.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeCurveFit} \alias{normalizeCurveFit} \alias{normalizeLoess} \alias{normalizeLowess} \alias{normalizeSpline} \alias{normalizeRobustSpline} \alias{normalizeCurveFit.matrix} \alias{normalizeLoess.matrix} \alias{normalizeLowess.matrix} \alias{normalizeSpline.matrix} \alias{normalizeRobustSpline.matrix} \encoding{latin1} \title{Weighted curve-fit normalization between a pair of channels} \description{ Weighted curve-fit normalization between a pair of channels. This method will estimate a smooth function of the dependency between the log-ratios and the log-intensity of the two channels and then correct the log-ratios (only) in order to remove the dependency. This is method is also known as \emph{intensity-dependent} or \emph{lowess normalization}. The curve-fit methods are by nature limited to paired-channel data. There exist at least one method trying to overcome this limitation, namely the cyclic-lowess [1], which applies the paired curve-fit method iteratively over all pairs of channels/arrays. Cyclic-lowess is not implemented here. We recommend that affine normalization [2] is used instead of curve-fit normalization. } \usage{ \method{normalizeCurveFit}{matrix}(X, weights=NULL, typeOfWeights=c("datapoint"), method=c("loess", "lowess", "spline", "robustSpline"), bandwidth=NULL, satSignal=2^16 - 1, ...) \method{normalizeLoess}{matrix}(X, ...) \method{normalizeLowess}{matrix}(X, ...) \method{normalizeSpline}{matrix}(X, ...) \method{normalizeRobustSpline}{matrix}(X, ...) } \arguments{ \item{X}{An Nx2 \code{\link[base]{matrix}} where the columns represent the two channels to be normalized.} \item{weights}{If \code{\link[base]{NULL}}, non-weighted normalization is done. If data-point weights are used, this should be a \code{\link[base]{vector}} of length N of data point weights used when estimating the normalization function. } \item{typeOfWeights}{A \code{\link[base]{character}} string specifying the type of weights given in argument \code{weights}. } \item{method}{\code{\link[base]{character}} string specifying which method to use when fitting the intensity-dependent function. Supported methods: \code{"loess"} (better than lowess), \code{"lowess"} (classic; supports only zero-one weights), \code{"spline"} (more robust than lowess at lower and upper intensities; supports only zero-one weights), \code{"robustSpline"} (better than spline). } \item{bandwidth}{A \code{\link[base]{double}} value specifying the bandwidth of the estimator used. } \item{satSignal}{Signals equal to or above this threshold will not be used in the fitting. } \item{...}{Not used.} } \value{ A Nx2 \code{\link[base]{matrix}} of the normalized two channels. The fitted model is returned as attribute \code{modelFit}. } \details{ A smooth function \eqn{c(A)} is fitted through data in \eqn{(A,M)}, where \eqn{M=log_2(y_2/y_1)} and \eqn{A=1/2*log_2(y_2*y_1)}. Data is normalized by \eqn{M <- M - c(A)}. Loess is by far the slowest method of the four, then lowess, and then robust spline, which iteratively calls the spline method. } \section{Negative, non-positive, and saturated values}{ Non-positive values are set to not-a-number (\code{\link[base:is.finite]{NaN}}). Data points that are saturated in one or more channels are not used to estimate the normalization function, but they are normalized. } \section{Missing values}{ The estimation of the normalization function will only be made based on complete non-saturated observations, i.e. observations that contains no \code{\link[base]{NA}} values nor saturated values as defined by \code{satSignal}. } \section{Weighted normalization}{ Each data point, that is, each row in \code{X}, which is a vector of length 2, can be assigned a weight in [0,1] specifying how much it should \emph{affect the fitting of the normalization function}. Weights are given by argument \code{weights}, which should be a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N. Regardless of weights, all data points are \emph{normalized} based on the fitted normalization function. Note that the lowess and the spline method only support zero-one \{0,1\} weights. For such methods, all weights that are less than a half are set to zero. } \section{Details on loess}{ For \code{\link[stats]{loess}}, the arguments \code{family="symmetric"}, \code{degree=1}, \code{span=3/4}, \code{control=loess.control(trace.hat="approximate"}, \code{iterations=5}, \code{surface="direct")} are used. } \author{Henrik Bengtsson} \references{ [1] M. \enc{strand}{Astrand}, Contrast Normalization of Oligonucleotide Arrays, Journal Computational Biology, 2003, 10, 95-102. \cr [2] Henrik Bengtsson and Ola \enc{Hssjer}{Hossjer}, \emph{Methodological Study of Affine Transformations of Gene Expression Data}, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100. \cr } \examples{ pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") rg <- read.table(pathname, header=TRUE, sep="\t") nbrOfScans <- max(rg$slide) rg <- as.list(rg) for (field in c("R", "G")) rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) rg$slide <- rg$spot <- NULL rg <- as.matrix(as.data.frame(rg)) colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) rgC <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) channelColor <- switch(channel, R="red", G="green") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsAPairs(rg[,sidx]) title(main=paste("Observed", channel)) box(col=channelColor) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The calibrated data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) plotMvsAPairs(rgC[,sidx]) title(main=paste("Calibrated", channel)) box(col=channelColor) } # for (channel ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The average calibrated data # # Note how the red signals are weaker than the green. The reason # for this can be that the scale factor in the green channel is # greater than in the red channel, but it can also be that there # is a remaining relative difference in bias between the green # and the red channel, a bias that precedes the scanning. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgCA <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCA[,sidx] <- calibrateMultiscan(rg[,sidx]) } rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2) colnames(rgCAavg) <- c("R", "G") for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE) } # Add some "fake" outliers outliers <- 1:600 rgCAavg[outliers,"G"] <- 50000 plotMvsA(rgCAavg) title(main="Average calibrated (AC)") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Weight-down outliers when normalizing weights <- rep(1, nrow(rgCAavg)) weights[outliers] <- 0.001 # Affine normalization of channels rgCANa <- normalizeAffine(rgCAavg, weights=weights) # It is always ok to rescale the affine normalized data if its # done on (R,G); not on (A,M)! However, this is only needed for # esthetic purposes. rgCANa <- rgCANa *2^1.4 plotMvsA(rgCANa) title(main="Normalized AC") # Curve-fit (lowess) normalization rgCANlw <- normalizeLowess(rgCAavg, weights=weights) plotMvsA(rgCANlw, col="orange", add=TRUE) # Curve-fit (loess) normalization rgCANl <- normalizeLoess(rgCAavg, weights=weights) plotMvsA(rgCANl, col="red", add=TRUE) # Curve-fit (robust spline) normalization rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights) plotMvsA(rgCANrs, col="blue", add=TRUE) legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19, col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine])) title(main="Normalized AC") plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE) plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE) abline(a=0, b=1, lty=2) legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19, col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") } \seealso{ \code{\link{normalizeAffine}}(). } \keyword{methods} aroma.light/man/fitPrincipalCurve.Rd0000644000175000017500000000740714136047216017320 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % fitPrincipalCurve.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{fitPrincipalCurve} \alias{fitPrincipalCurve} \alias{fitPrincipalCurve.matrix} \title{Fit a principal curve in K dimensions} \description{ Fit a principal curve in K dimensions. } \usage{ \method{fitPrincipalCurve}{matrix}(X, ..., verbose=FALSE) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} (K>=2) where the columns represent the dimension.} \item{...}{Other arguments passed to \code{\link[princurve]{principal_curve}}.} \item{verbose}{A \code{\link[base]{logical}} or a \code{\link[R.utils]{Verbose}} object.} } \value{ Returns a principal_curve object (which is a \code{\link[base]{list}}). See \code{\link[princurve]{principal_curve}} for more details. } \section{Missing values}{ The estimation of the normalization function will only be made based on complete observations, i.e. observations that contains no \code{\link[base]{NA}} values in any of the channels. } \author{Henrik Bengtsson} \references{ [1] Hastie, T. and Stuetzle, W, \emph{Principal Curves}, JASA, 1989.\cr [2] H. Bengtsson, A. Ray, P. Spellman and T.P. Speed, \emph{A single-sample method for normalizing and combining full-resolutioncopy numbers from multiple platforms, labs and analysis methods}, Bioinformatics, 2009. \cr } \examples{ # Simulate data from the model y <- a + bx + x^c + eps(bx) J <- 1000 x <- rexp(J) a <- c(2,15,3) b <- c(2,3,4) c <- c(1,2,1/2) bx <- outer(b,x) xc <- t(sapply(c, FUN=function(c) x^c)) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(b), mean=0, sd=0.1*x)) y <- a + bx + xc + eps y <- t(y) # Fit principal curve through (y_1, y_2, y_3) fit <- fitPrincipalCurve(y, verbose=TRUE) # Flip direction of 'lambda'? rho <- cor(fit$lambda, y[,1], use="complete.obs") flip <- (rho < 0) if (flip) { fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda } # Backtransform (y_1, y_2, y_3) to be proportional to each other yN <- backtransformPrincipalCurve(y, fit=fit) # Same backtransformation dimension by dimension yN2 <- y for (cc in 1:ncol(y)) { yN2[,cc] <- backtransformPrincipalCurve(y, fit=fit, dimensions=cc) } stopifnot(identical(yN2, yN)) xlim <- c(0, 1.04*max(x)) ylim <- range(c(y,yN), na.rm=TRUE) # Pairwise signals vs x before and after transform layout(matrix(1:4, nrow=2, byrow=TRUE)) par(mar=c(4,4,3,2)+0.1) for (cc in 1:3) { ylab <- substitute(y[c], env=list(c=cc)) plot(NA, xlim=xlim, ylim=ylim, xlab="x", ylab=ylab) abline(h=a[cc], lty=3) mtext(side=4, at=a[cc], sprintf("a=\%g", a[cc]), cex=0.8, las=2, line=0, adj=1.1, padj=-0.2) points(x, y[,cc]) points(x, yN[,cc], col="tomato") legend("topleft", col=c("black", "tomato"), pch=19, c("orignal", "transformed"), bty="n") } title(main="Pairwise signals vs x before and after transform", outer=TRUE, line=-2) # Pairwise signals before and after transform layout(matrix(1:4, nrow=2, byrow=TRUE)) par(mar=c(4,4,3,2)+0.1) for (rr in 3:2) { ylab <- substitute(y[c], env=list(c=rr)) for (cc in 1:2) { if (cc == rr) { plot.new() next } xlab <- substitute(y[c], env=list(c=cc)) plot(NA, xlim=ylim, ylim=ylim, xlab=xlab, ylab=ylab) abline(a=0, b=1, lty=2) points(y[,c(cc,rr)]) points(yN[,c(cc,rr)], col="tomato") legend("topleft", col=c("black", "tomato"), pch=19, c("orignal", "transformed"), bty="n") } } title(main="Pairwise signals before and after transform", outer=TRUE, line=-2) } \seealso{ \code{\link{backtransformPrincipalCurve}}(). \code{\link[princurve]{principal_curve}}. } \keyword{methods} aroma.light/man/distanceBetweenLines.Rd0000644000175000017500000001074214136047216017762 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % distanceBetweenLines.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{distanceBetweenLines} \alias{distanceBetweenLines.default} \alias{distanceBetweenLines} \title{Finds the shortest distance between two lines} \description{ Finds the shortest distance between two lines. Consider the two lines \eqn{x(s) = a_x + b_x*s} and \eqn{y(t) = a_y + b_y*t} in an K-space where the offset and direction \code{\link[base]{vector}}s are \eqn{a_x} and \eqn{b_x} (in \eqn{R^K}) that define the line \eqn{x(s)} (\eqn{s} is a scalar). Similar for the line \eqn{y(t)}. This function finds the point \eqn{(s,t)} for which \eqn{|x(s)-x(t)|} is minimal. } \usage{ \method{distanceBetweenLines}{default}(ax, bx, ay, by, ...) } \arguments{ \item{ax,bx}{Offset and direction \code{\link[base]{vector}} of length K for line \eqn{z_x}.} \item{ay,by}{Offset and direction \code{\link[base]{vector}} of length K for line \eqn{z_y}.} \item{...}{Not used.} } \value{ Returns the a \code{\link[base]{list}} containing \item{ax,bx}{The given line \eqn{x(s)}.} \item{ay,by}{The given line \eqn{y(t)}.} \item{s,t}{The values of \eqn{s} and \eqn{t} such that \eqn{|x(s)-y(t)|} is minimal.} \item{xs,yt}{The values of \eqn{x(s)} and \eqn{y(t)} at the optimal point \eqn{(s,t)}.} \item{distance}{The distance between the lines, i.e. \eqn{|x(s)-y(t)|} at the optimal point \eqn{(s,t)}.} } \author{Henrik Bengtsson} \examples{ for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break layout(matrix(1:4, nrow=2, ncol=2, byrow=TRUE)) ############################################################ # Lines in two-dimensions ############################################################ x <- list(a=c(1,0), b=c(1,2)) y <- list(a=c(0,2), b=c(1,1)) fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) xlim <- ylim <- c(-1,8) plot(NA, xlab="", ylab="", xlim=ylim, ylim=ylim) # Highlight the offset coordinates for both lines points(t(x$a), pch="+", col="red") text(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) points(t(y$a), pch="+", col="blue") text(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) v <- c(-1,1)*10; xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v) yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v) lines(xv, col="red") lines(yv, col="blue") points(t(fit$xs), cex=2.0, col="red") text(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) points(t(fit$yt), cex=1.5, col="blue") text(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) print(fit) ############################################################ # Lines in three-dimensions ############################################################ x <- list(a=c(0,0,0), b=c(1,1,1)) # The 'diagonal' y <- list(a=c(2,1,2), b=c(2,1,3)) # A 'fitted' line fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) xlim <- ylim <- zlim <- c(-1,3) dummy <- t(c(1,1,1))*100; # Coordinates for the lines in 3d v <- seq(-10,10, by=1); xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v, z=x$a[3]+x$b[3]*v) yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v, z=y$a[3]+y$b[3]*v) for (theta in seq(30,140,length.out=3)) { plot3d(dummy, theta=theta, phi=30, xlab="", ylab="", zlab="", xlim=ylim, ylim=ylim, zlim=zlim) # Highlight the offset coordinates for both lines points3d(t(x$a), pch="+", col="red") text3d(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) points3d(t(y$a), pch="+", col="blue") text3d(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) # Draw the lines lines3d(xv, col="red") lines3d(yv, col="blue") # Draw the two points that are closest to each other points3d(t(fit$xs), cex=2.0, col="red") text3d(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) points3d(t(fit$yt), cex=1.5, col="blue") text3d(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) # Draw the distance between the two points lines3d(rbind(fit$xs,fit$yt), col="purple", lwd=2) } print(fit) } # for (zzz in 0) rm(zzz) } \references{ [1] M. Bard and D. Himel, \emph{The Minimum Distance Between Two Lines in n-Space}, September 2001, Advisor Dennis Merino.\cr [2] Dan Sunday, \emph{Distance between 3D Lines and Segments}, Jan 2016, \url{https://www.geomalgorithms.com/algorithms.html}.\cr } \keyword{algebra} aroma.light/man/fitNaiveGenotypes.Rd0000644000175000017500000000441714136047216017330 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % fitNaiveGenotypes.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{fitNaiveGenotypes} \alias{fitNaiveGenotypes} \alias{fitNaiveGenotypes.numeric} \title{Fit naive genotype model from a normal sample} \description{ Fit naive genotype model from a normal sample. } \usage{ \method{fitNaiveGenotypes}{numeric}(y, cn=rep(2L, times = length(y)), subsetToFit=NULL, flavor=c("density", "fixed"), adjust=1.5, ..., censorAt=c(-0.1, 1.1), verbose=FALSE) } \arguments{ \item{y}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J containing allele B fractions for a normal sample.} \item{cn}{An optional \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J specifying the true total copy number in \eqn{\{0,1,2,NA\}} at each locus. This can be used to specify which loci are diploid and which are not, e.g. autosomal and sex chromosome copy numbers.} \item{subsetToFit}{An optional \code{\link[base]{integer}} or \code{\link[base]{logical}} \code{\link[base]{vector}} specifying which loci should be used for estimating the model. If \code{\link[base]{NULL}}, all loci are used.} \item{flavor}{A \code{\link[base]{character}} string specifying the type of algorithm used.} \item{adjust}{A positive \code{\link[base]{double}} specifying the amount smoothing for the empirical density estimator.} \item{...}{Additional arguments passed to \code{\link{findPeaksAndValleys}}().} \item{censorAt}{A \code{\link[base]{double}} \code{\link[base]{vector}} of length two specifying the range for which values are considered finite. Values below (above) this range are treated as -\code{\link[base:is.finite]{Inf}} (+\code{\link[base:is.finite]{Inf}}).} \item{verbose}{A \code{\link[base]{logical}} or a \code{\link[R.utils]{Verbose}} object.} } \value{ Returns a \code{\link[base]{list}} of \code{\link[base]{list}}s. } \author{Henrik Bengtsson} \seealso{ To call genotypes see \code{\link{callNaiveGenotypes}}(). Internally \code{\link{findPeaksAndValleys}}() is used to identify the thresholds. } \keyword{methods} aroma.light/man/plotMvsA.Rd0000644000175000017500000000246114136047216015427 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % plotMvsA.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{plotMvsA} \alias{plotMvsA} \alias{plotMvsA.matrix} \title{Plot log-ratios vs log-intensities} \description{ Plot log-ratios vs log-intensities. } \usage{ \method{plotMvsA}{matrix}(X, Alab="A", Mlab="M", Alim=c(0, 16), Mlim=c(-1, 1) * diff(Alim) * aspectRatio, aspectRatio=1, pch=".", ..., add=FALSE) } \arguments{ \item{X}{Nx2 \code{\link[base]{matrix}} with two channels and N observations.} \item{Alab,Mlab}{Labels on the x and y axes.} \item{Alim,Mlim}{Plot range on the A and M axes.} \item{aspectRatio}{Aspect ratio between \code{Mlim} and \code{Alim}.} \item{pch}{Plot symbol used.} \item{...}{Additional arguments accepted by \code{\link[graphics]{points}}.} \item{add}{If \code{\link[base:logical]{TRUE}}, data points are plotted in the current plot, otherwise a new plot is created.} } \details{ Red channel is assumed to be in column one and green in column two. Log-ratio are calculated taking channel one over channel two. } \value{ Returns nothing. } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/normalizeQuantileSpline.Rd0000644000175000017500000001047714136047216020546 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeQuantileSpline.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeQuantileSpline} \alias{normalizeQuantileSpline} \alias{normalizeQuantileSpline.numeric} \alias{normalizeQuantileSpline.matrix} \alias{normalizeQuantileSpline.list} \title{Normalizes the empirical distribution of one or more samples to a target distribution} \usage{ \method{normalizeQuantileSpline}{numeric}(x, w=NULL, xTarget, sortTarget=TRUE, robust=TRUE, ...) \method{normalizeQuantileSpline}{matrix}(X, w=NULL, xTarget=NULL, sortTarget=TRUE, robust=TRUE, ...) \method{normalizeQuantileSpline}{list}(X, w=NULL, xTarget=NULL, sortTarget=TRUE, robust=TRUE, ...) } \description{ Normalizes the empirical distribution of one or more samples to a target distribution. After normalization, all samples have the same average empirical density distribution. } \arguments{ \item{x, X}{A single (\eqn{K=1}) \code{\link[base]{numeric}} \code{\link[base]{vector}} of length \eqn{N}, a \code{\link[base]{numeric}} \eqn{NxK} \code{\link[base]{matrix}}, or a \code{\link[base]{list}} of length \eqn{K} with \code{\link[base]{numeric}} \code{\link[base]{vector}}s, where \eqn{K} represents the number of samples and \eqn{N} the number of data points.} \item{w}{An optional \code{\link[base]{numeric}} \code{\link[base]{vector}} of length \eqn{N} of weights specific to each data point.} \item{xTarget}{The target empirical distribution as a \emph{sorted} \code{\link[base]{numeric}} \code{\link[base]{vector}} of length \eqn{M}. If \code{\link[base]{NULL}} and \code{X} is a \code{\link[base]{list}}, then the target distribution is calculated as the average empirical distribution of the samples.} \item{sortTarget}{If \code{\link[base:logical]{TRUE}}, argument \code{xTarget} will be sorted, otherwise it is assumed to be already sorted.} \item{robust}{If \code{\link[base:logical]{TRUE}}, the normalization function is estimated robustly.} \item{...}{Arguments passed to (\code{\link[stats]{smooth.spline}} or \code{\link[aroma.light]{robustSmoothSpline}}).} } \value{ Returns an object of the same type and dimensions as the input. } \section{Missing values}{ Both argument \code{X} and \code{xTarget} may contain non-finite values. These values do not affect the estimation of the normalization function. Missing values and other non-finite values in \code{X}, remain in the output as is. No new missing values are introduced. } \examples{ # Simulate three samples with on average 20\% missing values N <- 10000 X <- cbind(rnorm(N, mean=3, sd=1), rnorm(N, mean=4, sd=2), rgamma(N, shape=2, rate=1)) X[sample(3*N, size=0.20*3*N)] <- NA # Plot the data layout(matrix(c(1,0,2:5), ncol=2, byrow=TRUE)) xlim <- range(X, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") Xn <- normalizeQuantile(X) plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") plotXYCurve(X, Xn, xlim=xlim, main="The three normalized distributions") Xn2 <- normalizeQuantileSpline(X, xTarget=Xn[,1], spar=0.99) plotDensity(Xn2, lwd=2, xlim=xlim, main="The three normalized distributions") plotXYCurve(X, Xn2, xlim=xlim, main="The three normalized distributions") } \author{Henrik Bengtsson} \seealso{ The target distribution can be calculated as the average using \code{\link{averageQuantile}}(). Internally either \code{\link[aroma.light]{robustSmoothSpline}} (\code{robust=TRUE}) or \code{\link[stats]{smooth.spline}} (\code{robust=FALSE}) is used. An alternative normalization method that is also normalizing the empirical densities of samples is \code{\link{normalizeQuantileRank}}(). Contrary to this method, that method requires that all samples are based on the exact same set of data points and it is also more likely to over-correct in the tails of the distributions. } \references{ [1] H. Bengtsson, R. Irizarry, B. Carvalho, and T. Speed, \emph{Estimation and assessment of raw copy numbers at the single locus level}, Bioinformatics, 2008. \cr } \keyword{methods} \keyword{nonparametric} \keyword{multivariate} \keyword{robust} aroma.light/man/plotXYCurve.Rd0000644000175000017500000000446014136047216016127 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % plotXYCurve.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{plotXYCurve} \alias{plotXYCurve} \alias{plotXYCurve.numeric} \alias{plotXYCurve.matrix} \title{Plot the relationship between two variables as a smooth curve} \usage{ \method{plotXYCurve}{numeric}(x, y, col=1L, lwd=2, dlwd=1, dcol=NA, xlim=NULL, ylim=xlim, xlab=NULL, ylab=NULL, curveFit=smooth.spline, ..., add=FALSE) \method{plotXYCurve}{matrix}(X, Y, col=seq_len(nrow(X)), lwd=2, dlwd=1, dcol=NA, xlim=NULL, ylim=xlim, xlab=NULL, ylab=NULL, curveFit=smooth.spline, ..., add=FALSE) } \description{ Plot the relationship between two variables as a smooth curve. } \arguments{ \item{x, y, X, Y}{Two \code{\link[base]{numeric}} \code{\link[base]{vector}}s of length N for one curve (K=1), or two \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}:es for K curves.} \item{col}{The color of each curve. Either a scalar specifying the same value of all curves, or a \code{\link[base]{vector}} of K curve-specific values.} \item{lwd}{The line width of each curve. Either a scalar specifying the same value of all curves, or a \code{\link[base]{vector}} of K curve-specific values.} \item{dlwd}{The width of each density curve.} \item{dcol}{The fill color of the interior of each density curve.} \item{xlim, ylim}{The x and y plotting limits.} \item{xlab, ylab}{The x and y labels.} \item{curveFit}{The \code{\link[base]{function}} used to fit each curve. The two first arguments of the function must take \code{x} and \code{y}, and the function must return a \code{\link[base]{list}} with fitted elements \code{x} and \code{y}.} \item{...}{Additional arguments passed to \code{\link[graphics]{lines}} used to draw each curve.} \item{add}{If \code{\link[base:logical]{TRUE}}, the graph is added to the current plot, otherwise a new plot is created.} } \value{ Returns nothing. } \section{Missing values}{ Data points (x,y) with non-finite values are excluded. } \author{Henrik Bengtsson} \keyword{methods} \keyword{nonparametric} \keyword{multivariate} \keyword{robust} aroma.light/man/averageQuantile.Rd0000644000175000017500000000301114136047216016767 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % averageQuantile.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{averageQuantile} \alias{averageQuantile} \alias{averageQuantile.list} \alias{averageQuantile.matrix} \title{Gets the average empirical distribution} \usage{ \method{averageQuantile}{list}(X, ...) \method{averageQuantile}{matrix}(X, ...) } \description{ Gets the average empirical distribution for a set of samples. } \arguments{ \item{X}{A \code{\link[base]{list}} with K \code{\link[base]{numeric}} \code{\link[base]{vector}}s, or a \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}. If a \code{\link[base]{list}}, the \code{\link[base]{vector}}s may be of different lengths.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length equal to the longest \code{\link[base]{vector}} in argument \code{X}. } \section{Missing values}{ Missing values are excluded. } \seealso{ \code{\link{normalizeQuantileRank}}(). \code{\link{normalizeQuantileSpline}}(). \code{\link[stats]{quantile}}. } \author{ Parts adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 \& 2006. Original code by Ben Bolstad at Statistics Department, University of California. } \keyword{methods} \keyword{nonparametric} \keyword{multivariate} \keyword{robust} aroma.light/man/wpca.Rd0000644000175000017500000001620314136047216014613 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % wpca.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{wpca} \alias{wpca} \alias{wpca.matrix} \title{Light-weight Weighted Principal Component Analysis} \usage{ \method{wpca}{matrix}(x, w=NULL, center=TRUE, scale=FALSE, method=c("dgesdd", "dgesvd"), swapDirections=FALSE, ...) } \description{ Calculates the (weighted) principal components of a matrix, that is, finds a new coordinate system (not unique) for representing the given multivariate data such that i) all dimensions are orthogonal to each other, and ii) all dimensions have maximal variances. } \arguments{ \item{x}{An NxK \code{\link[base]{matrix}}.} \item{w}{An N \code{\link[base]{vector}} of weights for each row (observation) in the data matrix. If \code{\link[base]{NULL}}, all observations get the same weight, that is, standard PCA is used.} \item{center}{If \code{\link[base:logical]{TRUE}}, the (weighted) sample mean column \code{\link[base]{vector}} is subtracted from each column in \code{mat}, first. If data is not centered, the effect will be that a linear subspace that goes through the origin is fitted.} \item{scale}{If \code{\link[base:logical]{TRUE}}, each column in \code{mat} is divided by its (weighted) root-mean-square of the centered column, first.} \item{method}{If \code{"dgesdd"} LAPACK's divide-and-conquer based SVD routine is used (faster [1]). If \code{"dgesvd"}, LAPACK's QR-decomposition-based routine is used. } \item{swapDirections}{If \code{\link[base:logical]{TRUE}}, the signs of eigenvectors that have more negative than positive components are inverted. The signs of corresponding principal components are also inverted. This is only of interest when for instance visualizing or comparing with other PCA estimates from other methods, because the PCA (SVD) decomposition of a matrix is not unique. } \item{...}{Not used.} } \value{ Returns a \code{\link[base]{list}} with elements: \item{pc}{An NxK \code{\link[base]{matrix}} where the column \code{\link[base]{vector}}s are the principal components (a.k.a. loading vectors, spectral loadings or factors etc).} \item{d}{An K \code{\link[base]{vector}} containing the eigenvalues of the principal components.} \item{vt}{An KxK \code{\link[base]{matrix}} containing the eigenvector of the principal components.} \item{xMean}{The center coordinate.} It holds that \code{x == t(t(fit$pc \%*\% fit$vt) + fit$xMean)}. } \section{Method}{ A singular value decomposition (SVD) is carried out. Let X=\code{mat}, then the SVD of the matrix is \eqn{X = U D V'}, where \eqn{U} and \eqn{V} are orthogonal, and \eqn{D} is a diagonal matrix with singular values. The principal returned by this method are \eqn{U D}. Internally \code{La.svd()} (or \code{svd()}) of the \pkg{base} package is used. For a popular and well written introduction to SVD see for instance [2]. } \examples{ for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break # ------------------------------------------------------------- # A first example # ------------------------------------------------------------- # Simulate data from the model y <- a + bx + eps(bx) x <- rexp(1000) a <- c(2,15,3) b <- c(2,3,15) bx <- outer(b,x) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) y <- a + bx + eps y <- t(y) # Add some outliers by permuting the dimensions for 1/3 of the observations idx <- sample(1:nrow(y), size=1/3*nrow(y)) y[idx,] <- y[idx,c(2,3,1)] # Down-weight the outliers W times to demonstrate how weights are used W <- 10 # Plot the data with fitted lines at four different view points N <- 4 theta <- seq(0,180,length.out=N) phi <- rep(30, length.out=N) # Use a different color for each set of weights col <- topo.colors(W) opar <- par(mar=c(1,1,1,1)+0.1) layout(matrix(1:N, nrow=2, byrow=TRUE)) for (kk in seq_along(theta)) { # Plot the data plot3d(y, theta=theta[kk], phi=phi[kk]) # First, same weights for all observations w <- rep(1, length=nrow(y)) for (ww in 1:W) { # Fit a line using IWPCA through data fit <- wpca(y, w=w, swapDirections=TRUE) # Get the first principal component ymid <- fit$xMean d0 <- apply(y, MARGIN=2, FUN=min) - ymid d1 <- apply(y, MARGIN=2, FUN=max) - ymid b <- fit$vt[1,] y0 <- -b * max(abs(d0)) y1 <- b * max(abs(d1)) yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) yline <- yline + ymid points3d(t(ymid), col=col) lines3d(t(yline), col=col) # Down-weight outliers only, because here we know which they are. w[idx] <- w[idx]/2 } # Highlight the last one lines3d(t(yline), col="red", lwd=3) } par(opar) } # for (zzz in 0) rm(zzz) if (dev.cur() > 1) dev.off() # ------------------------------------------------------------- # A second example # ------------------------------------------------------------- # Data x <- c(1,2,3,4,5) y <- c(2,4,3,3,6) opar <- par(bty="L") opalette <- palette(c("blue", "red", "black")) xlim <- ylim <- c(0,6) # Plot the data and the center mass plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim) points(mean(x), mean(y), cex=2, lwd=2, col="blue") # Linear regression y ~ x fit <- lm(y ~ x) abline(fit, lty=1, col=1) # Linear regression y ~ x through without intercept fit <- lm(y ~ x - 1) abline(fit, lty=2, col=1) # Linear regression x ~ y fit <- lm(x ~ y) c <- coefficients(fit) b <- 1/c[2] a <- -b*c[1] abline(a=a, b=b, lty=1, col=2) # Linear regression x ~ y through without intercept fit <- lm(x ~ y - 1) b <- 1/coefficients(fit) abline(a=0, b=b, lty=2, col=2) # Orthogonal linear "regression" fit <- wpca(cbind(x,y)) b <- fit$vt[1,2]/fit$vt[1,1] a <- fit$xMean[2]-b*fit$xMean[1] abline(a=a, b=b, lwd=2, col=3) # Orthogonal linear "regression" without intercept fit <- wpca(cbind(x,y), center=FALSE) b <- fit$vt[1,2]/fit$vt[1,1] a <- fit$xMean[2]-b*fit$xMean[1] abline(a=a, b=b, lty=2, lwd=2, col=3) legend(xlim[1],ylim[2], legend=c("lm(y~x)", "lm(y~x-1)", "lm(x~y)", "lm(x~y-1)", "pca", "pca w/o intercept"), lty=rep(1:2,3), lwd=rep(c(1,1,2),each=2), col=rep(1:3,each=2)) palette(opalette) par(opar) } \author{Henrik Bengtsson} \references{ [1] J. Demmel and J. Dongarra, \emph{DOE2000 Progress Report}, 2004. \url{https://people.eecs.berkeley.edu/~demmel/DOE2000/Report0100.html} \cr [2] Todd Will, \emph{Introduction to the Singular Value Decomposition}, UW-La Crosse, 2004. \url{http://websites.uwlax.edu/twill/svd/} \cr } \seealso{ For a iterative re-weighted PCA method, see \code{\link{iwpca}}(). For Singular Value Decomposition, see \code{\link[base]{svd}}(). For other implementations of Principal Component Analysis functions see (if they are installed): \code{\link[stats]{prcomp}} in package \pkg{stats} and \code{pca()} in package \pkg{pcurve}. } \keyword{methods} \keyword{algebra} aroma.light/man/plotMvsMPairs.Rd0000644000175000017500000000241214136047216016436 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % plotMvsMPairs.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{plotMvsMPairs} \alias{plotMvsMPairs} \alias{plotMvsMPairs.matrix} \title{Plot log-ratios vs log-ratios for all pairs of columns} \description{ Plot log-ratios vs log-ratios for all pairs of columns. } \usage{ \method{plotMvsMPairs}{matrix}(X, xlab="M", ylab="M", xlim=c(-1, 1) * 6, ylim=xlim, pch=".", ..., add=FALSE) } \arguments{ \item{X}{Nx2K \code{\link[base]{matrix}} where N is the number of observations and 2K is an even number of channels.} \item{xlab,ylab}{Labels on the x and y axes.} \item{xlim,ylim}{Plot range on the x and y axes.} \item{pch}{Plot symbol used.} \item{...}{Additional arguments accepted by \code{\link[graphics]{points}}.} \item{add}{If \code{\link[base:logical]{TRUE}}, data points are plotted in the current plot, otherwise a new plot is created.} } \details{ Log-ratio are calculated by over paired columns, e.g. column 1 and 2, column 3 and 4, and so on. } \value{ Returns nothing. } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/normalizeTumorBoost.Rd0000644000175000017500000001326714136047216017726 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeTumorBoost.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeTumorBoost} \alias{normalizeTumorBoost} \alias{normalizeTumorBoost.numeric} \title{Normalizes allele B fractions for a tumor given a match normal} \description{ TumorBoost [1] is a normalization method that normalizes the allele B fractions of a tumor sample given the allele B fractions and genotypes of a matched normal. The method is a single-sample (single-pair) method. It does not require total copy-number estimates. The normalization is done such that the total copy number is unchanged afterwards. } \usage{ \method{normalizeTumorBoost}{numeric}(betaT, betaN, muN=callNaiveGenotypes(betaN), preserveScale=FALSE, flavor=c("v4", "v3", "v2", "v1"), ...) } \arguments{ \item{betaT, betaN}{Two \code{\link[base]{numeric}} \code{\link[base]{vector}}s each of length J with tumor and normal allele B fractions, respectively.} \item{muN}{An optional \code{\link[base]{vector}} of length J containing normal genotypes calls in (0,1/2,1,\code{\link[base]{NA}}) for (AA,AB,BB).} \item{preserveScale}{If \code{\link[base:logical]{TRUE}}, SNPs that are heterozygous in the matched normal are corrected for signal compression using an estimate of signal compression based on the amount of correction performed by TumorBoost on SNPs that are homozygous in the matched normal.} \item{flavor}{A \code{\link[base]{character}} string specifying the type of correction applied.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J containing the normalized allele B fractions for the tumor. Attribute \code{modelFit} is a \code{\link[base]{list}} containing model fit parameters. } \details{ Allele B fractions are defined as the ratio between the allele B signal and the sum of both (all) allele signals at the same locus. Allele B fractions are typically within [0,1], but may have a slightly wider support due to for instance negative noise. This is typically also the case for the returned normalized allele B fractions. } \section{Flavors}{ This method provides a few different "flavors" for normalizing the data. The following values of argument \code{flavor} are accepted: \itemize{ \item{v4: (default) The TumorBoost method, i.e. Eqns. (8)-(9) in [1].} \item{v3: Eqn (9) in [1] is applied to both heterozygous and homozygous SNPs, which effectively is v4 where the normalized allele B fractions for homozygous SNPs becomes 0 and 1.} \item{v2: ...} \item{v1: TumorBoost where correction factor is forced to one, i.e. \eqn{\eta_j=1}. As explained in [1], this is a suboptimal normalization method. See also the discussion in the paragraph following Eqn (12) in [1].} } } \section{Preserving scale}{ \emph{As of \pkg{aroma.light} v1.33.3 (March 30, 2014), argument \code{preserveScale} no longer has a default value and has to be specified explicitly. This is done in order to change the default to \code{\link[base:logical]{FALSE}} in a future version, while minimizing the risk for surprises.} Allele B fractions are more or less compressed toward a half, e.g. the signals for homozygous SNPs are slightly away from zero and one. The TumorBoost method decreases the correlation in allele B fractions between the tumor and the normal \emph{conditioned on the genotype}. What it does not control for is the mean level of the allele B fraction \emph{conditioned on the genotype}. By design, most flavors of the method will correct the homozygous SNPs such that their mean levels get close to the expected zero and one levels. However, the heterozygous SNPs will typically keep the same mean levels as before. One possibility is to adjust the signals such as the mean levels of the heterozygous SNPs relative to that of the homozygous SNPs is the same after as before the normalization. If argument \code{preserveScale=TRUE}, then SNPs that are heterozygous (in the matched normal) are corrected for signal compression using an estimate of signal compression based on the amount of correction performed by TumorBoost on SNPs that are homozygous (in the matched normal). The option of preserving the scale is \emph{not} discussed in the TumorBoost paper [1], which presents the \code{preserveScale=FALSE} version. } \examples{ library(R.utils) # Load data pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") data <- loadObject(pathname) attachLocally(data) pos <- position/1e6 muN <- genotypeN layout(matrix(1:4, ncol=1)) par(mar=c(2.5,4,0.5,1)+0.1) ylim <- c(-0.05, 1.05) col <- rep("#999999", length(muN)) col[muN == 1/2] <- "#000000" # Allele B fractions for the normal sample plot(pos, betaN, col=col, ylim=ylim) # Allele B fractions for the tumor sample plot(pos, betaT, col=col, ylim=ylim) # TumorBoost w/ naive genotype calls betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE) plot(pos, betaTN, col=col, ylim=ylim) # TumorBoost w/ external multi-sample genotype calls betaTNx <- normalizeTumorBoost(betaT=betaT, betaN=betaN, muN=muN, preserveScale=FALSE) plot(pos, betaTNx, col=col, ylim=ylim) } \author{Henrik Bengtsson, Pierre Neuvial} \references{ [1] H. Bengtsson, P. Neuvial and T.P. Speed, \emph{TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays}, BMC Bioinformatics, 2010, 11:245. [PMID 20462408] \cr } \keyword{methods} aroma.light/man/1._Calibration_and_Normalization.Rd0000644000175000017500000002203414136047216022135 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % 901.CalibrationAndNormalization.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{1. Calibration and Normalization} \alias{1. Calibration and Normalization} \title{1. Calibration and Normalization} \encoding{latin1} \description{ In this section we give \emph{our} recommendation on how spotted two-color (or multi-color) microarray data is best calibrated and normalized. } \section{Classical background subtraction}{ We do \emph{not} recommend background subtraction in classical means where background is estimated by various image analysis methods. This means that we will only consider foreground signals in the analysis. We estimate "background" by other means. In what is explain below, only a global background, that is, a global bias, is estimated and removed. } \section{Multiscan calibration}{ In Bengtsson et al (2004) we give evidence that microarray scanners can introduce a significant bias in data. This bias, which is about 15-25 out of 65535, \emph{will} introduce intensity dependency in the log-ratios, as explained in Bengtsson & \enc{Hssjer}{Hossjer} (2006). In Bengtsson et al (2004) we find that this bias is stable across arrays (and a couple of months), but further research is needed in order to tell if this is true over a longer time period. To calibrate signals for scanner biases, scan the same array at multiple PMT-settings at three or more (K >= 3) different PMT settings (preferably in decreasing order). While doing this, \emph{do not adjust the laser power settings}. Also, do the multiscan \emph{without} washing, cleaning or by other means changing the array between subsequent scans. Although not necessary, it is preferred that the array remains in the scanner between subsequent scans. This will simplify the image analysis since spot identification can be made once if images aligns perfectly. After image analysis, read all K scans for the same array into the two matrices, one for the red and one for the green channel, where the K columns corresponds to scans and the N rows to the spots. It is enough to use foreground signals. In order to multiscan calibrate the data, for each channel separately call \code{Xc <- calibrateMultiscan(X)} where \code{X} is the NxK matrix of signals for one channel across all scans. The calibrated signals are returned in the Nx1 matrix \code{Xc}. Multiscan calibration may sometimes be skipped, especially if affine normalization is applied immediately after, but we do recommend that every lab check at least once if their scanner introduce bias. If the offsets in a scanner is already estimated from earlier multiscan analyses, or known by other means, they can readily be subtracted from the signals of each channel. If arrays are still multiscanned, it is possible to force the calibration method to fit the model with zero intercept (assuming the scanner offsets have been subtracted) by adding argument \code{center=FALSE}. } \section{Affine normalization}{ In Bengtsson & \enc{Hssjer}{Hossjer} (2006), we carry out a detailed study on how biases in each channel introduce so called intensity-dependent log-ratios among other systematic artifacts. Data with (additive) bias in each channel is said to be \emph{affinely} transformed. Data without such bias, is said to be \emph{linearly} (proportionally) transform. Ideally, observed signals (data) is a linear (proportional) function of true gene expression levels. We do \emph{not} assume proportional observations. The scanner bias is real evidence that assuming linearity is not correct. Affine normalization corrects for affine transformation in data. Without control spots it is not possible to estimate the bias in each of the channels but only the relative bias such that after normalization the effective bias are the same in all channels. This is why we call it normalization and not calibration. In its simplest form, affine normalization is done by \code{Xn <- normalizeAffine(X)} where \code{X} is a Nx2 matrix with the first column holds the foreground signals from the red channel and the second holds the signals from the green channel. If three- or four-channel data is used these are added the same way. The normalized data is returned as a Nx2 matrix \code{Xn}. To normalize all arrays and all channels at once, one may put all data into one big NxK matrix where the K columns hold the all channels from the first array, then all channels from the second array and so on. Then \code{Xn <- normalizeAffine(X)} will return the across-array and across-channel normalized data in the NxK matrix \code{Xn} where the columns are stored in the same order as in matrix \code{X}. Equal effective bias in all channels is much better. First of all, any intensity-dependent bias in the log-ratios is removed \emph{for all non-differentially expressed genes}. There is still an intensity-dependent bias in the log-ratios for differentially expressed genes, but this is now symmetric around log-ratio zero. Affine normalization will (by default and recommended) normalize \emph{all} arrays together and at once. This will guarantee that all arrays are "on the same scale". Thus, it \emph{not} recommended to apply a classical between-array scale normalization afterward. Moreover, the average log-ratio will be zero after an affine normalization. Note that an affine normalization will only remove curvature in the log-ratios at lower intensities. If a strong intensity-dependent bias at high intensities remains, this is most likely due to saturation effects, such as too high PMT settings or quenching. Note that for a perfect affine normalization you \emph{should} expect much higher noise levels in the \emph{log-ratios} at lower intensities than at higher. It should also be approximately symmetric around zero log-ratio. In other words, \emph{a strong fanning effect is a good sign}. Due to different noise levels in red and green channels, different PMT settings in different channels, plus the fact that the minimum signal is zero, "odd shapes" may be seen in the log-ratio vs log-intensity graphs at lower intensities. Typically, these show themselves as non-symmetric in positive and negative log-ratios. Note that you should not see this at higher intensities. If there is a strong intensity-dependent effect left after the affine normalization, we recommend, for now, that a subsequent curve-fit or quantile normalization is done. Which one, we do not know. Why negative signals? By default, 5\% of the normalized signals will have a non-positive signal in one or both channels. \emph{This is on purpose}, although the exact number 5\% is chosen by experience. The reason for introducing negative signals is that they are indeed expected. For instance, when measure a zero gene expression level, there is a chance that the observed value is (should be) negative due to measurement noise. (For this reason it is possible that the scanner manufacturers have introduced scanner bias on purpose to avoid negative signals, which then all would be truncated to zero.) To adjust the ratio (or number) of negative signals allowed, use for example \code{normalizeAffine(X, constraint=0.01)} for 1\% negative signals. If set to zero (or \code{"max"}) only as much bias is removed such that no negative signals exist afterward. Note that this is also true if there were negative signals on beforehand. Why not lowess normalization? Curve-fit normalization methods such as lowess normalization are basically designed based on linearity assumptions and will for this reason not correct for channel biases. Curve-fit normalization methods can by definition only be applied to one pair of channels at the time and do therefore require a subsequent between-array scale normalization, which is by the way very ad hoc. Why not quantile normalization? Affine normalization can be though of a special case of quantile normalization that is more robust than the latter. See Bengtsson & \enc{Hssjer}{Hossjer} (2006) for details. Quantile normalization is probably better to apply than curve-fit normalization methods, but less robust than affine normalization, especially at extreme (low and high) intensities. For this reason, we do recommend to use affine normalization first, and if this is not satisfactory, quantile normalization may be applied. } \section{Linear (proportional) normalization}{ If the channel offsets are zero, already corrected for, or estimated by other means, it is possible to normalize the data robustly by fitting the above affine model without intercept, that is, fitting a truly linear model. This is done adding argument \code{center=FALSE} when calling \code{normalizeAffine()}. } \author{Henrik Bengtsson} \keyword{documentation} aroma.light/man/medianPolish.Rd0000644000175000017500000000461114136047216016275 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % medianPolish.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{medianPolish} \alias{medianPolish} \alias{medianPolish.matrix} \title{Median polish} \description{ Median polish. } \usage{ \method{medianPolish}{matrix}(X, tol=0.01, maxIter=10L, na.rm=NA, ..., .addExtra=TRUE) } \arguments{ \item{X}{N-times-K \code{\link[base]{matrix}}} \item{tol}{A \code{\link[base]{numeric}} value greater than zero used as a threshold to identify when the algorithm has converged.} \item{maxIter}{Maximum number of iterations.} \item{na.rm}{If \code{\link[base:logical]{TRUE}} (\code{\link[base:logical]{FALSE}}), \code{\link[base]{NA}}s are exclude (not exclude). If \code{\link[base]{NA}}, it is assumed that \code{X} contains no \code{\link[base]{NA}} values.} \item{.addExtra}{If \code{\link[base:logical]{TRUE}}, the name of argument \code{X} is returned and the returned structure is assigned a class. This will make the result compatible what \code{\link[stats]{medpolish}} returns.} \item{...}{Not used.} } \value{ Returns a named \code{\link[base]{list}} structure with elements: \item{overall}{The fitted constant term.} \item{row}{The fitted row effect.} \item{col}{The fitted column effect.} \item{residuals}{The residuals.} \item{converged}{If \code{\link[base:logical]{TRUE}}, the algorithm converged, otherwise not.} } \details{ The implementation of this method give identical estimates as \code{\link[stats]{medpolish}}, but is about 3-5 times more efficient when there are no \code{\link[base]{NA}} values. } \author{Henrik Bengtsson} \examples{ # Deaths from sport parachuting; from ABC of EDA, p.224: deaths <- matrix(c(14,15,14, 7,4,7, 8,2,10, 15,9,10, 0,2,0), ncol=3, byrow=TRUE) rownames(deaths) <- c("1-24", "25-74", "75-199", "200++", "NA") colnames(deaths) <- 1973:1975 print(deaths) mp <- medianPolish(deaths) mp1 <- medpolish(deaths, trace=FALSE) print(mp) ff <- c("overall", "row", "col", "residuals") stopifnot(all.equal(mp[ff], mp1[ff])) # Validate decomposition: stopifnot(all.equal(deaths, mp$overall+outer(mp$row,mp$col,"+")+mp$resid)) } \seealso{ \code{\link[stats]{medpolish}}. } \keyword{methods} \keyword{algebra} aroma.light/man/normalizeQuantileRank.matrix.Rd0000644000175000017500000000700214136047216021500 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeQuantileRank.matrix.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeQuantileRank.matrix} \alias{normalizeQuantileRank.matrix} \title{Normalizes the empirical distribution of a set of samples to a common target distribution} \usage{ \method{normalizeQuantileRank}{matrix}(X, ties=FALSE, robust=FALSE, weights=NULL, typeOfWeights=c("channel", "signal"), ...) } \description{ Normalizes the empirical distribution of a set of samples to a common target distribution. The average sample distribution is calculated either robustly or not by utilizing either \code{weightedMedian()} or \code{weighted.mean()}. A weighted method is used if any of the weights are different from one. } \arguments{ \item{X}{a numerical NxK \code{\link[base]{matrix}} with the K columns representing the channels and the N rows representing the data points.} \item{robust}{If \code{\link[base:logical]{TRUE}}, the (weighted) median function is used for calculating the average sample distribution, otherwise the (weighted) mean function is used.} \item{ties}{Should ties in \code{x} be treated with care or not? For more details, see "limma:normalizeQuantiles".} \item{weights}{If \code{\link[base]{NULL}}, non-weighted normalization is done. If channel weights, this should be a \code{\link[base]{vector}} of length K specifying the weights for each channel. If signal weights, it should be an NxK \code{\link[base]{matrix}} specifying the weights for each signal. } \item{typeOfWeights}{A \code{\link[base]{character}} string specifying the type of weights given in argument \code{weights}.} \item{...}{Not used.} } \value{ Returns an object of the same shape as the input argument. } \section{Missing values}{ Missing values are excluded when estimating the "common" (the baseline). Values that are \code{\link[base]{NA}} remain \code{\link[base]{NA}} after normalization. No new \code{\link[base]{NA}}s are introduced. } \section{Weights}{ Currently only channel weights are support due to the way quantile normalization is done. If signal weights are given, channel weights are calculated from these by taking the mean of the signal weights in each channel. } \examples{ # Simulate three samples with on average 20\% missing values N <- 10000 X <- cbind(rnorm(N, mean=3, sd=1), rnorm(N, mean=4, sd=2), rgamma(N, shape=2, rate=1)) X[sample(3*N, size=0.20*3*N)] <- NA # Normalize quantiles Xn <- normalizeQuantile(X) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, Xn, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") } \author{ Adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 \& 2006. Original code by Ben Bolstad at Statistics Department, University of California. Support for calculating the average sample distribution using (weighted) mean or median was added by Henrik Bengtsson. } \seealso{ \code{\link[stats]{median}}, \code{\link[matrixStats]{weightedMedian}}, \code{\link[base]{mean}}() and \code{\link[stats]{weighted.mean}}. \code{\link{normalizeQuantileSpline}}(). } \keyword{methods} \keyword{nonparametric} \keyword{multivariate} \keyword{robust} aroma.light/man/normalizeAverage.Rd0000644000175000017500000000270614136047216017157 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeAverage.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeAverage} \alias{normalizeAverage} \alias{normalizeAverage.list} \alias{normalizeAverage.matrix} \title{Rescales channel vectors to get the same average} \description{ Rescales channel vectors to get the same average. } \usage{ \method{normalizeAverage}{matrix}(x, baseline=1, avg=stats::median, targetAvg=2200, ...) \method{normalizeAverage}{list}(x, baseline=1, avg=stats::median, targetAvg=2200, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}} (or \code{\link[base]{list}} of length K).} \item{baseline}{An \code{\link[base]{integer}} in [1,K] specifying which channel should be the baseline.} \item{avg}{A \code{\link[base]{function}} for calculating the average of one channel.} \item{targetAvg}{The average that each channel should have afterwards. If \code{\link[base]{NULL}}, the baseline column sets the target average.} \item{...}{Additional arguments passed to the \code{avg} \code{\link[base]{function}}.} } \value{ Returns a normalized \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}} (or \code{\link[base]{list}} of length K). } \author{Henrik Bengtsson} \keyword{methods} aroma.light/man/sampleCorrelations.Rd0000644000175000017500000000340314136047216017525 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % sampleCorrelations.matrix.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{sampleCorrelations} \alias{sampleCorrelations} \alias{sampleCorrelations.matrix} \title{Calculates the correlation for random pairs of observations} \description{ Calculates the correlation for random pairs of observations. } \usage{ \method{sampleCorrelations}{matrix}(X, MARGIN=1, pairs=NULL, npairs=max(5000, nrow(X)), ...) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} where N >= 2 and K >= 2.} \item{MARGIN}{The dimension (1 or 2) in which the observations are. If \code{MARGIN==1} (\code{==2}), each row (column) is an observation.} \item{pairs}{If a Lx2 \code{\link[base]{matrix}}, the L index pairs for which the correlations are calculated. If \code{\link[base]{NULL}}, pairs of observations are sampled.} \item{npairs}{The number of correlations to calculate.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{double}} \code{\link[base]{vector}} of length \code{npairs}. } \author{Henrik Bengtsson} \examples{ # Simulate 20000 genes with 10 observations each X <- matrix(rnorm(n=20000), ncol=10) # Calculate the correlation for 5000 random gene pairs cor <- sampleCorrelations(X, npairs=5000) print(summary(cor)) } \seealso{ \code{\link[base]{sample}}(). } \references{ [1] A. Ploner, L. Miller, P. Hall, J. Bergh & Y. Pawitan. \emph{Correlation test to assess low-level processing of high-density oligonucleotide microarray data}. BMC Bioinformatics, 2005, vol 6. } \keyword{methods} \keyword{utilities} aroma.light/man/pairedAlleleSpecificCopyNumbers.Rd0000644000175000017500000000305514136047216022102 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % pairedAlleleSpecificCopyNumbers.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{pairedAlleleSpecificCopyNumbers} \alias{pairedAlleleSpecificCopyNumbers} \alias{pairedAlleleSpecificCopyNumbers.numeric} \title{Calculating tumor-normal paired allele-specific copy number stratified on genotypes} \description{ Calculating tumor-normal paired allele-specific copy number stratified on genotypes. The method is a single-sample (single-pair) method. It requires paired tumor-normal parent-specific copy number signals. } \usage{ \method{pairedAlleleSpecificCopyNumbers}{numeric}(thetaT, betaT, thetaN, betaN, muN=callNaiveGenotypes(betaN), ...) } \arguments{ \item{thetaT, betaT}{Theta and allele-B fraction signals for the tumor.} \item{thetaN, betaN}{Total and allele-B fraction signals for the matched normal.} \item{muN}{An optional \code{\link[base]{vector}} of length J containing normal genotypes calls in (0,1/2,1,\code{\link[base]{NA}}) for (AA,AB,BB).} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{data.frame}} with elements \code{CT}, \code{betaT} and \code{muN}. } \seealso{ This definition of calculating tumor-normal paired ASCN is related to how the \code{\link{normalizeTumorBoost}}() method calculates normalized tumor BAFs. } \author{Pierre Neuvial, Henrik Bengtsson} \keyword{methods} aroma.light/man/calibrateMultiscan.Rd0000644000175000017500000001412414136047216017467 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % calibrateMultiscan.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{calibrateMultiscan} \alias{calibrateMultiscan} \alias{calibrateMultiscan.matrix} \encoding{latin1} \title{Weighted affine calibration of a multiple re-scanned channel} \description{ Weighted affine calibration of a multiple re-scanned channel. } \usage{ \method{calibrateMultiscan}{matrix}(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1", constraint="diagonal", satSignal=2^16 - 1, ..., average=median, deviance=NULL, project=FALSE, .fitOnly=FALSE) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} (K>=2) where the columns represent the multiple scans of one channel (a two-color array contains two channels) to be calibrated.} \item{weights}{If \code{\link[base]{NULL}}, non-weighted normalization is done. If data-point weights are used, this should be a \code{\link[base]{vector}} of length N of data point weights used when estimating the normalization function. } \item{typeOfWeights}{A \code{\link[base]{character}} string specifying the type of weights given in argument \code{weights}. } \item{method}{A \code{\link[base]{character}} string specifying how the estimates are robustified. See \code{\link{iwpca}}() for all accepted values.} \item{constraint}{Constraint making the bias parameters identifiable. See \code{\link{fitIWPCA}}() for more details.} \item{satSignal}{Signals equal to or above this threshold is considered saturated signals.} \item{...}{Other arguments passed to \code{\link{fitIWPCA}}() and in turn \code{\link{iwpca}}(), e.g. \code{center} (see below).} \item{average}{A \code{\link[base]{function}} to calculate the average signals between calibrated scans.} \item{deviance}{A \code{\link[base]{function}} to calculate the deviance of the signals between calibrated scans.} \item{project}{If \code{\link[base:logical]{TRUE}}, the calibrated data points projected onto the diagonal line, otherwise not. Moreover, if \code{\link[base:logical]{TRUE}}, argument \code{average} is ignored.} \item{.fitOnly}{If \code{\link[base:logical]{TRUE}}, the data will not be back-transform.} } \value{ If \code{average} is specified or \code{project} is \code{\link[base:logical]{TRUE}}, an Nx1 \code{\link[base]{matrix}} is returned, otherwise an NxK \code{\link[base]{matrix}} is returned. If \code{deviance} is specified, a deviance Nx1 \code{\link[base]{matrix}} is returned as attribute \code{deviance}. In addition, the fitted model is returned as attribute \code{modelFit}. } \section{Negative, non-positive, and saturated values}{ Affine multiscan calibration applies also to negative values, which are therefor also calibrated, if they exist. Saturated signals in any scan are set to \code{\link[base]{NA}}. Thus, they will not be used to estimate the calibration function, nor will they affect an optional projection. } \section{Missing values}{ Only observations (rows) in \code{X} that contain all finite values are used in the estimation of the calibration functions. Thus, observations can be excluded by setting them to \code{\link[base]{NA}}. } \section{Weighted normalization}{ Each data point/observation, that is, each row in \code{X}, which is a vector of length K, can be assigned a weight in [0,1] specifying how much it should \emph{affect the fitting of the calibration function}. Weights are given by argument \code{weights}, which should be a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N. Regardless of weights, all data points are \emph{calibrated} based on the fitted calibration function. } \section{Robustness}{ By default, the model fit of multiscan calibration is done in \eqn{L_1} (\code{method="L1"}). This way, outliers affect the parameter estimates less than ordinary least-square methods. When calculating the average calibrated signal from multiple scans, by default the median is used, which further robustify against outliers. For further robustness, downweight outliers such as saturated signals, if possible. Tukey's biweight function is supported, but not used by default because then a "bandwidth" parameter has to selected. This can indeed be done automatically by estimating the standard deviation, for instance using MAD. However, since scanner signals have heteroscedastic noise (standard deviation is approximately proportional to the non-logged signal), Tukey's bandwidth parameter has to be a function of the signal too, cf. \code{\link[stats]{loess}}. We have experimented with this too, but found that it does not significantly improve the robustness compared to \eqn{L_1}. Moreover, using Tukey's biweight as is, that is, assuming homoscedastic noise, seems to introduce a (scale dependent) bias in the estimates of the offset terms. } \section{Using a known/previously estimated offset}{ If the scanner offsets can be assumed to be known, for instance, from prior multiscan analyses on the scanner, then it is possible to fit the scanner model with no (zero) offset by specifying argument \code{center=FALSE}. Note that you cannot specify the offset. Instead, subtract it from all signals before calibrating, e.g. \code{Xc <- calibrateMultiscan(X-e, center=FALSE)} where \code{e} is the scanner offset (a scalar). You can assert that the model is fitted without offset by \code{stopifnot(all(attr(Xc, "modelFit")$adiag == 0))}. } \details{ Fitting is done by iterated re-weighted principal component analysis (IWPCA). } \author{Henrik Bengtsson} \references{ [1] H. Bengtsson, J. Vallon-Christersson and G. \enc{Jnsson}{Jonsson}, \emph{Calibration and assessment of channel-specific biases in microarray data with extended dynamical range}, BMC Bioinformatics, 5:177, 2004. \cr } \examples{\dontrun{# For an example, see help(normalizeAffine).}} \seealso{ \code{\link{1. Calibration and Normalization}}. \code{\link{normalizeAffine}}(). } \keyword{methods} aroma.light/man/iwpca.Rd0000644000175000017500000001171514136047216014767 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % iwpca.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{iwpca} \alias{iwpca} \alias{iwpca.matrix} \title{Fits an R-dimensional hyperplane using iterative re-weighted PCA} \description{ Fits an R-dimensional hyperplane using iterative re-weighted PCA. } \usage{ \method{iwpca}{matrix}(X, w=NULL, R=1, method=c("symmetric", "bisquare", "tricube", "L1"), maxIter=30, acc=1e-04, reps=0.02, fit0=NULL, ...) } \arguments{ \item{X}{N-times-K \code{\link[base]{matrix}} where N is the number of observations and K is the number of dimensions.} \item{w}{An N \code{\link[base]{vector}} of weights for each row (observation) in the data matrix. If \code{\link[base]{NULL}}, all observations get the same weight.} \item{R}{Number of principal components to fit. By default a line is fitted.} \item{method}{ If \code{"symmetric"} (or \code{"bisquare"}), Tukey's biweight is used. If \code{"tricube"}, the tricube weight is used. If \code{"L1"}, the model is fitted in \eqn{L_1}. If a \code{\link[base]{function}}, it is used to calculate weights for next iteration based on the current iteration's residuals.} \item{maxIter}{Maximum number of iterations.} \item{acc}{The (Euclidean) distance between two subsequent parameters fit for which the algorithm is considered to have converged.} \item{reps}{Small value to be added to the residuals before the the weights are calculated based on their inverse. This is to avoid infinite weights.} \item{fit0}{A \code{\link[base]{list}} containing elements \code{vt} and \code{pc} specifying an initial fit. If \code{\link[base]{NULL}}, the initial guess will be equal to the (weighted) PCA fit.} \item{...}{Additional arguments accepted by \code{\link{wpca}}().} } \value{ Returns the fit (a \code{\link[base]{list}}) from the last call to \code{\link{wpca}}() with the additional elements \code{nbrOfIterations} and \code{converged}. } \details{ This method uses weighted principal component analysis (WPCA) to fit a R-dimensional hyperplane through the data with initial internal weights all equal. At each iteration the internal weights are recalculated based on the "residuals". If \code{method=="L1"}, the internal weights are 1 / sum(abs(r) + reps). This is the same as \code{method=function(r) 1/sum(abs(r)+reps)}. The "residuals" are orthogonal Euclidean distance of the principal components R,R+1,...,K. In each iteration before doing WPCA, the internal weighted are multiplied by the weights given by argument \code{w}, if specified. } \author{Henrik Bengtsson} \examples{ for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break # Simulate data from the model y <- a + bx + eps(bx) x <- rexp(1000) a <- c(2,15,3) b <- c(2,3,4) bx <- outer(b,x) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) y <- a + bx + eps y <- t(y) # Add some outliers by permuting the dimensions for 1/10 of the observations idx <- sample(1:nrow(y), size=1/10*nrow(y)) y[idx,] <- y[idx,c(2,3,1)] # Plot the data with fitted lines at four different view points opar <- par(mar=c(1,1,1,1)+0.1) N <- 4 layout(matrix(1:N, nrow=2, byrow=TRUE)) theta <- seq(0,270,length.out=N) phi <- rep(20, length.out=N) xlim <- ylim <- zlim <- c(0,45); persp <- list(); for (kk in seq_along(theta)) { # Plot the data persp[[kk]] <- plot3d(y, theta=theta[kk], phi=phi[kk], xlim=xlim, ylim=ylim, zlim=zlim) } # Weights on the observations # Example a: Equal weights w <- NULL # Example b: More weight on the outliers (uncomment to test) w <- rep(1, length(x)); w[idx] <- 0.8 # ...and show all iterations too with different colors. maxIter <- c(seq(1,20,length.out=10),Inf) col <- topo.colors(length(maxIter)) # Show the fitted value for every iteration for (ii in seq_along(maxIter)) { # Fit a line using IWPCA through data fit <- iwpca(y, w=w, maxIter=maxIter[ii], swapDirections=TRUE) ymid <- fit$xMean d0 <- apply(y, MARGIN=2, FUN=min) - ymid d1 <- apply(y, MARGIN=2, FUN=max) - ymid b <- fit$vt[1,] y0 <- -b * max(abs(d0)) y1 <- b * max(abs(d1)) yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) yline <- yline + ymid for (kk in seq_along(theta)) { # Set pane to draw in par(mfg=c((kk-1) \%/\% 2, (kk-1) \%\% 2) + 1); # Set the viewpoint of the pane options(persp.matrix=persp[[kk]]); # Get the first principal component points3d(t(ymid), col=col[ii]) lines3d(t(yline), col=col[ii]) # Highlight the last one if (ii == length(maxIter)) lines3d(t(yline), col="red", lwd=3) } } par(opar) } # for (zzz in 0) rm(zzz) } \seealso{ Internally \code{\link{wpca}}() is used for calculating the weighted PCA. } \keyword{methods} \keyword{algebra} aroma.light/man/likelihood.smooth.spline.Rd0000644000175000017500000001057214136047216020610 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % likelihood.smooth.spline.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{likelihood.smooth.spline} \alias{likelihood.smooth.spline} \title{Calculate the log likelihood of a smoothing spline given the data} \usage{ \method{likelihood}{smooth.spline}(object, x=NULL, y=NULL, w=NULL, base=exp(1), rel.tol=.Machine$double.eps^(1/8), ...) } \arguments{ \item{object}{The smooth.spline object.} \item{x, y}{The x and y values for which the (weighted) likelihood will be calculated. If \code{x} is of type \code{xy.coords} any value of argument \code{y} will be omitted. If \code{x==NULL}, the x and y values of the smoothing spline will be used.} \item{w}{The weights for which the (weighted) likelihood will be calculated. If \code{\link[base]{NULL}}, weights equal to one are assumed.} \item{base}{The base of the logarithm of the likelihood. If \code{\link[base]{NULL}}, the non-logged likelihood is returned.} \item{rel.tol}{The relative tolerance used in the call to \code{integrate}.} \item{...}{Not used.} } \description{ Calculate the (log) likelihood of a spline given the data used to fit the spline, \eqn{g}. The likelihood consists of two main parts: 1) (weighted) residuals sum of squares, and 2) a penalty term. The penalty term consists of a \emph{smoothing parameter} \eqn{lambda} and a \emph{roughness measure} of the spline \eqn{J(g) = \int g''(t) dt}. Hence, the overall log likelihood is \deqn{\log L(g|x) = (y-g(x))'W(y-g(x)) + \lambda J(g)} In addition to the overall likelihood, all its separate components are also returned. Note: when fitting a smooth spline with \eqn{(x,y)} values where the \eqn{x}'s are \emph{not} unique, \code{smooth.spline} will replace such \eqn{(x,y)}'s with a new pair \eqn{(x,y')} where \eqn{y'} is a reweighted average on the original \eqn{y}'s. It is important to be aware of this. In such cases, the resulting \code{smooth.spline} object does \emph{not} contain all \eqn{(x,y)}'s and therefore this function will not calculate the weighted residuals sum of square on the original data set, but on the data set with unique \eqn{x}'s. See examples below how to calculate the likelihood for the spline with the original data. } \value{ Returns the overall (log) likelihood of class \code{SmoothSplineLikelihood}, a class with the following attributes: \item{wrss}{the (weighted) residual sum of square} \item{penalty}{the penalty which is equal to \code{-lambda*roughness}.} \item{lambda}{the smoothing parameter} \item{roughness}{the value of the roughness functional given the specific smoothing spline and the range of data} } \details{ The roughness penalty for the smoothing spline, \eqn{g}, fitted from data in the interval \eqn{[a,b]} is defined as \deqn{J(g) = \int_a^b g''(t) dt} which is the same as \deqn{J(g) = g'(b) - g'(a)} The latter is calculated internally by using \code{\link[stats]{predict.smooth.spline}}. } \examples{ # Define f(x) f <- expression(0.1*x^4 + 1*x^3 + 2*x^2 + x + 10*sin(2*x)) # Simulate data from this function in the range [a,b] a <- -2; b <- 5 x <- seq(a, b, length.out=3000) y <- eval(f) # Add some noise to the data y <- y + rnorm(length(y), 0, 10) # Plot the function and its second derivative plot(x,y, type="l", lwd=4) # Fit a cubic smoothing spline and plot it g <- smooth.spline(x,y, df=16) lines(g, col="yellow", lwd=2, lty=2) # Calculating the (log) likelihood of the fitted spline l <- likelihood(g) cat("Log likelihood with unique x values:\n") print(l) # Note that this is not the same as the log likelihood of the # data on the fitted spline iff the x values are non-unique x[1:5] <- x[1] # Non-unique x values g <- smooth.spline(x,y, df=16) l <- likelihood(g) cat("\nLog likelihood of the *spline* data set:\n") print(l) # In cases with non unique x values one has to proceed as # below if one want to get the log likelihood for the original # data. l <- likelihood(g, x=x, y=y) cat("\nLog likelihood of the *original* data set:\n") print(l) } \seealso{ \code{\link[stats]{smooth.spline}} and \code{\link{robustSmoothSpline}}(). } \author{Henrik Bengtsson} \keyword{methods} \keyword{smooth} \keyword{internal} aroma.light/man/backtransformAffine.Rd0000644000175000017500000000716214136047216017632 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % backtransformAffine.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{backtransformAffine} \alias{backtransformAffine} \alias{backtransformAffine.matrix} \title{Reverse affine transformation} \description{ Reverse affine transformation. } \usage{ \method{backtransformAffine}{matrix}(X, a=NULL, b=NULL, project=FALSE, ...) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} containing data to be backtransformed.} \item{a}{A scalar, \code{\link[base]{vector}}, a \code{\link[base]{matrix}}, or a \code{\link[base]{list}}. First, if a \code{\link[base]{list}}, it is assumed to contained the elements \code{a} and \code{b}, which are the used as if they were passed as separate arguments. If a \code{\link[base]{vector}}, a matrix of size NxK is created which is then filled \emph{row by row} with the values in the vector. Commonly, the vector is of length K, which means that the matrix will consist of copies of this vector stacked on top of each other. If a \code{\link[base]{matrix}}, a matrix of size NxK is created which is then filled \emph{column by column} with the values in the matrix (collected column by column. Commonly, the matrix is of size NxK, or NxL with L < K and then the resulting matrix consists of copies sitting next to each other. The resulting NxK matrix is subtracted from the NxK matrix \code{X}. } \item{b}{A scalar, \code{\link[base]{vector}}, a \code{\link[base]{matrix}}. A NxK matrix is created from this argument. For details see argument \code{a}. The NxK matrix \code{X-a} is divided by the resulting NxK matrix. } \item{project}{ returned (K values per data point are returned). If \code{\link[base:logical]{TRUE}}, the backtransformed values "\code{(X-a)/b}" are projected onto the line L(a,b) so that all columns will be identical. } \item{...}{Not used.} } \value{ The "\code{(X-a)/b}" backtransformed NxK \code{\link[base]{matrix}} is returned. If \code{project} is \code{\link[base:logical]{TRUE}}, an Nx1 \code{\link[base]{matrix}} is returned, because all columns are identical anyway. } \section{Missing values}{ Missing values remain missing values. If projected, data points that contain missing values are projected without these. } \examples{ X <- matrix(1:8, nrow=4, ncol=2) X[2,2] <- NA print(X) # Returns a 4x2 matrix print(backtransformAffine(X, a=c(1,5))) # Returns a 4x2 matrix print(backtransformAffine(X, b=c(1,1/2))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:4,ncol=1))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:3,ncol=1))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:2,ncol=1), b=c(1,2))) # Returns a 4x1 matrix print(backtransformAffine(X, b=c(1,1/2), project=TRUE)) # If the columns of X are identical, and a identity # backtransformation is applied and projected, the # same matrix is returned. X <- matrix(1:4, nrow=4, ncol=3) Y <- backtransformAffine(X, b=c(1,1,1), project=TRUE) print(X) print(Y) stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) # If the columns of X are identical, and a identity # backtransformation is applied and projected, the # same matrix is returned. X <- matrix(1:4, nrow=4, ncol=3) X[,2] <- X[,2]*2; X[,3] <- X[,3]*3 print(X) Y <- backtransformAffine(X, b=c(1,2,3)) print(Y) Y <- backtransformAffine(X, b=c(1,2,3), project=TRUE) print(Y) stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) } \keyword{methods} aroma.light/man/callNaiveGenotypes.Rd0000644000175000017500000000726714136047216017467 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % callNaiveGenotypes.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{callNaiveGenotypes} \alias{callNaiveGenotypes} \alias{callNaiveGenotypes.numeric} \title{Calls genotypes in a normal sample} \description{ Calls genotypes in a normal sample. } \usage{ \method{callNaiveGenotypes}{numeric}(y, cn=rep(2L, times = length(y)), ..., modelFit=NULL, verbose=FALSE) } \arguments{ \item{y}{A \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J containing allele B fractions for a normal sample.} \item{cn}{An optional \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J specifying the true total copy number in \eqn{\{0,1,2,NA\}} at each locus. This can be used to specify which loci are diploid and which are not, e.g. autosomal and sex chromosome copy numbers.} \item{...}{Additional arguments passed to \code{\link{fitNaiveGenotypes}}().} \item{modelFit}{A optional model fit as returned by \code{\link{fitNaiveGenotypes}}().} \item{verbose}{A \code{\link[base]{logical}} or a \code{\link[R.utils]{Verbose}} object.} } \value{ Returns a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length J containing the genotype calls in allele B fraction space, that is, in [0,1] where 1/2 corresponds to a heterozygous call, and 0 and 1 corresponds to homozygous A and B, respectively. Non called genotypes have value \code{\link[base]{NA}}. } \examples{ layout(matrix(1:3, ncol=1)) par(mar=c(2,4,4,1)+0.1) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A bimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAA <- rnorm(n=10000, mean=0, sd=0.1) xBB <- rnorm(n=10000, mean=1, sd=0.1) x <- c(xAA,xBB) fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x, cn=rep(1,length(x)), verbose=-20) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,BB)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAB <- rnorm(n=10000, mean=1/2, sd=0.1) x <- c(xAA,xAB,xBB) x[sample(length(x), size=0.05*length(x))] <- NA; x[sample(length(x), size=0.01*length(x))] <- -Inf; x[sample(length(x), size=0.01*length(x))] <- +Inf; fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,AB,BB)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with clear separation # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAA <- rnorm(n=10000, mean=0, sd=0.02) xAB <- rnorm(n=10000, mean=1/2, sd=0.02) xBB <- rnorm(n=10000, mean=1, sd=0.02) x <- c(xAA,xAB,xBB) fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA',AB',BB')") abline(v=fit$x) } \section{Missing and non-finite values}{ A missing value always gives a missing (\code{\link[base]{NA}}) genotype call. Negative infinity (-\code{\link[base:is.finite]{Inf}}) always gives genotype call 0. Positive infinity (+\code{\link[base:is.finite]{Inf}}) always gives genotype call 1. } \author{Henrik Bengtsson} \seealso{ Internally \code{\link{fitNaiveGenotypes}}() is used to identify the thresholds. } \keyword{methods} aroma.light/man/fitXYCurve.Rd0000644000175000017500000000721614136047216015735 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % fitXYCurve.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{fitXYCurve} \alias{fitXYCurve} \alias{fitXYCurve.matrix} \alias{backtransformXYCurve} \alias{backtransformXYCurve.matrix} \title{Fitting a smooth curve through paired (x,y) data} \description{ Fitting a smooth curve through paired (x,y) data. } \usage{ \method{fitXYCurve}{matrix}(X, weights=NULL, typeOfWeights=c("datapoint"), method=c("loess", "lowess", "spline", "robustSpline"), bandwidth=NULL, satSignal=2^16 - 1, ...) } \arguments{ \item{X}{An Nx2 \code{\link[base]{matrix}} where the columns represent the two channels to be normalized.} \item{weights}{If \code{\link[base]{NULL}}, non-weighted normalization is done. If data-point weights are used, this should be a \code{\link[base]{vector}} of length N of data point weights used when estimating the normalization function. } \item{typeOfWeights}{A \code{\link[base]{character}} string specifying the type of weights given in argument \code{weights}. } \item{method}{\code{\link[base]{character}} string specifying which method to use when fitting the intensity-dependent function. Supported methods: \code{"loess"} (better than lowess), \code{"lowess"} (classic; supports only zero-one weights), \code{"spline"} (more robust than lowess at lower and upper intensities; supports only zero-one weights), \code{"robustSpline"} (better than spline). } \item{bandwidth}{A \code{\link[base]{double}} value specifying the bandwidth of the estimator used. } \item{satSignal}{Signals equal to or above this threshold will not be used in the fitting. } \item{...}{Not used.} } \value{ A named \code{\link[base]{list}} structure of class \code{XYCurve}. } \section{Missing values}{ The estimation of the function will only be made based on complete non-saturated observations, i.e. observations that contains no \code{\link[base]{NA}} values nor saturated values as defined by \code{satSignal}. } \section{Weighted normalization}{ Each data point, that is, each row in \code{X}, which is a vector of length 2, can be assigned a weight in [0,1] specifying how much it should \emph{affect the fitting of the normalization function}. Weights are given by argument \code{weights}, which should be a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N. Note that the lowess and the spline method only support zero-one \{0,1\} weights. For such methods, all weights that are less than a half are set to zero. } \section{Details on loess}{ For \code{\link[stats]{loess}}, the arguments \code{family="symmetric"}, \code{degree=1}, \code{span=3/4}, \code{control=loess.control(trace.hat="approximate"}, \code{iterations=5}, \code{surface="direct")} are used. } \author{Henrik Bengtsson} \examples{ # Simulate data from the model y <- a + bx + x^c + eps(bx) x <- rexp(1000) a <- c(2,15) b <- c(2,1) c <- c(1,2) bx <- outer(b,x) xc <- t(sapply(c, FUN=function(c) x^c)) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) Y <- a + bx + xc + eps Y <- t(Y) lim <- c(0,70) plot(Y, xlim=lim, ylim=lim) # Fit principal curve through a subset of (y_1, y_2) subset <- sample(nrow(Y), size=0.3*nrow(Y)) fit <- fitXYCurve(Y[subset,], bandwidth=0.2) lines(fit, col="red", lwd=2) # Backtransform (y_1, y_2) keeping y_1 unchanged YN <- backtransformXYCurve(Y, fit=fit) points(YN, col="blue") abline(a=0, b=1, col="red", lwd=2) } \keyword{methods} aroma.light/man/sampleTuples.Rd0000644000175000017500000000241214136047216016334 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % sampleTuples.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{sampleTuples} \alias{sampleTuples.default} \alias{sampleTuples} \title{Sample tuples of elements from a set} \description{ Sample tuples of elements from a set. The elements within a sampled tuple are unique, i.e. no two elements are the same. } \usage{ \method{sampleTuples}{default}(x, size, length, ...) } \arguments{ \item{x}{A set of elements to sample from.} \item{size}{The number of tuples to sample.} \item{length}{The length of each tuple.} \item{...}{Additional arguments passed to \code{\link[base]{sample}}().} } \value{ Returns a NxK \code{\link[base]{matrix}} where N = \code{size} and K = \code{length}. } \author{Henrik Bengtsson} \examples{ pairs <- sampleTuples(1:10, size=5, length=2) print(pairs) triples <- sampleTuples(1:10, size=5, length=3) print(triples) # Allow tuples with repeated elements quadruples <- sampleTuples(1:3, size=5, length=4, replace=TRUE) print(quadruples) } \seealso{ \code{\link[base]{sample}}(). } \keyword{utilities} aroma.light/man/normalizeAffine.Rd0000644000175000017500000002323514136047216016775 0ustar nileshnilesh%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % normalizeAffine.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{normalizeAffine} \alias{normalizeAffine} \alias{normalizeAffine.matrix} \encoding{latin1} \title{Weighted affine normalization between channels and arrays} \description{ Weighted affine normalization between channels and arrays. This method will remove curvature in the M vs A plots that are due to an affine transformation of the data. In other words, if there are (small or large) biases in the different (red or green) channels, biases that can be equal too, you will get curvature in the M vs A plots and this type of curvature will be removed by this normalization method. Moreover, if you normalize all slides at once, this method will also bring the signals on the same scale such that the log-ratios for different slides are comparable. Thus, do not normalize the scale of the log-ratios between slides afterward. It is recommended to normalize as many slides as possible in one run. The result is that if creating log-ratios between any channels and any slides, they will contain as little curvature as possible. Furthermore, since the relative scale between any two channels on any two slides will be one if one normalizes all slides (and channels) at once it is possible to add or multiply with the \emph{same} constant to all channels/arrays without introducing curvature. Thus, it is easy to rescale the data afterwards as demonstrated in the example. } \usage{ \method{normalizeAffine}{matrix}(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1", constraint=0.05, satSignal=2^16 - 1, ..., .fitOnly=FALSE) } \arguments{ \item{X}{An NxK \code{\link[base]{matrix}} (K>=2) where the columns represent the channels, to be normalized.} \item{weights}{If \code{\link[base]{NULL}}, non-weighted normalization is done. If data-point weights are used, this should be a \code{\link[base]{vector}} of length N of data point weights used when estimating the normalization function. } \item{typeOfWeights}{A \code{\link[base]{character}} string specifying the type of weights given in argument \code{weights}. } \item{method}{A \code{\link[base]{character}} string specifying how the estimates are robustified. See \code{\link{iwpca}}() for all accepted values.} \item{constraint}{Constraint making the bias parameters identifiable. See \code{\link{fitIWPCA}}() for more details.} \item{satSignal}{Signals equal to or above this threshold will not be used in the fitting.} \item{...}{Other arguments passed to \code{\link{fitIWPCA}}() and in turn \code{\link{iwpca}}(). For example, the weight argument of \code{\link{iwpca}}(). See also below.} \item{.fitOnly}{If \code{\link[base:logical]{TRUE}}, the data will not be back-transform.} } \value{ A NxK \code{\link[base]{matrix}} of the normalized channels. The fitted model is returned as attribute \code{modelFit}. } \section{Negative, non-positive, and saturated values}{ Affine normalization applies equally well to negative values. Thus, contrary to normalization methods applied to log-ratios, such as curve-fit normalization methods, affine normalization, will not set these to \code{\link[base]{NA}}. Data points that are saturated in one or more channels are not used to estimate the normalization function, but they are normalized. } \section{Missing values}{ The estimation of the affine normalization function will only be made based on complete non-saturated observations, i.e. observations that contains no \code{\link[base]{NA}} values nor saturated values as defined by \code{satSignal}. } \section{Weighted normalization}{ Each data point/observation, that is, each row in \code{X}, which is a vector of length K, can be assigned a weight in [0,1] specifying how much it should \emph{affect the fitting of the affine normalization function}. Weights are given by argument \code{weights}, which should be a \code{\link[base]{numeric}} \code{\link[base]{vector}} of length N. Regardless of weights, all data points are \emph{normalized} based on the fitted normalization function. } \section{Robustness}{ By default, the model fit of affine normalization is done in \eqn{L_1} (\code{method="L1"}). This way, outliers affect the parameter estimates less than ordinary least-square methods. For further robustness, downweight outliers such as saturated signals, if possible. We do not use Tukey's biweight function for reasons similar to those outlined in \code{\link{calibrateMultiscan}}(). } \section{Using known/previously estimated channel offsets}{ If the channel offsets can be assumed to be known, then it is possible to fit the affine model with no (zero) offset, which formally is a linear (proportional) model, by specifying argument \code{center=FALSE}. In order to do this, the channel offsets have to be subtracted from the signals manually before normalizing, e.g. \code{Xa <- t(t(X)-a)} where \code{e} is \code{\link[base]{vector}} of length \code{ncol(X)}. Then normalize by \code{Xn <- normalizeAffine(Xa, center=FALSE)}. You can assert that the model is fitted without offset by \code{stopifnot(all(attr(Xn, "modelFit")$adiag == 0))}. } \details{ A line is fitted robustly through the \eqn{(y_R,y_G)} observations using an iterated re-weighted principal component analysis (IWPCA), which minimized the residuals that are orthogonal to the fitted line. Each observation is down-weighted by the inverse of the absolute residuals, i.e. the fit is done in \eqn{L_1}. } \author{Henrik Bengtsson} \references{ [1] Henrik Bengtsson and Ola \enc{Hssjer}{Hossjer}, \emph{Methodological Study of Affine Transformations of Gene Expression Data}, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100. \cr } \examples{ pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") rg <- read.table(pathname, header=TRUE, sep="\t") nbrOfScans <- max(rg$slide) rg <- as.list(rg) for (field in c("R", "G")) rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) rg$slide <- rg$spot <- NULL rg <- as.matrix(as.data.frame(rg)) colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) rgC <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) channelColor <- switch(channel, R="red", G="green") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsAPairs(rg[,sidx]) title(main=paste("Observed", channel)) box(col=channelColor) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The calibrated data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) plotMvsAPairs(rgC[,sidx]) title(main=paste("Calibrated", channel)) box(col=channelColor) } # for (channel ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The average calibrated data # # Note how the red signals are weaker than the green. The reason # for this can be that the scale factor in the green channel is # greater than in the red channel, but it can also be that there # is a remaining relative difference in bias between the green # and the red channel, a bias that precedes the scanning. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgCA <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCA[,sidx] <- calibrateMultiscan(rg[,sidx]) } rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2) colnames(rgCAavg) <- c("R", "G") for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE) } # Add some "fake" outliers outliers <- 1:600 rgCAavg[outliers,"G"] <- 50000 plotMvsA(rgCAavg) title(main="Average calibrated (AC)") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Weight-down outliers when normalizing weights <- rep(1, nrow(rgCAavg)) weights[outliers] <- 0.001 # Affine normalization of channels rgCANa <- normalizeAffine(rgCAavg, weights=weights) # It is always ok to rescale the affine normalized data if its # done on (R,G); not on (A,M)! However, this is only needed for # esthetic purposes. rgCANa <- rgCANa *2^1.4 plotMvsA(rgCANa) title(main="Normalized AC") # Curve-fit (lowess) normalization rgCANlw <- normalizeLowess(rgCAavg, weights=weights) plotMvsA(rgCANlw, col="orange", add=TRUE) # Curve-fit (loess) normalization rgCANl <- normalizeLoess(rgCAavg, weights=weights) plotMvsA(rgCANl, col="red", add=TRUE) # Curve-fit (robust spline) normalization rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights) plotMvsA(rgCANrs, col="blue", add=TRUE) legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19, col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine])) title(main="Normalized AC") plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE) plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE) abline(a=0, b=1, lty=2) legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19, col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") } \seealso{ \code{\link{calibrateMultiscan}}(). } \keyword{methods} aroma.light/tests/0000755000175000017500000000000014136047216013757 5ustar nileshnilesharoma.light/tests/rowAverages.matrix.R0000644000175000017500000000035614136047216017676 0ustar nileshnileshlibrary("aroma.light") X <- matrix(1:30, nrow=5L, ncol=6L) mu <- rowMeans(X) sd <- apply(X, MARGIN=1L, FUN=sd) y <- rowAverages(X) stopifnot(all(y == mu)) stopifnot(all(attr(y,"deviance") == sd)) stopifnot(all(attr(y,"df") == ncol(X))) aroma.light/tests/normalizeDifferencesToAverage.R0000644000175000017500000000164414136047216022043 0ustar nileshnileshlibrary("aroma.light") # Simulate three shifted tracks of different lengths with same profiles ns <- c(A=2, B=1, C=0.25)*1000 xx <- lapply(ns, FUN=function(n) { seq(from=1, to=max(ns), length.out=n) }) zz <- mapply(seq_along(ns), ns, FUN=function(z,n) rep(z,n)) yy <- list( A = rnorm(ns["A"], mean=0, sd=0.5), B = rnorm(ns["B"], mean=5, sd=0.4), C = rnorm(ns["C"], mean=-5, sd=1.1) ) yy <- lapply(yy, FUN=function(y) { n <- length(y) y[1:(n/2)] <- y[1:(n/2)] + 2 y[1:(n/4)] <- y[1:(n/4)] - 4 y }) # Shift all tracks toward the first track yyN <- normalizeDifferencesToAverage(yy, baseline=1) # The baseline channel is not changed stopifnot(identical(yy[[1]], yyN[[1]])) # Get the estimated parameters fit <- attr(yyN, "fit") # Plot the tracks layout(matrix(1:2, ncol=1)) x <- unlist(xx) col <- unlist(zz) y <- unlist(yy) yN <- unlist(yyN) plot(x, y, col=col, ylim=c(-10,10)) plot(x, yN, col=col, ylim=c(-10,10)) aroma.light/tests/wpca2.matrix.R0000644000175000017500000000264514136047216016430 0ustar nileshnileshlibrary("aroma.light") # ------------------------------------------------------------- # A second example # ------------------------------------------------------------- # Data x <- c(1,2,3,4,5) y <- c(2,4,3,3,6) opar <- par(bty="L") opalette <- palette(c("blue", "red", "black")) xlim <- ylim <- c(0,6) # Plot the data and the center mass plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim) points(mean(x), mean(y), cex=2, lwd=2, col="blue") # Linear regression y ~ x fit <- lm(y ~ x) abline(fit, lty=1, col=1) # Linear regression y ~ x through without intercept fit <- lm(y ~ x - 1) abline(fit, lty=2, col=1) # Linear regression x ~ y fit <- lm(x ~ y) c <- coefficients(fit) b <- 1/c[2] a <- -b*c[1] abline(a=a, b=b, lty=1, col=2) # Linear regression x ~ y through without intercept fit <- lm(x ~ y - 1) b <- 1/coefficients(fit) abline(a=0, b=b, lty=2, col=2) # Orthogonal linear "regression" fit <- wpca(cbind(x,y)) b <- fit$vt[1,2]/fit$vt[1,1] a <- fit$xMean[2]-b*fit$xMean[1] abline(a=a, b=b, lwd=2, col=3) # Orthogonal linear "regression" without intercept fit <- wpca(cbind(x,y), center=FALSE) b <- fit$vt[1,2]/fit$vt[1,1] a <- fit$xMean[2]-b*fit$xMean[1] abline(a=a, b=b, lty=2, lwd=2, col=3) legend(xlim[1],ylim[2], legend=c("lm(y~x)", "lm(y~x-1)", "lm(x~y)", "lm(x~y-1)", "pca", "pca w/o intercept"), lty=rep(1:2,3), lwd=rep(c(1,1,2),each=2), col=rep(1:3,each=2)) palette(opalette) par(opar) aroma.light/tests/fitPrincipalCurve.matrix.R0000644000175000017500000000422014136047216021034 0ustar nileshnileshlibrary("aroma.light") # Simulate data from the model y <- a + bx + x^c + eps(bx) J <- 1000 x <- rexp(J) a <- c(2,15,3) b <- c(2,3,4) c <- c(1,2,1/2) bx <- outer(b,x) xc <- t(sapply(c, FUN=function(c) x^c)) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(b), mean=0, sd=0.1*x)) y <- a + bx + xc + eps y <- t(y) # Fit principal curve through (y_1, y_2, y_3) fit <- fitPrincipalCurve(y, verbose=TRUE) # Flip direction of 'lambda'? rho <- cor(fit$lambda, y[,1], use="complete.obs") flip <- (rho < 0) if (flip) { fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda } # Backtransform (y_1, y_2, y_3) to be proportional to each other yN <- backtransformPrincipalCurve(y, fit=fit) # Same backtransformation dimension by dimension yN2 <- y for (cc in 1:ncol(y)) { yN2[,cc] <- backtransformPrincipalCurve(y, fit=fit, dimensions=cc) } stopifnot(identical(yN2, yN)) xlim <- c(0, 1.04*max(x)) ylim <- range(c(y,yN), na.rm=TRUE) # Pairwise signals vs x before and after transform layout(matrix(1:4, nrow=2, byrow=TRUE)) par(mar=c(4,4,3,2)+0.1) for (cc in 1:3) { ylab <- substitute(y[c], env=list(c=cc)) plot(NA, xlim=xlim, ylim=ylim, xlab="x", ylab=ylab) abline(h=a[cc], lty=3) mtext(side=4, at=a[cc], sprintf("a=%g", a[cc]), cex=0.8, las=2, line=0, adj=1.1, padj=-0.2) points(x, y[,cc]) points(x, yN[,cc], col="tomato") legend("topleft", col=c("black", "tomato"), pch=19, c("orignal", "transformed"), bty="n") } title(main="Pairwise signals vs x before and after transform", outer=TRUE, line=-2) # Pairwise signals before and after transform layout(matrix(1:4, nrow=2, byrow=TRUE)) par(mar=c(4,4,3,2)+0.1) for (rr in 3:2) { ylab <- substitute(y[c], env=list(c=rr)) for (cc in 1:2) { if (cc == rr) { plot.new() next } xlab <- substitute(y[c], env=list(c=cc)) plot(NA, xlim=ylim, ylim=ylim, xlab=xlab, ylab=ylab) abline(a=0, b=1, lty=2) points(y[,c(cc,rr)]) points(yN[,c(cc,rr)], col="tomato") legend("topleft", col=c("black", "tomato"), pch=19, c("orignal", "transformed"), bty="n") } } title(main="Pairwise signals before and after transform", outer=TRUE, line=-2) aroma.light/tests/callNaiveGenotypes.R0000644000175000017500000000323614136047216017702 0ustar nileshnileshlibrary("aroma.light") layout(matrix(1:3, ncol=1)) par(mar=c(2,4,4,1)+0.1) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A bimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAA <- rnorm(n=10000, mean=0, sd=0.1) xBB <- rnorm(n=10000, mean=1, sd=0.1) x <- c(xAA,xBB) fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x, cn=rep(1,length(x)), verbose=-20) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,BB)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with missing values # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAB <- rnorm(n=10000, mean=1/2, sd=0.1) x <- c(xAA,xAB,xBB) x[sample(length(x), size=0.05*length(x))] <- NA_real_ x[sample(length(x), size=0.01*length(x))] <- -Inf x[sample(length(x), size=0.01*length(x))] <- +Inf fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,AB,BB)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with clear separation # - - - - - - - - - - - - - - - - - - - - - - - - - - - - xAA <- rnorm(n=10000, mean=0, sd=0.02) xAB <- rnorm(n=10000, mean=1/2, sd=0.02) xBB <- rnorm(n=10000, mean=1, sd=0.02) x <- c(xAA,xAB,xBB) fit <- findPeaksAndValleys(x) print(fit) calls <- callNaiveGenotypes(x) xc <- split(x, calls) print(table(calls)) xx <- c(list(x),xc) plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA',AB',BB')") abline(v=fit$x) aroma.light/tests/normalizeAverage.matrix.R0000644000175000017500000000107114136047216020677 0ustar nileshnileshlibrary("aroma.light") # Simulate three samples with on average 20% missing values N <- 10000 X <- cbind(rnorm(N, mean=3, sd=1), rnorm(N, mean=4, sd=2), rgamma(N, shape=2, rate=1)) X[sample(3*N, size=0.20*3*N)] <- NA_real_ # Normalize quantiles Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(X, na.rm=TRUE)) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, Xn, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") aroma.light/tests/sampleCorrelations.matrix.R0000644000175000017500000000034514136047216021255 0ustar nileshnileshlibrary("aroma.light") # Simulate 20000 genes with 10 observations each X <- matrix(rnorm(n=20000), ncol=10) # Calculate the correlation for 5000 random gene pairs cor <- sampleCorrelations(X, npairs=5000) print(summary(cor)) aroma.light/tests/normalizeQuantileRank.list.R0000644000175000017500000000140514136047216021373 0ustar nileshnileshlibrary("aroma.light") # Simulate ten samples of different lengths N <- 10000 X <- list() for (kk in 1:8) { rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]] size <- runif(1, min=0.3, max=1) a <- rgamma(1, shape=20, rate=10) b <- rgamma(1, shape=10, rate=10) values <- rfcn(size*N, a, b) # "Censor" values values[values < 0 | values > 8] <- NA_real_ X[[kk]] <- values } # Add 20% missing values X <- lapply(X, FUN=function(x) { x[sample(length(x), size=0.20*length(x))] <- NA_real_ x }) # Normalize quantiles Xn <- normalizeQuantile(X) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions") aroma.light/tests/normalizeAffine.matrix.R0000644000175000017500000000517614136047216020527 0ustar nileshnileshlibrary("aroma.light") pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") rg <- read.table(pathname, header=TRUE, sep="\t") nbrOfScans <- max(rg$slide) rg <- as.list(rg) for (field in c("R", "G")) rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) rg$slide <- rg$spot <- NULL rg <- as.matrix(as.data.frame(rg)) colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) rgC <- rg layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) channelColor <- switch(channel, R="red", G="green") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsAPairs(rg, channel=channel) title(main=paste("Observed", channel)) box(col=channelColor) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The calibrated data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) plotMvsAPairs(rgC, channel=channel) title(main=paste("Calibrated", channel)) box(col=channelColor) } # for (channel ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The average calibrated data # # Note how the red signals are weaker than the green. The reason # for this can be that the scale factor in the green channel is # greater than in the red channel, but it can also be that there # is a remaining relative difference in bias between the green # and the red channel, a bias that precedes the scanning. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgCA <- matrix(NA_real_, nrow=nrow(rg), ncol=2) colnames(rgCA) <- c("R", "G") for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCA[,channel] <- calibrateMultiscan(rg[,sidx]) } plotMvsA(rgCA) title(main="Average calibrated") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The affine normalized average calibrated data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Create a matrix where the columns represent the channels # to be normalized. rgCAN <- rgCA # Affine normalization of channels rgCAN <- normalizeAffine(rgCAN) plotMvsA(rgCAN) title(main="Affine normalized A.C.") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # It is always ok to rescale the affine normalized data if its # done on (R,G); not on (A,M)! However, this is only needed for # esthetic purposes. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgCAN <- rgCAN * 2^5 plotMvsA(rgCAN) title(main="Rescaled normalized") aroma.light/tests/robustSmoothSpline.R0000644000175000017500000000221314136047216017763 0ustar nileshnileshlibrary("aroma.light") data(cars) attach(cars) plot(speed, dist, main = "data(cars) & robust smoothing splines") # Fit a smoothing spline using L_2 norm cars.spl <- smooth.spline(speed, dist) lines(cars.spl, col = "blue") # Fit a smoothing spline using L_1 norm cars.rspl <- robustSmoothSpline(speed, dist) lines(cars.rspl, col = "red") # Fit a smoothing spline using L_2 norm with 10 degrees of freedom lines(smooth.spline(speed, dist, df=10), lty=2, col = "blue") # Fit a smoothing spline using L_1 norm with 10 degrees of freedom lines(robustSmoothSpline(speed, dist, df=10), lty=2, col = "red") # Fit a smoothing spline using Tukey's biweight norm cars.rspl <- robustSmoothSpline(speed, dist, method = "symmetric") lines(cars.rspl, col = "purple") legend(5,120, c( paste("smooth.spline [C.V.] => df =",round(cars.spl$df,1)), paste("robustSmoothSpline L1 [C.V.] => df =",round(cars.rspl$df,1)), paste("robustSmoothSpline symmetric [C.V.] => df =",round(cars.rspl$df,1)), "standard with s( * , df = 10)", "robust with s( * , df = 10)" ), col = c("blue","red","purple","blue","red"), lty = c(1,1,1,2,2), bg='bisque') aroma.light/tests/wpca.matrix.R0000644000175000017500000000355014136047216016342 0ustar nileshnileshlibrary("aroma.light") for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break # ------------------------------------------------------------- # A first example # ------------------------------------------------------------- # Simulate data from the model y <- a + bx + eps(bx) x <- rexp(1000) a <- c(2,15,3) b <- c(2,3,15) bx <- outer(b,x) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) y <- a + bx + eps y <- t(y) # Add some outliers by permuting the dimensions for 1/3 of the observations idx <- sample(1:nrow(y), size=1/3*nrow(y)) y[idx,] <- y[idx,c(2,3,1)] # Down-weight the outliers W times to demonstrate how weights are used W <- 10 # Plot the data with fitted lines at four different view points N <- 4 theta <- seq(0,180,length.out=N) phi <- rep(30, length.out=N) # Use a different color for each set of weights col <- topo.colors(W) opar <- par(mar=c(1,1,1,1)+0.1) layout(matrix(1:N, nrow=2, byrow=TRUE)) for (kk in seq(theta)) { # Plot the data plot3d(y, theta=theta[kk], phi=phi[kk]) # First, same weights for all observations w <- rep(1, length=nrow(y)) for (ww in 1:W) { # Fit a line using IWPCA through data fit <- wpca(y, w=w, swapDirections=TRUE) # Get the first principal component ymid <- fit$xMean d0 <- apply(y, MARGIN=2, FUN=min) - ymid d1 <- apply(y, MARGIN=2, FUN=max) - ymid b <- fit$vt[1,] y0 <- -b * max(abs(d0)) y1 <- b * max(abs(d1)) yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) yline <- yline + ymid points3d(t(ymid), col=col) lines3d(t(yline), col=col) # Down-weight outliers only, because here we know which they are. w[idx] <- w[idx]/2 } # Highlight the last one lines3d(t(yline), col="red", lwd=3) } par(opar) } # for (zzz in 0) rm(zzz) aroma.light/tests/likelihood.smooth.spline.R0000644000175000017500000000210614136047216021025 0ustar nileshnileshlibrary("aroma.light") # Define f(x) f <- expression(0.1*x^4 + 1*x^3 + 2*x^2 + x + 10*sin(2*x)) # Simulate data from this function in the range [a,b] a <- -2; b <- 5 x <- seq(a, b, length.out=3000) y <- eval(f) # Add some noise to the data y <- y + rnorm(length(y), 0, 10) # Plot the function and its second derivative plot(x,y, type="l", lwd=4) # Fit a cubic smoothing spline and plot it g <- smooth.spline(x,y, df=16) lines(g, col="yellow", lwd=2, lty=2) # Calculating the (log) likelihood of the fitted spline l <- likelihood(g) cat("Log likelihood with unique x values:\n") print(l) # Note that this is not the same as the log likelihood of the # data on the fitted spline iff the x values are non-unique x[1:5] <- x[1] # Non-unique x values g <- smooth.spline(x,y, df=16) l <- likelihood(g) cat("\nLog likelihood of the *spline* data set:\n") print(l) # In cases with non unique x values one has to proceed as # below if one want to get the log likelihood for the original # data. l <- likelihood(g, x=x, y=y) cat("\nLog likelihood of the *original* data set:\n") print(l) aroma.light/tests/normalizeFragmentLength-ex2.R0000644000175000017500000001106314136047216021425 0ustar nileshnileshlibrary("aroma.light") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Example 2: Two-enzyme fragment-length normalization of 6 arrays # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - set.seed(0xbeef) # Number samples I <- 5 # Number of loci J <- 3000 # Fragment lengths (two enzymes) fl <- matrix(0, nrow=J, ncol=2) fl[,1] <- seq(from=100, to=1000, length.out=J) fl[,2] <- seq(from=1000, to=100, length.out=J) # Let 1/2 of the units be on both enzymes fl[seq(from=1, to=J, by=4),1] <- NA_real_ fl[seq(from=2, to=J, by=4),2] <- NA_real_ # Let some have unknown fragment lengths hasUnknownFL <- seq(from=1, to=J, by=15) fl[hasUnknownFL,] <- NA_real_ # Sty/Nsp mixing proportions: rho <- rep(1, I) rho[1] <- 1/3; # Less Sty in 1st sample rho[3] <- 3/2; # More Sty in 3rd sample # Simulate data z <- array(0, dim=c(J,2,I)) maxLog2Theta <- 12 for (ii in 1:I) { # Common effect for both enzymes mu <- function(fl) { k <- runif(n=1, min=3, max=5) mu <- rep(maxLog2Theta, length(fl)) ok <- is.finite(fl) mu[ok] <- mu[ok] - fl[ok]^{1/k} mu } # Calculate the effect for each data point for (ee in 1:2) { z[,ee,ii] <- mu(fl[,ee]) } # Update the Sty/Nsp mixing proportions ee <- 2 z[,ee,ii] <- rho[ii]*z[,ee,ii] # Add random errors for (ee in 1:2) { eps <- rnorm(J, mean=0, sd=1/sqrt(2)) z[,ee,ii] <- z[,ee,ii] + eps } } hasFl <- is.finite(fl) unitSets <- list( nsp = which( hasFl[,1] & !hasFl[,2]), sty = which(!hasFl[,1] & hasFl[,2]), both = which( hasFl[,1] & hasFl[,2]), none = which(!hasFl[,1] & !hasFl[,2]) ) # The observed data is a mix of two enzymes theta <- matrix(NA_real_, nrow=J, ncol=I) # Single-enzyme units for (ee in 1:2) { uu <- unitSets[[ee]] theta[uu,] <- 2^z[uu,ee,] } # Both-enzyme units (sum on intensity scale) uu <- unitSets$both theta[uu,] <- (2^z[uu,1,]+2^z[uu,2,])/2 # Missing units (sample from the others) uu <- unitSets$none theta[uu,] <- apply(theta, MARGIN=2, sample, size=length(uu)) # Calculate target array thetaT <- rowMeans(theta, na.rm=TRUE) targetFcns <- list() for (ee in 1:2) { uu <- unitSets[[ee]] fit <- lowess(fl[uu,ee], log2(thetaT[uu])) class(fit) <- "lowess" targetFcns[[ee]] <- function(fl, ...) { predict(fit, newdata=fl) } } # Fit model only to a subset of the data subsetToFit <- setdiff(1:J, seq(from=1, to=J, by=10)) # Normalize data (to a target baseline) thetaN <- matrix(NA_real_, nrow=J, ncol=I) fits <- vector("list", I) for (ii in 1:I) { lthetaNi <- normalizeFragmentLength(log2(theta[,ii]), targetFcns=targetFcns, fragmentLengths=fl, onMissing="median", subsetToFit=subsetToFit, .returnFit=TRUE) fits[[ii]] <- attr(lthetaNi, "modelFit") thetaN[,ii] <- 2^lthetaNi } # Plot raw data xlim <- c(0, max(fl, na.rm=TRUE)) ylim <- c(0, max(log2(theta), na.rm=TRUE)) Mlim <- c(-1,1)*4 xlab <- "Fragment length" ylab <- expression(log2(theta)) Mlab <- expression(M==log[2](theta/theta[R])) layout(matrix(1:(3*I), ncol=I, byrow=TRUE)) for (ii in 1:I) { plot(NA, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, main="raw") # Single-enzyme units for (ee in 1:2) { # The raw data uu <- unitSets[[ee]] points(fl[uu,ee], log2(theta[uu,ii]), col=ee+1) } # Both-enzyme units (use fragment-length for enzyme #1) uu <- unitSets$both points(fl[uu,1], log2(theta[uu,ii]), col=3+1) for (ee in 1:2) { # The true effects uu <- unitSets[[ee]] lines(lowess(fl[uu,ee], log2(theta[uu,ii])), col="black", lwd=4, lty=3) # The estimated effects fit <- fits[[ii]][[ee]]$fit lines(fit, col="orange", lwd=3) muT <- targetFcns[[ee]](fl[uu,ee]) lines(fl[uu,ee], muT, col="cyan", lwd=1) } } # Calculate log-ratios thetaR <- rowMeans(thetaN, na.rm=TRUE) M <- log2(thetaN/thetaR) # Plot normalized data for (ii in 1:I) { plot(NA, xlim=xlim, ylim=Mlim, xlab=xlab, ylab=Mlab, main="normalized") # Single-enzyme units for (ee in 1:2) { # The normalized data uu <- unitSets[[ee]] points(fl[uu,ee], M[uu,ii], col=ee+1) } # Both-enzyme units (use fragment-length for enzyme #1) uu <- unitSets$both points(fl[uu,1], M[uu,ii], col=3+1) } ylim <- c(0,1.5) for (ii in 1:I) { data <- list() for (ee in 1:2) { # The normalized data uu <- unitSets[[ee]] data[[ee]] <- M[uu,ii] } uu <- unitSets$both if (length(uu) > 0) data[[3]] <- M[uu,ii] uu <- unitSets$none if (length(uu) > 0) data[[4]] <- M[uu,ii] cols <- seq_along(data)+1 plotDensity(data, col=cols, xlim=Mlim, xlab=Mlab, main="normalized") abline(v=0, lty=2) } aroma.light/tests/findPeaksAndValleys.R0000644000175000017500000000217314136047216017774 0ustar nileshnileshlibrary("aroma.light") layout(matrix(1:3, ncol=1)) par(mar=c(2,4,4,1)+0.1) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A unimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x1 <- rnorm(n=10000, mean=0, sd=1) x <- x1 fit <- findPeaksAndValleys(x) print(fit) plot(density(x), lwd=2, main="x1") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x2 <- rnorm(n=10000, mean=4, sd=1) x3 <- rnorm(n=10000, mean=8, sd=1) x <- c(x1,x2,x3) fit <- findPeaksAndValleys(x) print(fit) plot(density(x), lwd=2, main="c(x1,x2,x3)") abline(v=fit$x) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A trimodal distribution with clear separation # - - - - - - - - - - - - - - - - - - - - - - - - - - - - x1b <- rnorm(n=10000, mean=0, sd=0.1) x2b <- rnorm(n=10000, mean=4, sd=0.1) x3b <- rnorm(n=10000, mean=8, sd=0.1) x <- c(x1b,x2b,x3b) # Illustrating explicit usage of density() d <- density(x) fit <- findPeaksAndValleys(d, tol=0) print(fit) plot(d, lwd=2, main="c(x1b,x2b,x3b)") abline(v=fit$x) aroma.light/tests/normalizeFragmentLength-ex1.R0000644000175000017500000000331614136047216021426 0ustar nileshnileshlibrary("aroma.light") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Example 1: Single-enzyme fragment-length normalization of 6 arrays # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Number samples I <- 9 # Number of loci J <- 1000 # Fragment lengths fl <- seq(from=100, to=1000, length.out=J) # Simulate data points with unknown fragment lengths hasUnknownFL <- seq(from=1, to=J, by=50) fl[hasUnknownFL] <- NA_real_ # Simulate data y <- matrix(0, nrow=J, ncol=I) maxY <- 12 for (kk in 1:I) { k <- runif(n=1, min=3, max=5) mu <- function(fl) { mu <- rep(maxY, length(fl)) ok <- !is.na(fl) mu[ok] <- mu[ok] - fl[ok]^{1/k} mu } eps <- rnorm(J, mean=0, sd=1) y[,kk] <- mu(fl) + eps } # Normalize data (to a zero baseline) yN <- apply(y, MARGIN=2, FUN=function(y) { normalizeFragmentLength(y, fragmentLengths=fl, onMissing="median") }) # The correction factors rho <- y-yN print(summary(rho)) # The correction for units with unknown fragment lengths # equals the median correction factor of all other units print(summary(rho[hasUnknownFL,])) # Plot raw data layout(matrix(1:9, ncol=3)) xlim <- c(0,max(fl, na.rm=TRUE)) ylim <- c(0,max(y, na.rm=TRUE)) xlab <- "Fragment length" ylab <- expression(log2(theta)) for (kk in 1:I) { plot(fl, y[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) ok <- (is.finite(fl) & is.finite(y[,kk])) lines(lowess(fl[ok], y[ok,kk]), col="red", lwd=2) } # Plot normalized data layout(matrix(1:9, ncol=3)) ylim <- c(-1,1)*max(y, na.rm=TRUE)/2 for (kk in 1:I) { plot(fl, yN[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) ok <- (is.finite(fl) & is.finite(y[,kk])) lines(lowess(fl[ok], yN[ok,kk]), col="blue", lwd=2) } aroma.light/tests/normalizeTumorBoost,flavors.R0000644000175000017500000000236214136047216021614 0ustar nileshnileshlibrary("aroma.light") library("R.utils") # Load data pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") data <- loadObject(pathname) # Drop loci with missing values data <- na.omit(data) attachLocally(data) pos <- position/1e6 # Call naive genotypes muN <- callNaiveGenotypes(betaN) # Genotype classes isAA <- (muN == 0) isAB <- (muN == 1/2) isBB <- (muN == 1) # Sanity checks stopifnot(all(muN[isAA] == 0)) stopifnot(all(muN[isAB] == 1/2)) stopifnot(all(muN[isBB] == 1)) # TumorBoost normalization with different flavors betaTNs <- list() for (flavor in c("v1", "v2", "v3", "v4")) { betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE, flavor=flavor) # Assert that no non-finite values are introduced stopifnot(all(is.finite(betaTN))) # Assert that nothing is flipped stopifnot(all(betaTN[isAA] < 1/2)) stopifnot(all(betaTN[isBB] > 1/2)) betaTNs[[flavor]] <- betaTN } # Plot layout(matrix(1:4, ncol=1)) par(mar=c(2.5,4,0.5,1)+0.1) ylim <- c(-0.05, 1.05) col <- rep("#999999", length(muN)) col[muN == 1/2] <- "#000000" for (flavor in names(betaTNs)) { betaTN <- betaTNs[[flavor]] ylab <- sprintf("betaTN[%s]", flavor) plot(pos, betaTN, col=col, ylim=ylim, ylab=ylab) } aroma.light/tests/normalizeTumorBoost.R0000644000175000017500000000154714136047216020147 0ustar nileshnileshlibrary("aroma.light") library("R.utils") # Load data pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") data <- loadObject(pathname) attachLocally(data) pos <- position/1e6 muN <- genotypeN layout(matrix(1:4, ncol=1)) par(mar=c(2.5,4,0.5,1)+0.1) ylim <- c(-0.05, 1.05) col <- rep("#999999", length(muN)) col[muN == 1/2] <- "#000000" # Allele B fractions for the normal sample plot(pos, betaN, col=col, ylim=ylim) # Allele B fractions for the tumor sample plot(pos, betaT, col=col, ylim=ylim) # TumorBoost w/ naive genotype calls betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE) plot(pos, betaTN, col=col, ylim=ylim) # TumorBoost w/ external multi-sample genotype calls betaTNx <- normalizeTumorBoost(betaT=betaT, betaN=betaN, muN=muN, preserveScale=FALSE) plot(pos, betaTNx, col=col, ylim=ylim) aroma.light/tests/normalizeQuantileSpline.matrix.R0000644000175000017500000000144514136047216022267 0ustar nileshnileshlibrary("aroma.light") # Simulate three samples with on average 20% missing values N <- 10000 X <- cbind(rnorm(N, mean=3, sd=1), rnorm(N, mean=4, sd=2), rgamma(N, shape=2, rate=1)) X[sample(3*N, size=0.20*3*N)] <- NA_real_ # Plot the data layout(matrix(c(1,0,2:5), ncol=2, byrow=TRUE)) xlim <- range(X, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") Xn <- normalizeQuantile(X) plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") plotXYCurve(X, Xn, xlim=xlim, main="The three normalized distributions") Xn2 <- normalizeQuantileSpline(X, xTarget=Xn[,1], spar=0.99) plotDensity(Xn2, lwd=2, xlim=xlim, main="The three normalized distributions") plotXYCurve(X, Xn2, xlim=xlim, main="The three normalized distributions") aroma.light/tests/distanceBetweenLines.R0000644000175000017500000000505414136047216020205 0ustar nileshnileshlibrary("aroma.light") for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break layout(matrix(1:4, nrow=2, ncol=2, byrow=TRUE)) ############################################################ # Lines in two-dimensions ############################################################ x <- list(a=c(1,0), b=c(1,2)) y <- list(a=c(0,2), b=c(1,1)) fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) xlim <- ylim <- c(-1,8) plot(NA, xlab="", ylab="", xlim=ylim, ylim=ylim) # Highlight the offset coordinates for both lines points(t(x$a), pch="+", col="red") text(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) points(t(y$a), pch="+", col="blue") text(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) v <- c(-1,1)*10 xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v) yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v) lines(xv, col="red") lines(yv, col="blue") points(t(fit$xs), cex=2.0, col="red") text(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) points(t(fit$yt), cex=1.5, col="blue") text(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) print(fit) ############################################################ # Lines in three-dimensions ############################################################ x <- list(a=c(0,0,0), b=c(1,1,1)) # The 'diagonal' y <- list(a=c(2,1,2), b=c(2,1,3)) # A 'fitted' line fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) xlim <- ylim <- zlim <- c(-1,3) dummy <- t(c(1,1,1))*100 # Coordinates for the lines in 3d v <- seq(-10,10, by=1) xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v, z=x$a[3]+x$b[3]*v) yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v, z=y$a[3]+y$b[3]*v) for (theta in seq(30,140,length.out=3)) { plot3d(dummy, theta=theta, phi=30, xlab="", ylab="", zlab="", xlim=ylim, ylim=ylim, zlim=zlim) # Highlight the offset coordinates for both lines points3d(t(x$a), pch="+", col="red") text3d(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) points3d(t(y$a), pch="+", col="blue") text3d(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) # Draw the lines lines3d(xv, col="red") lines3d(yv, col="blue") # Draw the two points that are closest to each other points3d(t(fit$xs), cex=2.0, col="red") text3d(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) points3d(t(fit$yt), cex=1.5, col="blue") text3d(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) # Draw the distance between the two points lines3d(rbind(fit$xs,fit$yt), col="purple", lwd=2) } print(fit) } # for (zzz in 0) rm(zzz) aroma.light/tests/backtransformPrincipalCurve.matrix.R0000644000175000017500000000667514136047216023126 0ustar nileshnileshlibrary("aroma.light") # Consider the case where K=4 measurements have been done # for the same underlying signals 'x'. The different measurements # have different systematic variation # # y_k = f(x_k) + eps_k; k = 1,...,K. # # In this example, we assume non-linear measurement functions # # f(x) = a + b*x + x^c + eps(b*x) # # where 'a' is an offset, 'b' a scale factor, and 'c' an exponential. # We also assume heteroscedastic zero-mean noise with standard # deviation proportional to the rescaled underlying signal 'x'. # # Furthermore, we assume that measurements k=2 and k=3 undergo the # same transformation, which may illustrate that the come from # the same batch. However, when *fitting* the model below we # will assume they are independent. # Transforms a <- c(2, 15, 15, 3) b <- c(2, 3, 3, 4) c <- c(1, 2, 2, 1/2) K <- length(a) # The true signal N <- 1000 x <- rexp(N) # The noise bX <- outer(b,x) E <- apply(bX, MARGIN=2, FUN=function(x) rnorm(K, mean=0, sd=0.1*x)) # The transformed signals with noise Xc <- t(sapply(c, FUN=function(c) x^c)) Y <- a + bX + Xc + E Y <- t(Y) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit principal curve # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit principal curve through Y = (y_1, y_2, ..., y_K) fit <- fitPrincipalCurve(Y) # Flip direction of 'lambda'? rho <- cor(fit$lambda, Y[,1], use="complete.obs") flip <- (rho < 0) if (flip) { fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda } L <- ncol(fit$s) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Backtransform data according to model fit # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Backtransform toward the principal curve (the "common scale") YN1 <- backtransformPrincipalCurve(Y, fit=fit) stopifnot(ncol(YN1) == K) # Backtransform toward the first dimension YN2 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1) stopifnot(ncol(YN2) == K) # Backtransform toward the last (fitted) dimension YN3 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=L) stopifnot(ncol(YN3) == K) # Backtransform toward the third dimension (dimension by dimension) # Note, this assumes that K == L. YN4 <- Y for (cc in 1:L) { YN4[,cc] <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1, dimensions=cc) } stopifnot(identical(YN4, YN2)) # Backtransform a subset toward the first dimension # Note, this assumes that K == L. YN5 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1, dimensions=2:3) stopifnot(identical(YN5, YN2[,2:3])) stopifnot(ncol(YN5) == 2) # Extract signals from measurement #2 and backtransform according # its model fit. Signals are standardized to target dimension 1. y6 <- Y[,2,drop=FALSE] yN6 <- backtransformPrincipalCurve(y6, fit=fit, dimensions=2, targetDimension=1) stopifnot(identical(yN6, YN2[,2,drop=FALSE])) stopifnot(ncol(yN6) == 1) # Extract signals from measurement #2 and backtransform according # the the model fit of measurement #3 (because we believe these # two have undergone very similar transformations. # Signals are standardized to target dimension 1. y7 <- Y[,2,drop=FALSE] yN7 <- backtransformPrincipalCurve(y7, fit=fit, dimensions=3, targetDimension=1) stopifnot(ncol(yN7) == 1) rho <- cor(yN7, yN6) print(rho) stopifnot(rho > 0.999) aroma.light/tests/fitXYCurve.matrix.R0000644000175000017500000000123514136047216017456 0ustar nileshnileshlibrary("aroma.light") # Simulate data from the model y <- a + bx + x^c + eps(bx) x <- rexp(1000) a <- c(2,15) b <- c(2,1) c <- c(1,2) bx <- outer(b,x) xc <- t(sapply(c, FUN=function(c) x^c)) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) Y <- a + bx + xc + eps Y <- t(Y) lim <- c(0,70) plot(Y, xlim=lim, ylim=lim) # Fit principal curve through a subset of (y_1, y_2) subset <- sample(nrow(Y), size=0.3*nrow(Y)) fit <- fitXYCurve(Y[subset,], bandwidth=0.2) lines(fit, col="red", lwd=2) # Backtransform (y_1, y_2) keeping y_1 unchanged YN <- backtransformXYCurve(Y, fit=fit) points(YN, col="blue") abline(a=0, b=1, col="red", lwd=2) aroma.light/tests/normalizeCurveFit.matrix.R0000644000175000017500000000712314136047216021060 0ustar nileshnileshlibrary("aroma.light") pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") rg <- read.table(pathname, header=TRUE, sep="\t") nbrOfScans <- max(rg$slide) rg <- as.list(rg) for (field in c("R", "G")) rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) rg$slide <- rg$spot <- NULL rg <- as.matrix(as.data.frame(rg)) colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) rgC <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) channelColor <- switch(channel, R="red", G="green") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsAPairs(rg[,sidx]) title(main=paste("Observed", channel)) box(col=channelColor) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The calibrated data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) plotMvsAPairs(rgC[,sidx]) title(main=paste("Calibrated", channel)) box(col=channelColor) } # for (channel ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The average calibrated data # # Note how the red signals are weaker than the green. The reason # for this can be that the scale factor in the green channel is # greater than in the red channel, but it can also be that there # is a remaining relative difference in bias between the green # and the red channel, a bias that precedes the scanning. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgCA <- rg for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCA[,sidx] <- calibrateMultiscan(rg[,sidx]) } rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2) colnames(rgCAavg) <- c("R", "G") for (channel in c("R", "G")) { sidx <- which(colnames(rg) == channel) rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE) } # Add some "fake" outliers outliers <- 1:600 rgCAavg[outliers,"G"] <- 50000 plotMvsA(rgCAavg) title(main="Average calibrated (AC)") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Weight-down outliers when normalizing weights <- rep(1, nrow(rgCAavg)) weights[outliers] <- 0.001 # Affine normalization of channels rgCANa <- normalizeAffine(rgCAavg, weights=weights) # It is always ok to rescale the affine normalized data if its # done on (R,G); not on (A,M)! However, this is only needed for # esthetic purposes. rgCANa <- rgCANa *2^1.4 plotMvsA(rgCANa) title(main="Normalized AC") # Curve-fit (lowess) normalization rgCANlw <- normalizeLowess(rgCAavg, weights=weights) plotMvsA(rgCANlw, col="orange", add=TRUE) # Curve-fit (loess) normalization rgCANl <- normalizeLoess(rgCAavg, weights=weights) plotMvsA(rgCANl, col="red", add=TRUE) # Curve-fit (robust spline) normalization rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights) plotMvsA(rgCANrs, col="blue", add=TRUE) legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19, col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine])) title(main="Normalized AC") plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE) plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE) abline(a=0, b=1, lty=2) legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19, col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") aroma.light/tests/backtransformAffine.matrix.R0000644000175000017500000000224414136047216021354 0ustar nileshnileshlibrary("aroma.light") X <- matrix(1:8, nrow=4, ncol=2) X[2,2] <- NA_integer_ print(X) # Returns a 4x2 matrix print(backtransformAffine(X, a=c(1,5))) # Returns a 4x2 matrix print(backtransformAffine(X, b=c(1,1/2))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:4,ncol=1))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:3,ncol=1))) # Returns a 4x2 matrix print(backtransformAffine(X, a=matrix(1:2,ncol=1), b=c(1,2))) # Returns a 4x1 matrix print(backtransformAffine(X, b=c(1,1/2), project=TRUE)) # If the columns of X are identical, and a identity # backtransformation is applied and projected, the # same matrix is returned. X <- matrix(1:4, nrow=4, ncol=3) Y <- backtransformAffine(X, b=c(1,1,1), project=TRUE) print(X) print(Y) stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) # If the columns of X are identical, and a identity # backtransformation is applied and projected, the # same matrix is returned. X <- matrix(1:4, nrow=4, ncol=3) X[,2] <- X[,2]*2; X[,3] <- X[,3]*3 print(X) Y <- backtransformAffine(X, b=c(1,2,3)) print(Y) Y <- backtransformAffine(X, b=c(1,2,3), project=TRUE) print(Y) stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) aroma.light/tests/sampleTuples.R0000644000175000017500000000041414136047216016557 0ustar nileshnileshlibrary("aroma.light") pairs <- sampleTuples(1:10, size=5, length=2) print(pairs) triples <- sampleTuples(1:10, size=5, length=3) print(triples) # Allow tuples with repeated elements quadruples <- sampleTuples(1:3, size=5, length=4, replace=TRUE) print(quadruples) aroma.light/tests/normalizeQuantileRank.matrix.R0000644000175000017500000000101514136047216021721 0ustar nileshnileshlibrary("aroma.light") # Simulate three samples with on average 20% missing values N <- 10000 X <- cbind(rnorm(N, mean=3, sd=1), rnorm(N, mean=4, sd=2), rgamma(N, shape=2, rate=1)) X[sample(3*N, size=0.20*3*N)] <- NA_real_ # Normalize quantiles Xn <- normalizeQuantile(X) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, Xn, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") aroma.light/tests/iwpca.matrix.R0000644000175000017500000000410614136047216016511 0ustar nileshnileshlibrary("aroma.light") for (zzz in 0) { # This example requires plot3d() in R.basic [http://www.braju.com/R/] if (!require(pkgName <- "R.basic", character.only=TRUE)) break # Simulate data from the model y <- a + bx + eps(bx) x <- rexp(1000) a <- c(2,15,3) b <- c(2,3,4) bx <- outer(b,x) eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) y <- a + bx + eps y <- t(y) # Add some outliers by permuting the dimensions for 1/10 of the observations idx <- sample(1:nrow(y), size=1/10*nrow(y)) y[idx,] <- y[idx,c(2,3,1)] # Plot the data with fitted lines at four different view points opar <- par(mar=c(1,1,1,1)+0.1) N <- 4 layout(matrix(1:N, nrow=2, byrow=TRUE)) theta <- seq(0,270,length.out=N) phi <- rep(20, length.out=N) xlim <- ylim <- zlim <- c(0,45) persp <- list() for (kk in seq_along(theta)) { # Plot the data persp[[kk]] <- plot3d(y, theta=theta[kk], phi=phi[kk], xlim=xlim, ylim=ylim, zlim=zlim) } # Weights on the observations # Example a: Equal weights w <- NULL # Example b: More weight on the outliers (uncomment to test) w <- rep(1, length(x)); w[idx] <- 0.8 # ...and show all iterations too with different colors. maxIter <- c(seq(1,20,length.out=10),Inf) col <- topo.colors(length(maxIter)) # Show the fitted value for every iteration for (ii in seq_along(maxIter)) { # Fit a line using IWPCA through data fit <- iwpca(y, w=w, maxIter=maxIter[ii], swapDirections=TRUE) ymid <- fit$xMean d0 <- apply(y, MARGIN=2, FUN=min) - ymid d1 <- apply(y, MARGIN=2, FUN=max) - ymid b <- fit$vt[1,] y0 <- -b * max(abs(d0)) y1 <- b * max(abs(d1)) yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) yline <- yline + ymid for (kk in seq_along(theta)) { # Set pane to draw in par(mfg=c((kk-1) %/% 2, (kk-1) %% 2) + 1) # Set the viewpoint of the pane options(persp.matrix=persp[[kk]]) # Get the first principal component points3d(t(ymid), col=col[ii]) lines3d(t(yline), col=col[ii]) # Highlight the last one if (ii == length(maxIter)) lines3d(t(yline), col="red", lwd=3) } } par(opar) } # for (zzz in 0) rm(zzz) aroma.light/tests/normalizeAverage.list.R0000644000175000017500000000147514136047216020356 0ustar nileshnileshlibrary("aroma.light") # Simulate ten samples of different lengths N <- 10000 X <- list() for (kk in 1:8) { rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]] size <- runif(1, min=0.3, max=1) a <- rgamma(1, shape=20, rate=10) b <- rgamma(1, shape=10, rate=10) values <- rfcn(size*N, a, b) # "Censor" values values[values < 0 | values > 8] <- NA_real_ X[[kk]] <- values } # Add 20% missing values X <- lapply(X, FUN=function(x) { x[sample(length(x), size=0.20*length(x))] <- NA_real_ x }) # Normalize quantiles Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(unlist(X), na.rm=TRUE)) # Plot the data layout(matrix(1:2, ncol=1)) xlim <- range(X, Xn, na.rm=TRUE) plotDensity(X, lwd=2, xlim=xlim, main="The original distributions") plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions") aroma.light/tests/medianPolish.matrix.R0000644000175000017500000000102714136047216020021 0ustar nileshnileshlibrary("aroma.light") # Deaths from sport parachuting; from ABC of EDA, p.224: deaths <- matrix(c(14,15,14, 7,4,7, 8,2,10, 15,9,10, 0,2,0), ncol=3, byrow=TRUE) rownames(deaths) <- c("1-24", "25-74", "75-199", "200++", "NA") colnames(deaths) <- 1973:1975 print(deaths) mp <- medianPolish(deaths) mp1 <- medpolish(deaths, trace=FALSE) print(mp) ff <- c("overall", "row", "col", "residuals") stopifnot(all.equal(mp[ff], mp1[ff])) # Validate decomposition: stopifnot(all.equal(deaths, mp$overall+outer(mp$row,mp$col,"+")+mp$resid)) aroma.light/.Rinstignore0000644000175000017500000000041514136047216015121 0ustar nileshnileshdoc/Makefile$ # Certain LaTeX files (e.g. bib, bst, sty) must be part of the build # such that they are available for R CMD check. These are excluded # from the install using .Rinstignore in the top-level directory # such as this one. doc/.*[.](bib|bst|sty)$ aroma.light/R/0000755000175000017500000000000014136047216013016 5ustar nileshnilesharoma.light/R/normalizeQuantileSpline.R0000644000175000017500000001734214136047216020026 0ustar nileshnilesh###########################################################################/** # @RdocGeneric normalizeQuantileSpline # @alias normalizeQuantileSpline.numeric # @alias normalizeQuantileSpline.matrix # @alias normalizeQuantileSpline.list # # @title "Normalizes the empirical distribution of one or more samples to a target distribution" # # \usage{ # @usage normalizeQuantileSpline,numeric # @usage normalizeQuantileSpline,matrix # @usage normalizeQuantileSpline,list # } # # \description{ # @get "title". # After normalization, all samples have the same average empirical # density distribution. # } # # \arguments{ # \item{x, X}{A single (\eqn{K=1}) @numeric @vector of length \eqn{N}, # a @numeric \eqn{NxK} @matrix, or a @list of length \eqn{K} with # @numeric @vectors, where \eqn{K} represents the number of samples # and \eqn{N} the number of data points.} # \item{w}{An optional @numeric @vector of length \eqn{N} of weights # specific to each data point.} # \item{xTarget}{The target empirical distribution as a \emph{sorted} # @numeric @vector of length \eqn{M}. # If @NULL and \code{X} is a @list, then the target distribution is # calculated as the average empirical distribution of the samples.} # \item{sortTarget}{If @TRUE, argument \code{xTarget} will be sorted, # otherwise it is assumed to be already sorted.} # \item{robust}{If @TRUE, the normalization function is # estimated robustly.} # \item{...}{Arguments passed to (@see "stats::smooth.spline" # or @see "aroma.light::robustSmoothSpline").} # } # # \value{ # Returns an object of the same type and dimensions as the input. # } # # \section{Missing values}{ # Both argument \code{X} and \code{xTarget} may contain non-finite values. # These values do not affect the estimation of the normalization function. # Missing values and other non-finite values in \code{X}, # remain in the output as is. No new missing values are introduced. # } # # @examples "../incl/normalizeQuantileSpline.matrix.Rex" # # @author "HB" # # \seealso{ # The target distribution can be calculated as the average # using @see "averageQuantile". # # Internally either # @see "aroma.light::robustSmoothSpline" (\code{robust=TRUE}) or # @see "stats::smooth.spline" (\code{robust=FALSE}) is used. # # An alternative normalization method that is also normalizing the # empirical densities of samples is @see "normalizeQuantileRank". # Contrary to this method, that method requires that all samples are # based on the exact same set of data points and it is also more likely # to over-correct in the tails of the distributions. # } # # \references{ # [1] @include "../incl/BengtssonH_etal_2008.bib.Rdoc" \cr # } # # @keyword "nonparametric" # @keyword "multivariate" # @keyword "robust" #*/########################################################################### setMethodS3("normalizeQuantileSpline", "list", function(X, w=NULL, xTarget=NULL, sortTarget=TRUE, robust=TRUE, ...) { # Argument 'xTarget': if (is.null(xTarget)) { # Get the target quantile for all channels? xTarget <- averageQuantile(X); sortTarget <- FALSE; } else if (!is.numeric(xTarget)) { throw("Argument 'xTarget' is not numeric: ", mode(xTarget)); } # Sort target distribution? if (sortTarget) { xTarget <- sort(xTarget, na.last=TRUE); } # Normalizes the data nTarget <- length(xTarget); X <- lapply(X, FUN=function(x) { normalizeQuantileSpline(x, w=w, xTarget=xTarget, sortTarget=FALSE, robust=TRUE, ...); }) X; }) setMethodS3("normalizeQuantileSpline", "matrix", function(X, w=NULL, xTarget=NULL, sortTarget=TRUE, robust=TRUE, ...) { # Argument 'xTarget': if (is.null(xTarget)) { # Get the target quantile for all channels? xTarget <- averageQuantile(X); sortTarget <- FALSE; } else if (!is.numeric(xTarget)) { throw("Argument 'xTarget' is not numeric: ", mode(xTarget)); } if (length(xTarget) != nrow(X)) { throw("Argument 'xTarget' is of different length than the number of rows in 'X': ", length(xTarget) , " != ", nrow(X)); } # Sort target distribution? if (sortTarget) { xTarget <- sort(xTarget, na.last=TRUE); } # Normalize each of the columns towards the target distribution for (cc in seq_len(ncol(X))) { X[,cc] <- normalizeQuantileSpline(X[,cc], w=w, xTarget=xTarget, sortTarget=FALSE, robust=robust, ...); } X; }) # normalizeQuantileSpline.matrix() setMethodS3("normalizeQuantileSpline", "numeric", function(x, w=NULL, xTarget, sortTarget=TRUE, robust=TRUE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - n <- length(x); # Argument 'w': if (!is.null(w)) { if (!is.numeric(w)) { throw("Argument 'w' is not numeric: ", mode(w)); } if (length(w) != n) { throw("Argument 'w' is of different length than 'x': ", length(w), " != ", n); } } # Argument 'xTarget': if (!is.numeric(xTarget)) { throw("Argument 'xTarget' is not numeric: ", mode(xTarget)); } if (length(xTarget) != n) { throw("Argument 'xTarget' is of different length than 'x': ", length(xTarget), " != ", n); } # Sort target distribution? if (sortTarget) { xTarget <- sort(xTarget, na.last=TRUE); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # A) Fit normalization function # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Sort signals (w/ weights) to be normalized o <- order(x, na.last=TRUE); xx <- x[o]; if (!is.null(w)) w <- w[o]; # Not needed anymore o <- NULL; # Keep only finite values ok <- (is.finite(xx) & is.finite(xTarget)); # Exclude data points with zero weight if (!is.null(w)) ok <- (ok & w > 0); xx <- xx[ok]; if (!is.null(w)) w <- w[ok]; xTarget <- xTarget[ok]; # Not needed anymore ok <- NULL; if (robust) { # robustSmoothSpline() does not return 'data'. fit <- robustSmoothSpline(x=xx, w=w, y=xTarget, ...); } else { # smooth.spline() returns 'data' by default. fit <- smooth.spline(x=xx, w=w, y=xTarget, keep.data=FALSE, ...); } # Not needed anymore xx <- xTarget <- NULL; # Not needed below fit[c("x", "y", "w", "yin", "call")] <- NULL; # Saves < 1MB, though. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # B) Normalize the data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ok <- is.finite(x); x[ok] <- predict(fit, x=x[ok])$y; x; }) # normalizeQuantileSpline.numeric() ############################################################################## # HISTORY: # 2013-10-08 # o SPEEDUP: Now normalizeQuantileSpline(..., sortTarget=TRUE) sorts the # target only once for lists of vectors just as done for matrices. # 2013-10-07 # o normalizeQuantileSpline() for list:s gained explicit argument 'w', # 'sortTarget' and 'robust'. # 2013-05-25 # o SPEEDUP: Removed all gc() calls. # 2008-04-14 # o Added normalizeQuantileSpline() for list:s, which works analogously as # normalizeQuantileRank() for list:s. # 2007-03-28 # o BUG FIX: Weights 'w' are now correctly ordered. # o BUG FIX: Due to an incorrect if(), TRUE & FALSE was swapped for 'robust'. # o Memory optimization; now the fitting is not keeping the data. # o Renamed argument 'sort' to 'sortTarget'. # 2007-03-22 # o Updated the Rdocs slightly. # 2007-02-05 # o Now normalizeQuantileSpline() handles NAs too. # 2007-02-04 # o Created from normalizeQuantile.numeric.R. ############################################################################## aroma.light/R/rowAverages.matrix.R0000644000175000017500000000317414136047216016736 0ustar nileshnileshsetMethodS3("rowAverages", "matrix", function(X, average=base::mean, deviance=stats::sd, df=function(x, ...) length(if(na.rm) na.omit(x) else x), na.rm=TRUE, ..., asAttributes=TRUE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (!is.matrix(X)) stop(paste("Argument 'X' is not a matrix: ", class(X)[1])); # Argument: '...', 'average', and 'deviance'. args <- list(...); args[["average"]] <- average; args[["deviance"]] <- deviance; args[["df"]] <- df; for (kk in seq_along(args)) { key <- names(args)[kk]; arg <- args[[kk]]; if (!is.null(arg) && !is.function(arg)) stop(paste("Argument '", key, "' must be a function: ", mode(arg))); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Calculate the average and the deviance # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - stats <- list(); for (kk in seq_along(args)) { key <- names(args)[kk]; arg <- args[[kk]]; stats[[key]] <- as.matrix(apply(X, MARGIN=1, FUN=arg, na.rm=na.rm)); } if (asAttributes) { attrs <- attributes(X); X <- stats[["average"]]; stats[["average"]] <- NULL; mostattributes(X) <- c(stats, attrs); X; } else { stats; } }) # rowAverages() ############################################################################ # HISTORY: # o 2004-05-17 # Recreated. Made into its own function. This is need by *many* methods. ############################################################################ aroma.light/R/normalizeDifferencesToAverage.R0000644000175000017500000000471014136047216021077 0ustar nileshnilesh###########################################################################/** # @RdocGeneric normalizeDifferencesToAverage # @alias normalizeDifferencesToAverage.list # # @title "Rescales channel vectors to get the same average" # # \description{ # @get "title". # } # # \usage{ # @usage normalizeDifferencesToAverage,list # } # # \arguments{ # \item{x}{A @numeric @list of length K.} # \item{baseline}{An @integer in [1,K] specifying which channel should be # the baseline. The baseline channel will be almost unchanged. # If @NULL, the channels will be shifted towards median of them all.} # \item{FUN}{A @function for calculating the average of one channel.} # \item{...}{Additional arguments passed to the \code{avg} @function.} # } # # \value{ # Returns a normalized @list of length K. # } # # @examples "../incl/normalizeDifferencesToAverage.Rex" # # @author "HB" #*/########################################################################### setMethodS3("normalizeDifferencesToAverage", "list", function(x, baseline=1, FUN=median, ...) { # Argument 'x': if (!is.list(x)) { throw("Argument 'x' is not a list: ", class(x)[1]); } # Number dimensions ndim <- length(x); # Argument 'baseline': if (!is.null(baseline)) { baseline <- as.integer(baseline); if (baseline < 1 && baseline > ndim) { throw(sprintf("Argument 'baseline' is out of range [1,%d]: %d", ndim, baseline)); } } # Calculate the overall average level for each dimension mus <- sapply(x, FUN=function(y) { y <- y[is.finite(y)]; FUN(y); }); # Estimate the overall target level if (is.null(baseline)) { targetMu <- mus[baseline]; } else { targetMu <- median(mus, na.rm=TRUE); } # Calculate the required overall shifts per dimension deltas <- mus - targetMu; # Shift all dimensions so that all have the same overall average xN <- mapply(x, as.list(deltas), FUN=function(y, dy) { y <- y - dy; list(y); }); # Return estimated parameters fit <- list(mus=mus, baseline=baseline, targetMu=targetMu, deltas=deltas); attr(xN, "fit") <- fit; xN; }) ############################################################################ # HISTORY: # 2010-04-04 # o Made the code independent of R.utils::Arguments. # 2009-09-30 # o Created from the source of an aroma.tcga vignette from May 2009. ############################################################################ aroma.light/R/plotMvsAPairs.R0000644000175000017500000000500114136047216015701 0ustar nileshnilesh#########################################################################/** # @RdocGeneric plotMvsAPairs # @alias plotMvsAPairs.matrix # # @title "Plot log-ratios/log-intensities for all unique pairs of data vectors" # # \description{ # @get "title". # } # # \usage{ # @usage plotMvsAPairs,matrix # } # # \arguments{ # \item{X}{NxK @matrix where N is the number of observations and # K is the number of channels.} # \item{Alab,Mlab}{Labels on the x and y axes.} # \item{Alim,Mlim}{Plot range on the A and M axes.} # \item{pch}{Plot symbol used.} # \item{...}{Additional arguments accepted by @see "graphics::points".} # \item{add}{If @TRUE, data points are plotted in the current plot, # otherwise a new plot is created.} # } # # \details{ # Log-ratios and log-intensities are calculated for each neighboring pair # of channels (columns) and plotted. Thus, in total there will be K-1 # data set plotted. # # The colors used for the plotted pairs are 1, 2, and so on. To change # the colors, use a different color palette. # } # # \value{ # Returns nothing. # } # # @author "HB" #*/######################################################################### setMethodS3("plotMvsAPairs", "matrix", function(X, Alab="A", Mlab="M", Alim=c(0,16), Mlim=c(-1,1)*diff(Alim), pch=".", ..., add=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X' if (ncol(X) < 2) throw("Argument 'X' must have at least two columns: ", ncol(X)); if (!add) { plot(NA, xlab=Alab, ylab=Mlab, xlim=Alim, ylim=Mlim); } nbrOfChannels <- ncol(X); # Do not plot (or generate false) M vs A for non-positive signals. X[X <= 0] <- NA; col <- 1; for (ii in 1:(nbrOfChannels-1)) { Xii <- as.double(X[,ii]); for (jj in (ii+1):nbrOfChannels) { Xjj <- as.double(X[,jj]); M <- log(Xii/Xjj, base=2); A <- log(Xii*Xjj, base=2)/2; points(A,M, pch=pch, col=col, ...); col <- col + 1; } } }) # plotMvsAPairs() ############################################################################ # HISTORY: # 2005-09-06 # o Coercing to doubles to avoid overflow when multiplying to integers. # o Now non-positive signals are excluded. # 2005-06-03 # o Now using arguments 'Alab' and 'Mlab' instead of 'xlab' and 'ylab'. # Same for 'Alim' and 'Mlim'. # 2005-04-07 # o Created Rdoc comments. ############################################################################# aroma.light/R/fitIWPCA.R0000644000175000017500000003550214136047216014514 0ustar nileshnilesh########################################################################/** # @RdocGeneric fitIWPCA # @alias fitIWPCA.matrix # # @title "Robust fit of linear subspace through multidimensional data" # # \description{ # @get "title". # } # # \usage{ # @usage fitIWPCA,matrix # } # # \arguments{ # \item{X}{NxK @matrix where N is the number of observations and # K is the number of dimensions (channels). # } # # \item{constraint}{A @character string or a @numeric value. # If @character it specifies which additional constraint to be used # to specify the offset parameters along the fitted line; # # If \code{"diagonal"}, the offset vector will be a point on the line # that is closest to the diagonal line (1,...,1). # With this constraint, all bias parameters are identifiable. # # If \code{"baseline"} (requires argument \code{baselineChannel}), the # estimates are such that of the bias and scale parameters of the # baseline channel is 0 and 1, respectively. # With this constraint, all bias parameters are identifiable. # # If \code{"max"}, the offset vector will the point on the line that is # as "great" as possible, but still such that each of its components is # less than the corresponding minimal signal. This will guarantee that # no negative signals are created in the backward transformation. # If @numeric value, the offset vector will the point on the line # such that after applying the backward transformation there are # \code{constraint*N}. Note that \code{constraint==0} corresponds # approximately to \code{constraint=="max"}. # With the latter two constraints, the bias parameters are only # identifiable modulo the fitted line. # } # # \item{baselineChannel}{Index of channel toward which all other # channels are conform. # This argument is required if \code{constraint=="baseline"}. # This argument is optional if \code{constraint=="diagonal"} and # then the scale factor of the baseline channel will be one. The # estimate of the bias parameters is not affected in this case. # Defaults to one, if missing. # } # # \item{...}{Additional arguments accepted by @see "iwpca". # For instance, a N @vector of weights for each observation may be # given, otherwise they get the same weight. # } # # \item{aShift, Xmin}{For internal use only.} # } # # \value{ # Returns a @list that contains estimated parameters and algorithm # details; # # \item{a}{A @double @vector \eqn{(a[1],...,a[K])}with offset # parameter estimates. # It is made identifiable according to argument \code{constraint}. # } # \item{b}{A @double @vector \eqn{(b[1],...,b[K])}with scale # parameter estimates. It is made identifiable by constraining # \code{b[baselineChannel] == 1}. # These estimates are independent of argument \code{constraint}. # } # \item{adiag}{If identifiability constraint \code{"diagonal"}, # a @double @vector \eqn{(adiag[1],...,adiag[K])}, where # \eqn{adiag[1] = adiag[2] = ... adiag[K]}, specifying the point # on the diagonal line that is closest to the fitted line, # otherwise the zero vector. # } # \item{eigen}{A KxK @matrix with columns of eigenvectors. # } # \item{converged}{@TRUE if the algorithm converged, otherwise @FALSE. # } # \item{nbrOfIterations}{The number of iterations for the algorithm # to converge, or zero if it did not converge. # } # # \item{t0}{Internal parameter estimates, which contains no more # information than the above listed elements. # } # \item{t}{Always @NULL.} # } # # \details{ # This method uses re-weighted principal component analysis (IWPCA) # to fit a the model \eqn{y_n = a + bx_n + eps_n} where \eqn{y_n}, # \eqn{a}, \eqn{b}, and \eqn{eps_n} are vector of the K and \eqn{x_n} # is a scalar. # # The algorithm is: # For iteration i: # 1) Fit a line \eqn{L} through the data close using weighted PCA # with weights \eqn{\{w_n\}}. Let # \eqn{r_n = \{r_{n,1},...,r_{n,K}\}} # be the \eqn{K} principal components. # 2) Update the weights as # \eqn{w_n <- 1 / \sum_{2}^{K} (r_{n,k} + \epsilon_r)} # where we have used the residuals of all but the first principal # component. # 3) Find the point a on \eqn{L} that is closest to the # line \eqn{D=(1,1,...,1)}. Similarly, denote the point on D that is # closest to \eqn{L} by \eqn{t=a*(1,1,...,1)}. # } # # @author # # %examples "fitMultiIWPCA.matrix.Rex" # # \seealso{ # This is an internal method used by the @see "calibrateMultiscan" # and @see "normalizeAffine" methods. # Internally the function @see "iwpca" is used to fit a line # through the data cloud and the function @see "distanceBetweenLines" to # find the closest point to the diagonal (1,1,...,1). # } # # @keyword "algebra" #*/######################################################################## setMethodS3("fitIWPCA", "matrix", function(X, constraint=c("diagonal", "baseline", "max"), baselineChannel=NULL, ..., aShift=rep(0, times=ncol(X)), Xmin=NULL) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 0. Define local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - statistic <- function(X, ..., constraint="diagonal", Xmin=NULL, baselineChannel=1, aShift=rep(0, times=ncol(X))) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit an K-dimensional line through the data using iterative # re-weighted PCA. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fit <- iwpca(X, ...); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get the fitted line L # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get the center of the fitted line... ax <- fit$xMean; names(ax) <- NULL; # ...and the fitted eigenvectors (u1,u2,...,uK) # with ui*uj = 0; i!=j and ui*ui = 1. U <- t(fit$vt); colnames(U) <- rownames(U) <- NULL; # The fitted scale parameters b=(b[1],b[2],...,b[K]) where # the elements are rescaled such that b[1] == 1. # [ min(b[i]) == 1. Before it was such that b[1] == 1, but this # is probably better. ] # [ Indeed not; this is not good if one do more than one estimate per # array, e.g. printtip etc. /HB 2004-04-26 ] # [ With the introduction of 'baselineChannel' it is possible to # specify which channel should get scale one. / HB 2004-06-30 ] U1 <- U[,1]; bx <- as.vector(U1/U1[baselineChannel]); # Shift the data. # [ This is for instance useful if fitting towards the diagonal line # and resampling under H0: y_i = alpha + z_i and /HB 2004-01-02 ] ax <- ax + aShift; if (identical(constraint, "diagonal")) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the point t on the fitted line that is closest to the # points s on the "diagonal" line (1,1,...,1) in K-space. # [This works also for lines in two dimension.] # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # x(s) is the fitted line (the first IWPCA component) # y(t) is the diagonal line ay <- rep(0, times=length(ax)); # (0,0,...,0) by <- rep(1, times=length(ay)); # (1,1,...,1) dbl <- distanceBetweenLines(ax=ax,bx=bx, ay=ay,by=by); a <- as.vector(dbl$xs); adiag <- as.vector(dbl$yt); } else if (identical(constraint, "baseline")) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the point t on the fitted line for which the bias parameter of # the baseline channel is zero, i.e. for which a[baselineChannel]==0. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # The scale parameters are already such that b[baselineChannel]==1. # y[c,i] = a[c] + b[c]*x[c,i] ; c = 1,...,C # y[b,i] = a[b] + x[b,i] ; b - baseline channel # Similar to the constraint=="max" reasoning: # For channel b, find t such that # ax[b] + bx[b]*t == 0 <=> { bx[b]==1 } <=> t = -ax[b] # => a[c] <- ax[c] + bx[c]*t <=> a[c] <- ax[c] - bx[c]*ax[b] a <- ax - bx*ax[baselineChannel]; adiag <- rep(0, times=length(ax)); } else if (identical(constraint, "max")) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the "greatest" point t on the fitted line that is within # the cube C whose upper limits are defined by the minimum value # in each channel. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the minimal value of each X component. if (is.null(Xmin)) Xmin <- colMins(X, na.rm=TRUE); # For each component k, find the value t such that # ax[k] + bx[k]*t[k] == Xmin[k] <=> t[k] == (Xmin[k] - ax[k])/bx[k] t <- (Xmin-ax)/bx; # Choose minimum t[k] # Now, amax <<= Xmin if amax <- ax - bx[k]*min(t) where <<= (\prec) # means componentswise less or equal than. a <- ax + bx*min(t); adiag <- rep(0, times=length(ax)); } else if (is.numeric(constraint)) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the "greatest" point t on the fitted line that is within # the cube C whose upper limits are defined by the alpha quantile # value in each channel. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find the alpha quantile value of each X component. if (is.null(Xmin)) { Xmin <- colQuantiles(X, probs=constraint, na.rm=TRUE); } # For each component k, find the value t such that # ax[k] + bx[k]*t[k] == Xmin[k] <=> t[k] == (Xmin[k] - ax[k])/bx[k] t <- (Xmin-ax)/bx; # Choose minimum t[k] # Now, amax <<= Xmin if amax <- ax - bx[k]*min(t) where <<= (\prec) # means componentswise less or equal than. a <- ax + bx*min(t); adiag <- rep(0, times=length(ax)); } # Return the statistic t <- c(a=a); t <- c(t, b=bx); t <- c(t, adiag=adiag); t <- c(t, U=as.vector(U)); t <- c(t, niter=fit$nbrOfIterations * (fit$converged*2-1)); t; } # statistic() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (!is.matrix(X)) stop("Argument 'X' must be a matrix:", mode(X)); N <- nrow(X); K <- ncol(X); if (K == 1) { stop("Argument 'X' must have two or more columns:", K); } if (N < K) { stop("Argument 'X' must have at least as many rows as columns:", N, "<", K); } # Argument: 'constraint' if (is.numeric(constraint)) { if (length(constraint) != 1) stop("Argument 'constraint' can not be a numerical vector."); if (constraint < 0 || constraint > 1) stop("Invalid value of argument 'constraint':", constraint); } else { constraint <- match.arg(constraint); if (identical(constraint, "baseline")) { if (is.null(baselineChannel)) { stop("Argument 'baselineChannel' must be given if 'constraint' is \"baseline\"."); } } } # Argument: 'baselineChannel' if (!is.null(baselineChannel)) { if (!is.numeric(baselineChannel) || length(baselineChannel) != 1) { stop("Argument 'baselineChannel' must be a single numeric: ", baselineChannel); } if (baselineChannel < 1 || baselineChannel > ncol(X)) { stop("Argument 'baselineChannel' is out of range [1,", ncol(X),"]: ", baselineChannel); } if (!(constraint %in% c("baseline", "diagonal"))) { stop("Argument 'baselineChannel' must not be specified if argument 'constraint' is \"baseline\" or \"diagonal\": ", constraint); } if (!is.null(Xmin)) { stop("Argument 'Xmin' must not be specified if 'baselineChannel' is specified: ", paste(Xmin, collapse=", ")); } } else { baselineChannel <- 1; } # Argument: 'aShift' if (is.null(aShift)) { aShift <- rep(0, times=ncol(X)); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Prepare the data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (identical(constraint, "max")) { Xmin <- colMins(X, na.rm=TRUE); } else { Xmin <- NULL; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Fit the model # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Use only finite observations isFinite <- apply(X, MARGIN=1L, FUN=function(r) all(is.finite(r))); # Number of finite observations N <- sum(isFinite); # Validate the number of finite observations if (N < K) { stop("Argument 'X' must have at least as many non-finite observations (rows) as columns:", N, "<", K); } t0 <- statistic(X[isFinite,], constraint=constraint, Xmin=Xmin, baselineChannel=baselineChannel, aShift=aShift, ...); t <- NULL; # Extract the parameter estimates from the internal estimation vector. a <- t0[regexpr("^a[0-9]*$", names(t0)) != -1]; b <- t0[regexpr("^b[0-9]*$", names(t0)) != -1]; adiag <- t0[regexpr("^adiag[0-9]*$", names(t0)) != -1]; U <- t0[regexpr("^U[0-9]*$", names(t0)) != -1]; U <- matrix(U, nrow=sqrt(length(U))); niter <- as.integer(abs(t0["niter"])); converged <- (niter > 0); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 5. Return the parameter estimates # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - list(a=a, b=b, adiag=adiag, eigen=U, converged=converged, nbrOfIterations=niter, t0=t0, t=t); }) # fitIWPCA() ########################################################################### # HISTORY: # 2013-09-26 # o Now utilizing colMins() and colQuantiles() of 'matrixStats'. # 2011-02-05 # o DOCUMENTATION: Fixed broken links to help for iwpca(). # 2006-01-22 # o Added Rdoc help on the returned parameters. # o If missing, 'baselineChannel' is now set to one before calling the # internal function. # o Now fitIWPCA() does not return the data matrix. This is to save memory. # The calling algorithm can equally well add the data if it is needed. # 2004-06-30 # o Added argument 'baselineChannel' with 'constraint' "baseline". This # is useful for instance when normalizing toward a common reference. In # such cases, the common-reference channel is unaffected and only the # other channel(s) is affinely transformed. # 2004-06-28 # o BUG FIX: Forgot to exclude non-finite observations when fitting the # iwpca(). This was done before, but somehow it disappeared while # re-organizing the code. ########################################################################### aroma.light/R/normalizeAffine.R0000644000175000017500000002252114136047216016254 0ustar nileshnilesh#########################################################################/** # @RdocGeneric normalizeAffine # @alias normalizeAffine.matrix # # \encoding{latin1} # # @title "Weighted affine normalization between channels and arrays" # # \description{ # @get "title". # # This method will remove curvature in the M vs A plots that are # due to an affine transformation of the data. In other words, if there # are (small or large) biases in the different (red or green) channels, # biases that can be equal too, you will get curvature in the M vs A plots # and this type of curvature will be removed by this normalization method. # # Moreover, if you normalize all slides at once, this method will also # bring the signals on the same scale such that the log-ratios for # different slides are comparable. Thus, do not normalize the scale of # the log-ratios between slides afterward. # # It is recommended to normalize as many slides as possible in one run. # The result is that if creating log-ratios between any channels and any # slides, they will contain as little curvature as possible. # # Furthermore, since the relative scale between any two channels on any # two slides will be one if one normalizes all slides (and channels) at # once it is possible to add or multiply with the \emph{same} constant # to all channels/arrays without introducing curvature. Thus, it is # easy to rescale the data afterwards as demonstrated in the example. # } # # \usage{ # @usage normalizeAffine,matrix # } # # \arguments{ # \item{X}{An NxK @matrix (K>=2) where the columns represent the channels, # to be normalized.} # \item{weights}{If @NULL, non-weighted normalization is done. # If data-point weights are used, this should be a @vector of length # N of data point weights used when estimating the normalization # function. # } # \item{typeOfWeights}{A @character string specifying the type of # weights given in argument \code{weights}. # } # \item{method}{A @character string specifying how the estimates are # robustified. See @see "iwpca" for all accepted values.} # \item{constraint}{Constraint making the bias parameters identifiable. # See @see "fitIWPCA" for more details.} # \item{satSignal}{Signals equal to or above this threshold will not # be used in the fitting.} # \item{...}{Other arguments passed to @see "fitIWPCA" and in # turn @see "iwpca". For example, the weight argument # of @see "iwpca". See also below.} # \item{.fitOnly}{If @TRUE, the data will not be back-transform.} # } # # \value{ # A NxK @matrix of the normalized channels. # The fitted model is returned as attribute \code{modelFit}. # } # # \section{Negative, non-positive, and saturated values}{ # Affine normalization applies equally well to negative values. Thus, # contrary to normalization methods applied to log-ratios, such as curve-fit # normalization methods, affine normalization, will not set these to @NA. # # Data points that are saturated in one or more channels are not used # to estimate the normalization function, but they are normalized. # } # # \section{Missing values}{ # The estimation of the affine normalization function will only be made # based on complete non-saturated observations, i.e. observations that # contains no @NA values nor saturated values as defined by \code{satSignal}. # } # # \section{Weighted normalization}{ # Each data point/observation, that is, each row in \code{X}, which is a # vector of length K, can be assigned a weight in [0,1] specifying how much # it should \emph{affect the fitting of the affine normalization function}. # Weights are given by argument \code{weights}, # which should be a @numeric @vector of length N. Regardless of weights, # all data points are \emph{normalized} based on the fitted normalization # function. # } # # \section{Robustness}{ # By default, the model fit of affine normalization is done in \eqn{L_1} # (\code{method="L1"}). This way, outliers affect the parameter estimates # less than ordinary least-square methods. # # For further robustness, downweight outliers such as saturated signals, # if possible. # # We do not use Tukey's biweight function for reasons similar to those # outlined in @see "calibrateMultiscan". # } # # \section{Using known/previously estimated channel offsets}{ # If the channel offsets can be assumed to be known, then it is # possible to fit the affine model with no (zero) offset, which # formally is a linear (proportional) model, by specifying # argument \code{center=FALSE}. # In order to do this, the channel offsets have to be subtracted # from the signals manually before normalizing, e.g. # \code{Xa <- t(t(X)-a)} where \code{e} is @vector of length # \code{ncol(X)}. Then normalize by # \code{Xn <- normalizeAffine(Xa, center=FALSE)}. # You can assert that the model is fitted without offset by # \code{stopifnot(all(attr(Xn, "modelFit")$adiag == 0))}. # } # # \details{ # A line is fitted robustly through the \eqn{(y_R,y_G)} observations # using an iterated re-weighted principal component analysis (IWPCA), # which minimized the residuals that are orthogonal to the fitted line. # Each observation is down-weighted by the inverse of the absolute # residuals, i.e. the fit is done in \eqn{L_1}. # } # # @author # # \references{ # [1] @include "../incl/BengtssonHossjer_2006.bib.Rdoc" \cr # } # # @examples "../incl/normalizeCurveFit.matrix.Rex" # # \seealso{ # @see "calibrateMultiscan". # } #*/######################################################################### setMethodS3("normalizeAffine", "matrix", function(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1", constraint=0.05, satSignal=2^16-1, ..., .fitOnly=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (ncol(X) < 2) stop("Affine normalization requires at least two channels: ", ncol(X)); if (nrow(X) < 3) stop("Affine normalization requires at least three observations: ", nrow(X)); # Argument: 'satSignal' if (satSignal < 0) stop("Argument 'satSignal' is negative: ", satSignal); # Argument: 'typeOfWeights' typeOfWeights <- match.arg(typeOfWeights); # Argument: 'weights' datapointWeights <- NULL; if (!is.null(weights)) { # If 'weights' is an object of a class with as.double(), cast it. weights <- as.double(weights); if (anyMissing(weights)) stop("Argument 'weights' must not contain NA values."); if (any(weights < 0 | weights > 1)) { stop("Argument 'weights' out of range [0,1]: ", paste(weights[weights < 0.0 | weights > 1.0], collapse=", ")); } weights <- as.vector(weights); if (length(weights) == 1) { weights <- rep(weights, length.out=nrow(X)); } else if (length(weights) != nrow(X)) { stop("Argument 'weights' does not have the same length as the number of data points (rows9 in the matrix: ", length(weights), " != ", nrow(X)); } datapointWeights <- weights; } # Argument: 'method' # Validate by fitIWPCA() -> iwpca() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Prepare data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Use only non-saturated observations to estimate the normalization # function (non-finite values are taken care of by fitIWPCA()). isSaturated <- (is.finite(X) & X >= satSignal); Xsat <- X[isSaturated]; X[isSaturated] <- NA; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Fit the model # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fit <- fitIWPCA(X, w=datapointWeights, method=method, constraint=constraint, ...); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. Backtransform # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - X[isSaturated] <- Xsat; # Not needed anymore isSaturated <- Xsat <- NULL; if (.fitOnly == FALSE) { X <- backtransformAffine(X, a=fit); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 5. Return the backtransformed data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - attr(X, "modelFit") <- fit; X; }) # normalizeAffine() ############################################################################ # HISTORY: # 2013-09-26 # o Now utilizing anyMissing(). # 2011-02-05 # o DOCUMENTATION: Added section on how to normalize when channel offsets # are supposed to be known/zero. # o DOCUMENTATION: Fixed broken links to help for iwpca(). # 2005-06-03 # o Added argument 'typeOfWeights' to make it similar to other normalization # methods, although only "datapoint" weights are allowed. # 2005-02-27 # o Passes argument 'methods' to fitIWPCA() now. # 2005-02-02 # o BUG FIX: isSaturated could contain NA. # 2005-02-01 # o Added argument '.fitOnly'. # o Added validation of argument 'weights'. # 2005-01-24 # o Added argument 'weights' (instead of passing 'w' to fitIWPCA()). # o Now, saturated functions are normalized, but just not used when # estimating the normalization function. # 2005-01-23 # o Updated the Rdoc comments and error messages. # 2004-12-28 # o Added Rdoc comments on weights. # 2004-06-28 # o BUG FIX: Missing braces in Rdoc comments. ############################################################################ aroma.light/R/backtransformPrincipalCurve.R0000644000175000017500000001517214136047216020652 0ustar nileshnilesh#########################################################################/** # @RdocGeneric backtransformPrincipalCurve # @alias backtransformPrincipalCurve.numeric # @alias backtransformPrincipalCurve.matrix # # @title "Reverse transformation of principal-curve fit" # # \description{ # @get "title". # } # # \usage{ # @usage backtransformPrincipalCurve,matrix # @usage backtransformPrincipalCurve,numeric # } # # \arguments{ # \item{X}{An NxK @matrix containing data to be backtransformed.} # \item{fit}{An MxL principal-curve fit object of class # \code{principal_curve} as returned by @see "fitPrincipalCurve". # Typically \eqn{L = K}, but not always. # } # \item{dimensions}{An (optional) subset of of D dimensions all in [1,L] # to be returned (and backtransform).} # \item{targetDimension}{An (optional) index specifying the dimension # in [1,L] to be used as the target dimension of the \code{fit}. # More details below.} # \item{...}{Passed internally to @see "stats::smooth.spline".} # } # # \value{ # The backtransformed NxK (or NxD) @matrix. # } # # \details{ # Each column in X ("dimension") is backtransformed independently # of the others. # } # # \section{Target dimension}{ # By default, the backtransform is such that afterward the signals are # approximately proportional to the (first) principal curve as fitted # by @see "fitPrincipalCurve". This scale and origin of this # principal curve is not uniquely defined. # If \code{targetDimension} is specified, then the backtransformed signals # are approximately proportional to the signals of the target dimension, # and the signals in the target dimension are unchanged. # } # # \section{Subsetting dimensions}{ # Argument \code{dimensions} can be used to backtransform a subset of # dimensions (K) based on a subset of the fitted dimensions (L). # If \eqn{K = L}, then both \code{X} and \code{fit} is subsetted. # If \eqn{K <> L}, then it is assumed that \code{X} is already # subsetted/expanded and only \code{fit} is subsetted. # } # # @examples "../incl/backtransformPrincipalCurve.matrix.Rex" # # \seealso{ # @see "fitPrincipalCurve" # } #*/######################################################################### setMethodS3("backtransformPrincipalCurve", "matrix", function(X, fit, dimensions=NULL, targetDimension=NULL, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X' if (!is.numeric(X)) { stop("Argument 'X' is not numeric: ", mode(X)); } dimnamesX <- dimnames(X); dimX <- dim(X); K <- dimX[2]; if (!is.matrix(X)) { X <- as.matrix(X); } # Argument 'fit' if (!inherits(fit, "principal_curve")) { stop("Argument 'fit' is not a principal_curve object: ", class(fit)[1]); } # Argument 'dimensions' dimS <- dim(fit$s); L <- dimS[2]; if (!is.null(dimensions)) { dimensions <- as.integer(dimensions); if (any(dimensions < 1 | dimensions > L)) { stop("Argument 'dimensions' contains values out of range [1,", L, "]"); } } # Argument 'targetDimension': if (!is.null(targetDimension)) { targetDimension <- as.integer(targetDimension); if (length(targetDimension) != 1L) { stop("Argument 'targetDimension' should be a scalar or NULL."); } if (targetDimension < 1L | targetDimension > L) { stop("Argument 'targetDimension' is out of range [1,", L, "]: ", targetDimension); } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Transform towards a target dimension? # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - hasTargetDimension <- (!is.null(targetDimension)); if (hasTargetDimension) { lambda <- fit$s[,targetDimension]; } else { lambda <- fit$lambda; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subset dimensions? # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - s <- fit$s; if (!is.null(dimensions)) { s <- s[,dimensions,drop=FALSE]; if (K == L) { X <- X[,dimensions,drop=FALSE]; dimX <- dim(X); dimnamesX <- dimnames(X); } dimS <- dim(s); L <- dimS[2]; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Find backtransformations and backtransform data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - naValue <- NA; mode(naValue) <- mode(X); Xhat <- matrix(naValue, nrow=dimX[1], ncol=dimX[2]); okLambda <- is.finite(lambda); for (kk in seq_len(L)) { sKK <- s[,kk]; ok <- (is.finite(sKK) & okLambda); fitKK <- smooth.spline(sKK[ok], lambda[ok], ...); Xkk <- X[,kk]; keep <- which(is.finite(Xkk)); Xkk <- Xkk[keep]; XhatKK <- predict(fitKK, x=Xkk)$y; # Sanity check stopifnot(length(XhatKK) == length(keep)); Xhat[keep,kk] <- XhatKK; } # Not needed anymore sKK <- lambda <- fitKK <- XhatKK <- keep <- s <- NULL; dim(Xhat) <- dimX; dimnames(Xhat) <- dimnamesX; Xhat; }) # backtransformPrincipalCurve() setMethodS3("backtransformPrincipalCurve", "numeric", function(X, ...) { X <- as.matrix(X); backtransformPrincipalCurve(X, ...); }) ########################################################################### # HISTORY: # 2013-04-18 # o BUG FIX: backtransformPrincipalCurve() gave an error if the # pricipal curve was fitted using data with missing values. # Now backtransformPrincipalCurve() preserves dimension names. # 2009-05-29 # o BUG FIX: Previous bug fix in backtransformPrincipalCurve() regarding # argument 'dimension' broke the initial purpose of this argument. Since # both use cases are still of interest, how the subsetting is done is now # based on whether the number of dimensions of the input data and the # model fit match or not. See help(backtransformPrincipalCurve.matrix). # Added several cases to the example code for testing this. # o Added more Rdoc comments. # 2009-05-12 # o BUG FIX: backtransformPrincipalCurve(..., dimensions) did not subset # the 'X' matrix. Also, the method now returns a matrix of the same # number of columns requested. The Rd example now illustrates this. # Thanks to Pierre Neuvial, UC Berkeley for the troublshooting and fix. # 2009-02-08 # o An error was thrown in backtransformPrincipalCurve() if argument # 'dimensions' was specified. # o BUG FIX: # 2009-01-12 # o Updated validation of arguments such that it does not require R.utils. # 2008-10-08 # o Added argument 'targetDimension' to backtransformPrincipalCurve(). # 2008-10-07 # o Created. ########################################################################### aroma.light/R/callNaiveGenotypes.R0000644000175000017500000001573114136047216016744 0ustar nileshnilesh###########################################################################/** # @RdocGeneric callNaiveGenotypes # @alias callNaiveGenotypes.numeric # # @title "Calls genotypes in a normal sample" # # \description{ # @get "title". # } # # \usage{ # @usage callNaiveGenotypes,numeric # } # # \arguments{ # \item{y}{A @numeric @vector of length J containing allele B fractions # for a normal sample.} # \item{cn}{An optional @numeric @vector of length J specifying the true # total copy number in \eqn{\{0,1,2,NA\}} at each locus. This can be # used to specify which loci are diploid and which are not, e.g. # autosomal and sex chromosome copy numbers.} # \item{...}{Additional arguments passed to @see "fitNaiveGenotypes".} # \item{modelFit}{A optional model fit as returned # by @see "fitNaiveGenotypes".} # \item{verbose}{A @logical or a @see "R.utils::Verbose" object.} # } # # \value{ # Returns a @numeric @vector of length J containing the genotype calls # in allele B fraction space, that is, in [0,1] where 1/2 corresponds # to a heterozygous call, and 0 and 1 corresponds to homozygous A # and B, respectively. # Non called genotypes have value @NA. # } # # @examples "../incl/callNaiveGenotypes.Rex" # # \section{Missing and non-finite values}{ # A missing value always gives a missing (@NA) genotype call. # Negative infinity (-@Inf) always gives genotype call 0. # Positive infinity (+@Inf) always gives genotype call 1. # } # # @author # # \seealso{ # Internally @see "fitNaiveGenotypes" is used to identify the thresholds. # } #*/########################################################################### setMethodS3("callNaiveGenotypes", "numeric", function(y, cn=rep(2L, times=length(y)), ..., modelFit=NULL, verbose=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'y': J <- length(y); y <- as.double(y); # Argument 'cn': cn <- as.integer(cn); if (length(cn) == 1L) { cn <- rep(cn, times=J); } else if (length(cn) != J) { stop("The length of argument 'cn' does not match 'y': ", length(cn), " != ", J); } uniqueCNs <- sort(unique(cn)); unknown <- which(!is.element(uniqueCNs, c(0,1,2,NA))); if (length(unknown) > 0L) { unknown <- paste(uniqueCNs[unknown], collapse=", "); stop("Argument 'cn' contains unknown CN levels: ", unknown); } # Argument 'modelFit': if (!is.null(modelFit)) { if (!inherits(modelFit, "NaiveGenotypeModelFit")) { throw("Argument 'modelFit' is not of class NaiveGenotypeModelFit: ", class(modelFit)[1]); } } # Argument 'verbose': verbose <- Arguments$getVerbose(verbose); if (verbose) { pushState(verbose); on.exit(popState(verbose)); } verbose && enter(verbose, "Calling genotypes from allele B fractions (BAFs)"); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit naive genotype model? # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.null(modelFit)) { verbose && enter(verbose, "Fitting naive genotype model"); modelFit <- fitNaiveGenotypes(y=y, cn=cn, ..., verbose=verbose); verbose && print(verbose, modelFit); verbose && exit(verbose); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Call genotypes # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - mu <- rep(NA_real_, times=J); # To please R CMD check type <- NULL; rm(list="type"); # Fitted CNs cns <- sapply(modelFit, FUN=function(fit) fit$cn); for (kk in seq_along(uniqueCNs)) { cnKK <- uniqueCNs[kk]; verbose && enter(verbose, sprintf("Copy number level #%d (C=%g) of %d", kk, cnKK, length(uniqueCNs))); # Special case if (cnKK == 0) { verbose && cat(verbose, "TCN=0 => BAF not defined. Skipping."); verbose && exit(verbose); next; } keep <- which(cn == cnKK); yKK <- y[keep]; idx <- which(cnKK == cns); if (length(idx) != 1L) { msg <- sprintf("Cannot call genotypes for %d loci with true total copy number %d, because the naive genotype model was not fit for such copy numbers. Skipping.", length(yKK), cnKK); verbose && cat(verbose, msg); verbose && exit(verbose); next; } fitKK <- modelFit[[idx]]; verbose && cat(verbose, "Model fit:"); verbose && print(verbose, fitKK); tau <- fitKK$tau; if (is.null(tau)) { # Backward compatibility fitValleys <- fitKK$fitValleys; verbose && cat(verbose, "Local minimas (\"valleys\") in BAF:"); verbose && print(verbose, fitValleys); tau <- fitValleys$x; # Not needed anymore fitValleys <- NULL; } verbose && printf(verbose, "Genotype threshholds [%d]: %s\n", length(tau), hpaste(tau)); # Call genotypes muKK <- rep(NA_real_, times=length(yKK)); if (cnKK == 1) { verbose && cat(verbose, "TCN=1 => BAF in {0,1}."); a <- tau[1]; verbose && printf(verbose, "Call regions: A = (-Inf,%.3f], B = (%.3f,+Inf)\n", a, a); muKK[yKK <= a] <- 0; muKK[a < yKK] <- 1; } else if (cnKK == 2) { verbose && cat(verbose, "TCN=2 => BAF in {0,1/2,1}."); a <- tau[1]; b <- tau[2]; verbose && printf(verbose, "Call regions: AA = (-Inf,%.3f], AB = (%.3f,%.3f], BB = (%.3f,+Inf)\n", a, a, b, b); muKK[yKK <= a] <- 0; muKK[a < yKK & yKK <= b] <- 1/2; muKK[b < yKK] <- 1; } else { verbose && printf(verbose, "TCN=%d => Skipping.\n", cnKK); } mu[keep] <- muKK; verbose && exit(verbose); } # for (kk ...) # Sanity check stopifnot(length(mu) == J); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Return genotype calls (and parameter estimates) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - attr(mu, "modelFit") <- modelFit; verbose && exit(verbose); mu; }) # callNaiveGenotypes() ########################################################################### # HISTORY: # 2012-04-16 # o CLEANUP: Dropped argument 'flavor' of callNaiveGenotypes(); it is # now passed to fitNaiveGenotypes() via '...'. # o GENERALIZATION: Now callNaiveGenotypes() no longer relies on 'modelFit' # to hold a 'fitValleys' element, but rather a 'tau' element. # 2010-10-14 # o TYPO FIX: Used name 'fitPeaks' instead of 'fitValleys'. # 2010-10-07 # o Now callNaiveGenotypes() utilizes fitNaiveGenotypes(). # o Added more detailed verbose to callNaiveGenotypes(). # 2010-07-23 # o Now callNaiveGenotypes() returns the model estimates as attribute # 'modelFit'. # 2010-04-04 # o Updated code such that R.utils::Verbose is optional. # o Corrected an Rdoc tag typo. # 2009-11-03 # o Added an example() to the Rd help of callNaiveGenotypes(). # 2009-07-08 # o BUG FIX: Was never tested. Now tested via example(normalizeTumorBoost). # 2009-07-06 # o Created from aroma.cn test script. ########################################################################### aroma.light/R/linearAlgebra.R0000644000175000017500000000136314136047216015674 0ustar nileshnilesh# The trace of matrix X tr <- function(X, na.rm=FALSE) { sum(diag(X), na.rm=na.rm); } # u and v are column vectors. scalarProduct <- function(u,v, na.rm=FALSE) { colSums(u*v, na.rm=na.rm); } # u are column vectors, v a single vector for now. projectUontoV <- function(u,v, na.rm=FALSE) { vN <- v / sqrt(sum(v^2, na.rm=na.rm)); sp <- scalarProduct(u,vN, na.rm=na.rm); sp <- rep(sp, each=length(vN)); sp <- matrix(sp, nrow=length(vN)); v * sp; } ############################################################################ # HISTORY: # 2005-01-24 # o Added arg. na.rm=FALSE to tr(), scalarProduct(), and projectUontoV(). # 2003-11-06 # o Created for readability. ############################################################################ aroma.light/R/000.R0000644000175000017500000000034614136047216013443 0ustar nileshnilesh## covr: skip=all ## Look for existing generic functions also in imported namespaces. ## This will affect whether setGenericS3() creates a generic function ## or not. options("R.methodsS3:checkImports:setGenericS3"=TRUE) aroma.light/R/normalizeFragmentLength.R0000644000175000017500000003456114136047216020000 0ustar nileshnilesh###########################################################################/** # @RdocDefault normalizeFragmentLength # # @title "Normalizes signals for PCR fragment-length effects" # # \description{ # @get "title". Some or all signals are used to estimated the # normalization function. All signals are normalized. # } # # @synopsis # # \arguments{ # \item{y}{A @numeric @vector of length K of signals to be normalized # across E enzymes.} # \item{fragmentLengths}{An @integer KxE @matrix of fragment lengths.} # \item{targetFcns}{An optional @list of E @functions; one per enzyme. # If @NULL, the data is normalized to have constant fragment-length # effects (all equal to zero on the log-scale).} # \item{subsetToFit}{The subset of data points used to fit the # normalization function. # If @NULL, all data points are considered.} # \item{onMissing}{Specifies how data points for which there is no # fragment length is normalized. # If \code{"ignore"}, the values are not modified. # If \code{"median"}, the values are updated to have the same # robust average as the other data points. # } # \item{.isLogged}{A @logical.} # \item{...}{Additional arguments passed to @see "stats::lowess".} # \item{.returnFit}{A @logical.} # } # # \value{ # Returns a @numeric @vector of the normalized signals. # } # # \section{Multi-enzyme normalization}{ # It is assumed that the fragment-length effects from multiple enzymes # added (with equal weights) on the intensity scale. # The fragment-length effects are fitted for each enzyme separately based # on units that are exclusively for that enzyme. # \emph{If there are no or very such units for an enzyme, the assumptions # of the model are not met and the fit will fail with an error.} # Then, from the above single-enzyme fits the average effect across # enzymes is the calculated for each unit that is on multiple enzymes. # } # # \section{Target functions}{ # It is possible to specify custom target function effects for each # enzyme via argument \code{targetFcns}. This argument has to be a # @list containing one @function per enzyme and ordered in the same # order as the enzyme are in the columns of argument # \code{fragmentLengths}. # For instance, if one wish to normalize the signals such that their # mean signal as a function of fragment length effect is constantly # equal to 2200 (or the intensity scale), the use # \code{targetFcns=function(fl, ...) log2(2200)} which completely # ignores fragment-length argument 'fl' and always returns a # constant. # If two enzymes are used, then use # \code{targetFcns=rep(list(function(fl, ...) log2(2200)), 2)}. # # Note, if \code{targetFcns} is @NULL, this corresponds to # \code{targetFcns=rep(list(function(fl, ...) 0), ncol(fragmentLengths))}. # # Alternatively, if one wants to only apply minimal corrections to # the signals, then one can normalize toward target functions that # correspond to the fragment-length effect of the average array. # } # # \examples{ # @include "../incl/normalizeFragmentLength-ex1.Rex" # # @include "../incl/normalizeFragmentLength-ex2.Rex" # } # # @author "HB" # # \references{ # [1] @include "../incl/BengtssonH_etal_2008.bib.Rdoc" \cr # } # # @keyword "nonparametric" # @keyword "robust" #*/########################################################################### setMethodS3("normalizeFragmentLength", "default", function(y, fragmentLengths, targetFcns=NULL, subsetToFit=NULL, onMissing=c("ignore", "median"), .isLogged=TRUE, ..., .returnFit=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'y': y <- as.double(y); nbrOfDataPoints <- length(y); okY <- is.finite(y); # Sanity check if (!any(okY, na.rm=TRUE)) { throw("Cannot fit normalization function to enzyme, because there are no (finite) data points in argument 'y'."); } # Argument 'fragmentLengths': if (!is.matrix(fragmentLengths)) { if (is.vector(fragmentLengths)) { fragmentLengths <- as.matrix(fragmentLengths); } else { throw("Argument 'fragmentLengths' must be a matrix: ", class(fragmentLengths)[[1]]); } } if (nrow(fragmentLengths) != nbrOfDataPoints) { throw("Number of rows in argument 'fragmentLengths' does not match the length of argument 'y': ", nrow(fragmentLengths), " != ", nbrOfDataPoints); } nbrOfEnzymes <- ncol(fragmentLengths); allEnzymes <- seq_len(nbrOfEnzymes); # Coerce to doubles for (ee in allEnzymes) { fragmentLengths[,ee] <- as.double(fragmentLengths[,ee]); } # Assert that there are some finite fragment lengths hasFL <- is.finite(fragmentLengths); if (!any(hasFL)) { throw("Cannot fit normalization function. Argument 'fragmentLengths' contains no finite values."); } # Assert that for each enzyme there exist some finite fragment lengths for (ee in allEnzymes) { if (sum(hasFL[,ee]) == 0) { throw(sprintf("Cannot fit normalization function to enzyme #%d, because there are no units with finite fragment lengths for this enzyme: ", ee)); } } # Count the number of enzymes per units countFL <- rep(0L, times=nbrOfDataPoints); for (ee in allEnzymes) { countFL <- countFL + as.integer(hasFL[,ee]); } # Assert that there are units from a single enzyme isSingleEnzymed <- (countFL == 1L); if (sum(isSingleEnzymed) == 0) { throw("Cannot fit normalization function, because none of the units are on fragments from a single enzyme, or equivalently, there exist no rows in argument 'fragmentLenghts' that only have one finite value."); } # Argument 'targetFcns': if (!is.null(targetFcns)) { if (!is.list(targetFcns)) { if (nbrOfEnzymes == 1L) { targetFcns <- list(targetFcns); } else { throw("Argument 'targetFcns' is not a list: ", class(targetFcns)[1]); } } if (length(targetFcns) != nbrOfEnzymes) { throw("Number of elements in 'targetFcns' does not match the number of columns in 'fragmentLengths': ", length(targetFcns), " != ", nbrOfEnzymes); } # Validate each element for (ee in allEnzymes) { if (!is.function(targetFcns[[ee]])) { throw("One element in 'targetFcns' is not a function: ", class(targetFcns[[ee]])[1]); } } } # Argument 'subsetToFit': if (!is.null(subsetToFit)) { subsetToFit <- as.integer(subsetToFit); if (length(subsetToFit) > nbrOfDataPoints) { throw("The length of argument 'subsetToFit' does not match the number of data points: ", length(subsetToFit), " != ", nbrOfDataPoints); } } # Argument 'onMissing': onMissing <- match.arg(onMissing); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Estimate normalization function and predict the signals # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit smooth curve to each enzyme separately # KxE matrix for sample (and target predictions) mu <- matrix(NA_real_, nrow=nbrOfDataPoints, ncol=nbrOfEnzymes); if (!is.null(targetFcns)) { muT <- matrix(NA_real_, nrow=nbrOfDataPoints, ncol=nbrOfEnzymes); } if (.returnFit) { fits <- vector("list", nbrOfEnzymes); } for (ee in allEnzymes) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # (a) Fit normalization function # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # (i) Fit only to units that are on fragments from a single (this) # enzyme and that there exist finite fragment lengths ok <- isSingleEnzymed & hasFL[,ee]; if (!any(ok)) { throw(sprintf("Cannot fit normalization function to enzyme #%d, because there are no units in argument 'fragmentLengths' that are unique to this enzyme and with finite fragment lengths: ", ee)); } # (ii) Fit only to units with non-missing data points. ok <- ok & okY; # Sanity check if (!any(ok)) { throw(sprintf("Cannot fit normalization function to enzyme #%d, because there are no units in argument 'fragmentLengths' that are unique to this enzyme and with finite fragment lengths and at the same time have finite values in argument 'y': ", ee)); } if (!is.null(subsetToFit)) { ok[-subsetToFit] <- FALSE; # Sanity check if (!any(ok)) { throw(sprintf("Cannot fit normalization function to enzyme #%d, because after subsetting there are no units in argument 'fragmentLengths' that are unique to this enzyme and with finite fragment lengths and at the same time have finite values in argument 'y': ", ee)); } } # All fragment lengths for current enzyme fl <- fragmentLengths[,ee]; # Fit finite {(lambda, log2theta)_j} to data points j on current enzyme suppressWarnings({ fit <- lowess(fl[ok], y[ok], ...); class(fit) <- "lowess"; }) # Not needed anymore ok <- NULL; if (.returnFit) fits[[ee]] <- fit; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # (b) Calculate correction factor # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate the correction factor for every data point on this enzyme ok <- (hasFL[,ee] & okY); mu[ok,ee] <- predict(fit, newdata=fl[ok]); if (.returnFit) { fits[[ee]] <- list(fit=fit, mu=mu[,ee]); } # Normalize toward a target function? if (!is.null(targetFcns)) { muT[ok,ee] <- targetFcns[[ee]](fl[ok]); if (.returnFit) { fits[[ee]]$muT <- muT[,ee]; } } # Not needed anymore fit <- fl <- NULL; } # for (ee ...) # Not needed anymore hasFL <- isSingleEnzymed <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate the *average* predicted signal across enzymes # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Sum on the non-log scale. if (.isLogged) { mu <- 2^mu; if (!is.null(targetFcns)) muT <- 2^muT; } mu <- rowSums(mu, na.rm=TRUE); # mu <- mu / countFL; # Averaging (needed?!?) if (!is.null(targetFcns)) { muT <- rowSums(muT, na.rm=TRUE); # muT <- muT / countFL; # Averaging (needed?!?) } # Special case: Units with unknown fragment lengths if (onMissing != "ignore") { isMissing <- (countFL == 0); if (any(isMissing)) { if (onMissing == "median") { # Let the predicted value for these units be the robust average # of all other units (based on the assumption that the missing # fragment lengths are distributed as the known ones). # Identify the set to be used to estimate the target average ok <- (okY & !isMissing); # Sanity check if (!any(ok)) { throw("Cannot fit normalization function to loci with unknown fragment lengths, because there are no (finite) data points to be fitted."); } if (!is.null(subsetToFit)) { ok[-subsetToFit] <- FALSE; # Sanity check if (!any(ok)) { throw("Cannot fit normalization function to loci with unknown fragment lengths, because after subsetting there are no (finite) data points to be fitted."); } } # Substitute the predicted means with the median of the already # predicted set of loci. mu[isMissing] <- median(mu[ok], na.rm=TRUE); if (!is.null(targetFcns)) { muT[isMissing] <- median(muT[ok], na.rm=TRUE); } # Not needed anymore ok <- NULL; } # if (onMissing == "median") } # Not needed anymore isMissing <- NULL; } # Not needed anymore countFL <- NULL; if (.isLogged) { mu <- log2(mu); if (!is.null(targetFcns)) muT <- log2(muT); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate the correction ("normalization") factor # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate correction factors if (is.null(targetFcns)) { dy <- mu; } else { dy <- (mu - muT); } # Not needed anymore mu <- NULL; if (!is.null(targetFcns)) { # Not needed anymore muT <- NULL; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize signals # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Transform signals ok <- is.finite(dy) & okY; # Not needed anymore okY <- NULL; y[ok] <- y[ok] - dy[ok]; if (.returnFit) { attr(y, "modelFit") <- fits; } y; }, private=TRUE) ############################################################################ # HISTORY: # 2010-09-18 # o ROBUSTNESS: Now normalizeFragmentLength() asserts that arguments # 'fragmentLengths' and 'y' contain at least some finite values and # specifies the same number of units. In addition, the method also # gives more informative error messages in case it cannot fit the # normalization function due to non-finite values. # 2008-09-11 # o Now onMissing="median" estimates the median on using the subset to fit. # 2008-09-10 # o Added argument 'onMissing' to normalizeFragmentLength() for specifying # how to normalize (if at all) data points for which the fragment lengths # are unknown. For backward compatibility, we start of by having it # "ignore" by default. # 2008-05-10 # o BUG FIX: If the 'subsetToFit' was shorter than the number of data # points, an exception was thrown. The test was supposed to be assert # that the subset was not greater than the number of data points. # 2008-04-14 # o Removed any usage of R.utils::Arguments. # 2007-11-29 # o BUG FIX: The implemented multi-enzyme model was not the one in mind; # The correction for the multi-enzyme data points was not right. # Have now created an updated example that displays the normalized # log-ratios (as a function of fragment length as well as they densities). # The example does also test the case for non-aliquot mixing proportions # between enzymes. This is actually automagically corrected for by the # way the model was set up, i.e. there is no need to estimate the # mixing proportions. # 2007-11-19 # o Added Rdoc examples. From these simulation examples, it looks like the # multi-enzyme normalization method works. # o Updated normalizeFragmentLength() to handle multiple enzymes. # 2006-11-28 # o Created. ############################################################################ aroma.light/R/iwpca.R0000644000175000017500000002223714136047216014252 0ustar nileshnilesh#########################################################################/** # @RdocGeneric iwpca # @alias iwpca.matrix # # @title "Fits an R-dimensional hyperplane using iterative re-weighted PCA" # # \description{ # @get "title". # } # # \usage{ # @usage iwpca,matrix # } # # \arguments{ # \item{X}{N-times-K @matrix where N is the number of observations and # K is the number of dimensions.} # \item{w}{An N @vector of weights for each row (observation) in # the data matrix. If @NULL, all observations get the same weight.} # \item{R}{Number of principal components to fit. By default a line # is fitted.} # \item{method}{ # If \code{"symmetric"} (or \code{"bisquare"}), Tukey's biweight # is used. If \code{"tricube"}, the tricube weight is used. # If \code{"L1"}, the model is fitted in \eqn{L_1}. # If a @function, it is used to calculate weights for next iteration # based on the current iteration's residuals.} # \item{maxIter}{Maximum number of iterations.} # \item{acc}{The (Euclidean) distance between two subsequent parameters # fit for which the algorithm is considered to have converged.} # \item{reps}{Small value to be added to the residuals before the # the weights are calculated based on their inverse. This is to avoid # infinite weights.} # \item{fit0}{A @list containing elements \code{vt} and \code{pc} # specifying an initial fit. # If @NULL, the initial guess will be equal to the (weighted) PCA fit.} # \item{...}{Additional arguments accepted by @see "wpca".} # } # # \value{ # Returns the fit (a @list) from the last call to @see "wpca" # with the additional elements \code{nbrOfIterations} and # \code{converged}. # } # # \details{ # This method uses weighted principal component analysis (WPCA) to fit a # R-dimensional hyperplane through the data with initial internal # weights all equal. # At each iteration the internal weights are recalculated based on # the "residuals". # If \code{method=="L1"}, the internal weights are 1 / sum(abs(r) + reps). # This is the same as \code{method=function(r) 1/sum(abs(r)+reps)}. # The "residuals" are orthogonal Euclidean distance of the principal # components R,R+1,...,K. # In each iteration before doing WPCA, the internal weighted are # multiplied by the weights given by argument \code{w}, if specified. # } # # @author # # @examples "../incl/iwpca.matrix.Rex" # # \seealso{ # Internally @see "wpca" is used for calculating the weighted PCA. # } # # @keyword "algebra" #*/######################################################################### setMethodS3("iwpca", "matrix", function(X, w=NULL, R=1, method=c("symmetric", "bisquare", "tricube", "L1"), maxIter=30, acc=1e-4, reps=0.02, fit0=NULL, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'w' if (!is.null(w)) w <- rep(w, length.out=nrow(X)); w0 <- w; # Argument: 'method' if (is.function(method)) { dummy <- method(1:5); if (!is.numeric(dummy)) stop("Argument 'method' (weight function) does not return numeric values."); if (!is.vector(dummy)) stop("Argument 'method' (weight function) does not return a vector."); if (length(dummy) != 5) stop("Argument 'method' (weight function) does not return the correct number of values."); } else { method <- match.arg(method); } # Argument: 'fit0' if (!is.null(fit0)) { if (!all(c("vt", "pc") %in% names(fit0))) { throw("Argument 'fit0' is missing element 'vt' or 'pc': ", paste(names(fit0), collapse=", ")); } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Fit the model # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Ulast <- 1/.Machine$double.eps; # A large number iter <- 0; isConverged <- FALSE; w <- rep(1, length=nrow(X)); while (!isConverged && iter < maxIter) { if (iter > 0 || is.null(fit0)) { iter <- iter + 1; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # "Re-weight the weights" # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!is.null(w0)) w <- w0 * w; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Fit N-dimensional weighted PCA. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fit <- wpca(X, w=w, scale=FALSE, ...); } else { fit <- fit0; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get the fitted line L # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get fitted eigenvectors (u1,u2,...,uN) # with ui*uj = 0; i!=j and ui*ui = 1. U <- fit$vt; colnames(U) <- rownames(U) <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Check for convergence # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - isConverged <- (sum(abs(U-Ulast))/length(U) < acc); Ulast <- U; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Finally, update the weights # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Residuals in the "tailing" dimensions. r <- fit$pc[,-c(1:R), drop=FALSE]; # Residuals in orthogonal Euclidean distance if (anyMissing(r)) { # Sometimes some residuals become NAs. r <- sqrt(rowSums(r^2, na.rm=TRUE)); } else { r <- sqrt(rowSums(r^2)); } # Down-weight points that are "far" away... if (is.character(method)) { if (method == "L1") { # Add small number to residuals to avoid infinite weights r <- abs(r) + reps; w <- 1/r; } else if (method %in% c("symmetric", "bisquare")) { # Add small number to residuals to avoid infinite weights r <- abs(r) + reps; r <- r/6; # Zero weights introduce NA's (for unknown reasons), therefore # with use a number very close to zero instead. w <- rep(.Machine$double.eps, times=length(r)); ii <- (r < 1); w[ii] <- (1-r[ii]^2)^2; # Not needed anymore ii <- NULL; } else if (method == "tricube") { # Add small number to residuals to avoid infinite weights r <- abs(r) + reps; r <- r/6; # Zero weights introduce NA's (for unknown reasons), therefore # with use a number very close to zero instead. w <- rep(.Machine$double.eps, times=length(r)); ii <- (r < 1); w[ii] <- (1-r[ii]^3)^3; # Not needed anymore ii <- NULL; } } else if (is.function(method)) { # Pass also the "fitted values" to the weight function. attr(r, "x") <- fit$pc[,1:R, drop=FALSE]; attr(r, "reps") <- reps; w <- method(r); # Combine w_i = ||w_{i,j}||_2 (Euclidean distance), if needed. if (is.matrix(w) && ncol(w) > 1) { w <- sqrt(rowSums(w^2)/ncol(w)); } else { w <- as.vector(w); } } # Sometimes some weights become NAs (not sure why). Set them to zero. if (anyMissing(w)) { w[is.na(w)] <- 0; } # Not needed anymore r <- NULL; } # while(...) fit$w <- w; fit$nbrOfIterations <- iter; fit$converged <- isConverged; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Return the estimated parameters # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fit; }) # iwpca() ############################################################################ # HISTORY: # 2013-09-26 # o Now utilizing anyMissing(). # 2006-04-25 # o Updated the example to first plot data from all viewpoints, then just # the lines. Faster since the lines are only fitted once and nicer. # 2005-05-03 # o Now test of argument 'fit0' checks if it is NULL. # 2005-03-28 # o Second try with initial guess; added argument 'fit0'. # 2005-02-08 # o Added "symmetric" (now default) and "tricube" too. # 2005-02-07 # o Argument 'method' is now how the weights are calculated from the # residuals, not how residuals are combined across dimensions. # Method "L2" is therefore removed, because it corresponds to wpca(). # o Added support for weight functions via argument 'method'. The function # must take a matrix of residuals as the first argument. # 2004-05-14 # o Made into a method of class matrix instead of a stand-alone function. # 2004-04-18 # o Added support for weighted IWPCA; simply by weighing the weights. # 2004-03-09 # o Now making use of our own weighted PCA method instead of the # multidim::acp() method, which is not maintained anymore. # 2003-12-29 # o Generalized the method to fit a R-dimensional hyperplane instead of # just a line, which is the default fit. # 2003-12-28 # o Added to the R.basic package. # o Created by extracted code from RGData.fitMultiIWPCA(). ############################################################################ aroma.light/R/zzz.R0000644000175000017500000000062114136047216013775 0ustar nileshnilesh## covr: skip=all # Allows conflicts. For more information, see library() and # conflicts() in [R] base. .conflicts.OK <- TRUE .onLoad <- function(libname, pkgname) { ## covr: skip=3 ns <- getNamespace(pkgname); pkg <- Package(pkgname); assign(pkgname, pkg, envir=ns); } .onAttach <- function(libname, pkgname) { pkg <- get(pkgname, envir=getNamespace(pkgname)); startupMessage(pkg); } aroma.light/R/sampleCorrelations.matrix.R0000644000175000017500000000735514136047216020324 0ustar nileshnilesh#########################################################################/** # @RdocGeneric sampleCorrelations # @alias sampleCorrelations.matrix # # @title "Calculates the correlation for random pairs of observations" # # \description{ # @get "title". # } # # \usage{ # @usage sampleCorrelations,matrix # } # # \arguments{ # \item{X}{An NxK @matrix where N >= 2 and K >= 2.} # \item{MARGIN}{The dimension (1 or 2) in which the observations are. # If \code{MARGIN==1} (\code{==2}), each row (column) is an observation.} # \item{pairs}{If a Lx2 @matrix, the L index pairs for which the # correlations are calculated. # If @NULL, pairs of observations are sampled.} # \item{npairs}{The number of correlations to calculate.} # \item{...}{Not used.} # } # # \value{ # Returns a @double @vector of length \code{npairs}. # } # # @author "HB" # # @examples "../incl/sampleCorrelations.matrix.Rex" # # \seealso{ # @see "base::sample". # } # # \references{ # [1] A. Ploner, L. Miller, P. Hall, J. Bergh & Y. Pawitan. # \emph{Correlation test to assess low-level processing of high-density # oligonucleotide microarray data}. BMC Bioinformatics, 2005, vol 6. # } # # @keyword utilities #*/######################################################################### setMethodS3("sampleCorrelations", "matrix", function(X, MARGIN=1, pairs=NULL, npairs=max(5000, nrow(X)), ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - corFast <- function(x, y, ...) { ## .Internal() calls are no longer allowed. /HB 2012-04-16 ## # 3 == "pairwise.complete.obs" ## .Internal(cor(x, y, as.integer(3), FALSE)); cor(x=x, y=y, use="pairwise.complete.obs", method="pearson"); } # corFast() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X' if (!is.matrix(X)) throw("Argument 'X' must be a matrix: ", mode(X)); if (nrow(X) < 2) throw("Argument 'X' must have more than two rows."); if (ncol(X) < 2) throw("Argument 'X' must have more than two columns."); # Argument 'MARGIN' if (MARGIN < 1 || MARGIN > 2) throw("Argument 'MARGIN' is out of range [1,2]: ", MARGIN); # Argument 'npairs' if (npairs < 1) throw("Argument 'npairs' must be equal or greater than one: ", npairs); # Get row/column-index pairs to calculate correlations for. if (is.null(pairs)) { pairs <- sampleTuples(dim(X)[MARGIN], size=npairs, length=2); } else { npairs <- nrow(pairs); } # Are 'pairs' and 'npairs' consistent with each other? if (nrow(pairs) < npairs) { throw("The number of pairs in 'pairs' is smaller than 'npairs': ", nrow(pairs), " < ", npairs); } # Pre-create result vector to optimize speed (and memory) cors <- rep(NA_real_, times=npairs); if (MARGIN == 1) { for (kk in 1:npairs) { pair <- pairs[kk,]; x <- X[pair[1],]; y <- X[pair[2],]; cors[kk] <- corFast(x,y); } } else { for (kk in 1:npairs) { pair <- pairs[kk,]; x <- X[,pair[1]]; y <- X[,pair[2]]; cors[kk] <- corFast(x,y); } } cors; }) # sampleCorrelations() ############################################################################ # HISTORY: # 2012-04-16 # o sampleCorrelations() no longer utilizes .Internal() calls. # o Added internal corFast() to sampleCorrelations(). # 2011-04-12 # o Now using NAs of the correct storage type. # 2005-07-25 # o Added Rdoc comments with a simple example. # 2005-04-07 # o Created. ############################################################################ aroma.light/R/fitNaiveGenotypes.R0000644000175000017500000001726714136047216016621 0ustar nileshnilesh###########################################################################/** # @RdocGeneric fitNaiveGenotypes # @alias fitNaiveGenotypes.numeric # # @title "Fit naive genotype model from a normal sample" # # \description{ # @get "title". # } # # \usage{ # @usage fitNaiveGenotypes,numeric # } # # \arguments{ # \item{y}{A @numeric @vector of length J containing allele B fractions # for a normal sample.} # \item{cn}{An optional @numeric @vector of length J specifying the true # total copy number in \eqn{\{0,1,2,NA\}} at each locus. This can be # used to specify which loci are diploid and which are not, e.g. # autosomal and sex chromosome copy numbers.} # \item{subsetToFit}{An optional @integer or @logical @vector specifying # which loci should be used for estimating the model. # If @NULL, all loci are used.} # \item{flavor}{A @character string specifying the type of algorithm used.} # \item{adjust}{A positive @double specifying the amount smoothing for # the empirical density estimator.} # \item{...}{Additional arguments passed to @see "findPeaksAndValleys".} # \item{censorAt}{A @double @vector of length two specifying the range # for which values are considered finite. Values below (above) this # range are treated as -@Inf (+@Inf).} # \item{verbose}{A @logical or a @see "R.utils::Verbose" object.} # } # # \value{ # Returns a @list of @lists. # } # # @author # # \seealso{ # To call genotypes see @see "callNaiveGenotypes". # Internally @see "findPeaksAndValleys" is used to identify the thresholds. # } #*/########################################################################### setMethodS3("fitNaiveGenotypes", "numeric", function(y, cn=rep(2L, times=length(y)), subsetToFit=NULL, flavor=c("density", "fixed"), adjust=1.5, ..., censorAt=c(-0.1,1.1), verbose=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'y': J <- length(y); y <- as.double(y); # Argument 'cn': cn <- as.integer(cn); if (length(cn) == 1L) { cn <- rep(cn, times=J); } else if (length(cn) != J) { stop("The length of argument 'cn' does not match 'y': ", length(cn), " != ", J); } uniqueCNs <- sort(unique(cn)); unknown <- which(!is.element(uniqueCNs, c(0,1,2,NA))); if (length(unknown) > 0L) { unknown <- paste(uniqueCNs[unknown], collapse=", "); stop("Argument 'cn' contains unknown CN levels: ", unknown); } # Argument 'subsetToFit': if (!is.null(subsetToFit)) { if (is.logical(subsetToFit)) { if (length(subsetToFit) != J) { stop("The length of argument 'subsetToFit' does not match 'y': ", length(subsetToFit), " != ", J); } subsetToFit <- which(subsetToFit); } else { subsetToFit <- as.integer(subsetToFit); subsetToFit <- sort(unique(subsetToFit)); if (!all(1 <= subsetToFit & subsetToFit <= J)) { stop(sprintf("Some elements of argument 'subsetToFit' is out of range [1,%d].", J)); } } } # Argument 'flavor': flavor <- match.arg(flavor); # Argument 'adjust': adjust <- as.double(adjust); if (length(adjust) != 1L) { stop("Argument 'adjust' must be single value: ", adjust); } if (adjust <= 0) { stop("Argument 'adjust' must be positive: ", adjust); } ## # Argument 'tol': ## tol <- as.double(tol); ## if (length(tol) != 1) { ## stop("Argument 'tol' must be single value: ", tol); ## } ## if (tol <= 0) { ## stop("Argument 'tol' must be positive: ", tol); ## } # Argument 'censorAt': censorAt <- as.double(censorAt); stopifnot(length(censorAt) == 2L); stopifnot(censorAt[1] <= censorAt[2]); # Argument 'verbose': verbose <- Arguments$getVerbose(verbose); if (verbose) { pushState(verbose); on.exit(popState(verbose)); } verbose && enter(verbose, "Fitting naive genotype model from normal allele B fractions (BAFs)"); verbose && cat(verbose, "Flavor: ", flavor); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Adjust signals # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (verbose) { enter(verbose, "Censoring BAFs"); cat(verbose, "Before:"); summary(verbose, y); print(verbose, sum(is.finite(y))); } # Censor values y[y < censorAt[1]] <- -Inf; y[y > censorAt[2]] <- +Inf; if (verbose) { cat(verbose, "After:"); summary(verbose, y); print(verbose, sum(is.finite(y))); exit(verbose); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Subsetting # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!is.null(subsetToFit)) { if (verbose) { enter(verbose, "Subsetting"); cat(verbose, "Number of data points before: ", length(y)); cat(verbose, "Number of true copy-number levels before: ", length(uniqueCNs)); } y <- y[subsetToFit]; cn <- cn[subsetToFit]; if (verbose) { uniqueCNs <- sort(unique(cn)); cat(verbose, "Number of data points afterward: ", length(y)); cat(verbose, "Number of true copy-number levels afterward: ", length(uniqueCNs)); exit(verbose); } } # To please R CMD check type <- NULL; rm(list="type"); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Call genotypes # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fitList <- list(); for (kk in seq_along(uniqueCNs)) { cnKK <- uniqueCNs[kk]; verbose && enter(verbose, sprintf("Copy number level #%d (C=%g) of %d", kk, cnKK, length(uniqueCNs))); keep <- which(cn == cnKK); yKK <- y[keep]; # Exclude missing and non-finited values when fitting the density yT <- yKK[is.finite(yKK)]; n <- length(yT); if (flavor == "density") { fit <- findPeaksAndValleys(yT, adjust=adjust, ...); verbose && cat(verbose, "Identified extreme points in density of BAF:"); verbose && print(verbose, fit); fitValleys <- subset(fit, type == "valley"); nbrOfGenotypeGroups <- nrow(fitValleys) + 1L; verbose && cat(verbose, "Local minimas (\"valleys\") in BAF:"); verbose && print(verbose, fitValleys); tau <- fitValleys$x; } else if (flavor == "fixed") { args <- list(...); tau <- args$tau; if (is.null(tau)) { tau <- seq_len(cnKK) / (cnKK + 1L); } nbrOfGenotypeGroups <- length(tau) + 1L; } # Sanity check stopifnot(length(tau) == nbrOfGenotypeGroups - 1L); # Store fitKK <- list( flavor = flavor, cn=cnKK, nbrOfGenotypeGroups=nbrOfGenotypeGroups, # Not really used tau=tau, n=n ); if (flavor == "density") { fitKK$fit <- fit; fitKK$fitValleys <- fitValleys; } fitList[[kk]] <- fitKK; verbose && exit(verbose); } # for (kk ...) verbose && exit(verbose); class(fitList) <- c("NaiveGenotypeModelFit", class(fitList)); fitList; }) # fitNaiveGenotypes() ########################################################################### # HISTORY: # 2012-04-16 # o Added support for fitNaiveGenotypes(..., flavor="fixed"). # o GENERALIZATION: Now fitNaiveGenotypes() returns also 'flavor' and # 'tau'. The latter are the genotype threshholds used by the caller. # 2010-10-14 # o TYPO FIX: Used name 'fitPeaks' instead of 'fitValleys'. # 2010-10-12 # o New default of argument 'censorAt' for fitNaiveGenotypes(). # o BUG FIX: fitNaiveGenotypes(..., subsetToFit=) would throw # an exception reporting "Some elements of argument 'subsetToFit' is # out of range ...". # 2010-10-07 # o Created from callNaiveGenotypes.R. ########################################################################### aroma.light/R/robustSmoothSpline.R0000644000175000017500000002651214136047216017032 0ustar nileshnilesh############################################################################/** # @RdocDefault robustSmoothSpline # # @title "Robust fit of a Smoothing Spline" # # @synopsis # # \description{ # Fits a smoothing spline robustly using the \eqn{L_1} norm. Currently, the # algorithm is an \emph{iterative reweighted smooth spline} algorithm which # calls \code{smooth.spline(x,y,w,...)} at each iteration with the weights # \code{w} equal to the inverse of the absolute value of the residuals for # the last iteration step. # } # # \arguments{ # \item{x}{a @vector giving the values of the predictor variable, or a # @list or a two-column @matrix specifying \code{x} and \code{y}. # If \code{x} is of class \code{smooth.spline} then \code{x$x} is used # as the \code{x} values and \code{x$yin} are used as the \code{y} # values.} # \item{y}{responses. If \code{y} is missing, the responses are assumed to be # specified by \code{x}.} # \item{w}{a @vector of weights the same length as \code{x} giving the weights # to use for each element of \code{x}. Default value is equal weight # to all values.} # \item{...}{Other arguments passed to @see "stats::smooth.spline".} # \item{minIter}{the minimum number of iterations used to fit the smoothing # spline robustly. Default value is 3.} # \item{maxIter}{the maximum number of iterations used to fit the smoothing # spline robustly. Default value is 25.} # \item{method}{the method used to compute robustness weights at each # iteration. Default value is \code{"L1"}, which uses the inverse of # the absolute value of the residuals. Using \code{"symmetric"} will # use Tukey's biweight with cut-off equal to six times the MAD of # the residuals, equivalent to @see "stats::lowess".} # \item{sdCriteria}{Convergence criteria, which the difference between the # standard deviation of the residuals between two consecutive # iteration steps. Default value is 2e-4.} # \item{reps}{Small positive number added to residuals to avoid division by # zero when calculating new weights for next iteration.} # \item{tol}{Passed to @see "stats::smooth.spline" (R >= 2.14.0).} # \item{plotCurves}{If @TRUE, the fitted splines are added to the current # plot, otherwise not.} # } # # \value{ # Returns an object of class \code{smooth.spline}. # } # # @examples "../incl/robustSmoothSpline.Rex" # # \seealso{ # This implementation of this function was adopted from # @see "stats::smooth.spline" of the \pkg{stats} package. # Because of this, this function is also licensed under GPL v2. # } # # @author # # @keyword "smooth" # @keyword "robust" #*/############################################################################ setMethodS3("robustSmoothSpline", "default", function(x, y=NULL, w=NULL, ..., minIter=3, maxIter=max(minIter, 50), method=c("L1", "symmetric"), sdCriteria=2e-4, reps=1e-15, tol=1e-6*IQR(x), plotCurves=FALSE) { requireNamespace("stats") || throw("Package not loaded: stats"); # smooth.spline() stats.smooth.spline <- smooth.spline; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Verify arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'w' if (is.numeric(w)) { w <- as.double(w); if (anyMissing(w)) { stop("Weights with value NA are not allowed."); } if (any(w < 0 | w > 1)) { stop("Weights out of range [0,1]: ", paste(w[w < 0.0 | w > 1.0], collapse=", ")); } } else if (!is.null(w)) { stop("Argument 'w' is of an unsupported datatype/class: ", class(weights)[1]); } # Argument: 'method' method <- match.arg(method) # Argument: 'reps' if (!is.numeric(reps) || length(reps) != 1 || reps <= 0) throw("Argument 'reps' must be a single postive number."); # smooth.spline() next will only operate on unique x-values. For this reason, # we have to remove corresponding weights too. There is a small problem here; # if different weights are used for data points (x,y,w) with same x-value, which # data points (x,y,w) should be used? Here we use the first one only. /HB 2005-01-24 uIdxs <- .whichUnique(x, tol = tol); nu <- length(uIdxs); w0 <- w[uIdxs]; # WORKAROUND # We need to make sure that 'g$x == x' below. /HB 2011-10-10 x <- x[uIdxs]; y <- y[uIdxs]; w <- w[uIdxs]; uIdxs <- seq_along(x); if (inherits(x, "smooth.spline")) { g <- x; } else if (missing(w) || is.null(w)) { x <- as.vector(x); y <- as.vector(y); g <- stats.smooth.spline(x, y, ..., tol=tol); # Sanity check /HB 2011-10-10 stopifnot(length(g$x) == nu); # Not needed anymore x <- y <- NULL; } else { x <- as.vector(x); y <- as.vector(y); w <- as.vector(w); g <- stats.smooth.spline(x, y, w=w, ..., tol=tol); # Sanity check /HB 2011-10-10 stopifnot(length(g$x) == nu); # Not needed anymore x <- y <- w <- NULL; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Step 0. Initiation # # This will generate an object of class smooth.spline # containing the fields # x : the distinct `x' values in increasing order. # y : the fitted values corresponding to `x'. # yin : the y values used at the unique `y' values. # From these the residuals can be calculated as # r <- yin - y # The important is that we use these (x,yin) as our # (x,y) in the rest of the algorithm. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - sdR0 <- NA_real_; col <- 0; ready <- FALSE; iter <- 0; while (!ready & iter < maxIter) { iter <- iter + 1; # Calculate the residuals and the weights r <- (g$yin-g$y); if (method == "L1") { w <- 1/(abs(r)+reps); # Add a small constant for stability. } else if (method == "symmetric") { rmad <- mad(r); threshold <- 6 * rmad; # same as lowess(). threshold <- max(reps, threshold); # avoid instability at very low MADs. w <- (1 - pmin(1, abs(r)/threshold)^2)^2; } # If the user specified weights initially, the weights # calculated from the inverse of the residuals are themselve # weighted by the user initial weights. if (!is.null(w0)) { w <- w0*w; } sdR <- sd(r); # Not needed anymore r <- NULL; if (iter > minIter) { if (!is.na(sdR0)) { dSd <- abs(sdR0-sdR); if (dSd < sdCriteria) break; } } # Remove "bad" weights. For instance, too large w's gives: # Error in smooth.spline(g$x, g$yin, w = w, ...) : # NA/NaN/Inf in foreign function call (arg 4) ok.weights <- (w != 0 & is.finite(w)); if (!all(ok.weights)) w[!ok.weights] <- 0; # Not needed anymore ok.weights <- NULL; g <- stats.smooth.spline(g$x, g$yin, w=w, ..., tol=tol); # Not needed anymore w <- NULL; if (plotCurves == TRUE) lines(g, col=(col<-col+1)); sdR0 <- sdR; } # while ( ... ) g$iter <- iter g; }) # robustSmoothSpline() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - .whichUnique <- function(x, ..., tol) { # We need to make sure that 'g$x == x' below. /HB 2011-10-10 xx <- x; keep <- rep(TRUE, times=length(x)); while (TRUE) { idxs <- which(keep); xx <- round((x[idxs] - mean(x[idxs]))/tol); # de-mean to avoid possible overflow dups <- duplicated(xx); if (!any(dups)) { break; } keep[idxs[dups]] <- FALSE; } # while() nd <- keep; # Sanity check stopifnot(length(nd) == length(x)); which(nd); } # .whichUnique() ###################################################################### # HISTORY # 2015-01-06 # o Using requestNamespace() instead of request(). # 2014-03-25 # o CLEANUP: The internal .Fortran() calls no longer pass DUP=FALSE, # which "may be disabled in future versions of R.". # 2013-09-26 # o Now utilizing anyMissing(). # 2012-08-30 # o BUG FIX: Now local getNativeSplineFitFunction() sets up the # function such that it is called via a FortranRoutine object, # rather than by name. # 2012-08-19 # o Added local getNativeSplineFitFunction() function to # robustSmoothSpline() which returns a wrapper to a proper # native and internal spline fit function of R. # o Make it clear that robustSmoothSpline() is under GPL (>= 2), # because it is adapted from smooth.spline() of R by R Core Team. # Added a GPL source code header. # 2011-10-10 # o Updated robustSmoothSpline() such that it works with the new # "uniqueness" scheme of smooth.spline() in R v2.14.0 and newer. # It is tricky, because robustSmoothSpline() is a reiterative # algorithm which requires that the choosen "unique" x:s does # not change in each iteration. Previously, 'signif(x, 6)' was # used to identify unique x:s, which gives the same set of values # when called twice, whereas this is not true for the new choice # with 'round((x - mean(x))/tol)'. # 2011-04-12 # o Now using as.double(NA) instead of NA, which is logical. # o Interestingly, stats::smooth.spline() of R v2.14.0 now does # very similar speedups as robustSmoothSpline() has done # internally in its smooth.spline.fit() since 2002. Great. # o CLEANUP: Now robustSmoothSpline() utilizes stats:::n.knots() # internally, if running on R v2.14.0 or newer. # 2008-07-20 # o MEMORY OPTIMIZATION: Removing more variables when done etc. # Helping the garbage collector by doing x <- as.vector(x) before # calling a function rather than having as.vector(x) as an argument. # 2007-06-08 # o Added declaration 'nx <- 0' in robustSmoothSpline.matrix() in # order to please R CMD check R v2.6.0. # 2007-01-01 # o Removed any code to make method backward compatibility with # R < 1.9.0, which was before 'modreg' was merged into 'stats'. # 2005-06-03 # o Now making use of setMethodS3(). # o Renamed to robustSmoothSpline(). # o Copied from R.basic. At the same time, we speedup functions were made # into local functions. # 2005-01-24 # o Added support for weights. # 2002-04-21 # o Updated due to modreg is merged into stats from R v1.9.0. # 2002-03-02 # o SPEED UP: Since robust. smooth. spline() now makes use of # the "home-made" smooth.spline.prepare() and smooth.spline0() it # speed up about three times on my test data; 32secs -> 9secs. # o Splitted smooth.spline() into the two functions # smooth.spline.prepare() and smooth.spline.fit(). The purpose of # this is to speed up robust.spline(), especially when there are # duplicate x values! # 2002-02-19 # o The idea of using w.org is not simple since the data points are # reorder by smooth.spline. # o Made w <- as.vector(w). # 2002-02-18 # o Created the Rd comments with an example adapted from # smooth.spline. # o Made it possible to specify weights even in the robust estimation. # o Added a assertion that the weights are non-illegal and not to # big. # o Renamed to robust. smooth. spline() and made analogue to # smooth.spline(). # 2002-02-15 # o Created. It seems like the robust spline alorithm gives pretty # much the same result as lowess. If not, the differences are # quite small compared to the noise level of cDNA microarray data. ###################################################################### aroma.light/R/plotXYCurve.R0000644000175000017500000001230514136047216015406 0ustar nileshnilesh###########################################################################/** # @RdocGeneric plotXYCurve # @alias plotXYCurve.numeric # @alias plotXYCurve.matrix # # @title "Plot the relationship between two variables as a smooth curve" # # \usage{ # @usage plotXYCurve,numeric # @usage plotXYCurve,matrix # } # # \description{ # @get "title". # } # # \arguments{ # \item{x, y, X, Y}{Two @numeric @vectors of length N for one curve (K=1), # or two @numeric NxK @matrix:es for K curves.} # \item{col}{The color of each curve. # Either a scalar specifying the same value of all curves, # or a @vector of K curve-specific values.} # \item{lwd}{The line width of each curve. # Either a scalar specifying the same value of all curves, # or a @vector of K curve-specific values.} # \item{dlwd}{The width of each density curve.} # \item{dcol}{The fill color of the interior of each density curve.} # \item{xlim, ylim}{The x and y plotting limits.} # \item{xlab, ylab}{The x and y labels.} # \item{curveFit}{The @function used to fit each curve. The two first # arguments of the function must take \code{x} and \code{y}, and the # function must return a @list with fitted elements \code{x} and # \code{y}.} # \item{...}{Additional arguments passed to @see "graphics::lines" # used to draw each curve.} # \item{add}{If @TRUE, the graph is added to the current plot, otherwise # a new plot is created.} # } # # \value{ # Returns nothing. # } # # \section{Missing values}{ # Data points (x,y) with non-finite values are excluded. # } # # @author "HB" # # @keyword "nonparametric" # @keyword "multivariate" # @keyword "robust" #*/########################################################################### setMethodS3("plotXYCurve", "numeric", function(x, y, col=1L, lwd=2, dlwd=1, dcol=NA, xlim=NULL, ylim=xlim, xlab=NULL, ylab=NULL, curveFit=smooth.spline, ..., add=FALSE) { if (is.null(xlab)) xlab <- substitute(X); if (is.null(ylab)) ylab <- substitute(Y); # Exclude non-finite data points ok <- (is.finite(x) & is.finite(y)); x <- x[ok]; y <- y[ok]; # Create empty plot? if (!add) { par(mar=c(5,4,4,5)+0.1); suppressWarnings({ plot(NA, xlim=xlim, ylim=ylim, xlab="", ylab="", ..., axes=FALSE); }) cex <- par("cex.lab")*par("cex"); mtext(xlab, side=1, line=3, cex=cex, col=par("col.lab"), font=par("font.lab")); mtext(ylab, side=4, line=3, cex=cex, col=par("col.lab"), font=par("font.lab")); abline(a=0, b=1, col="gray", lty=2L); } # Estimate and draw smooth function suppressWarnings({ args <- list(x=x, y=y, ...); keep <- intersect(names(args), names(formals(smooth.spline))); args <- args[keep]; fit <- do.call(smooth.spline, args=args); lines(fit, col=col, lwd=lwd, ...); }) usr <- par("usr"); # Limit the density plot to the plot region and data range rx <- range(x); rx[1L] <- max(rx[1], usr[1L]); rx[2L] <- min(rx[2], usr[2L]); ry <- range(y); ry[1L] <- max(ry[1], usr[3L]); ry[2L] <- min(ry[2], usr[4L]); # Estimate density of x d <- density(x, from=rx[1L], to=rx[2L]); n <- length(d$y); d$y[c(1L,n)] <- 0; d$y <- d$y / max(d$y, na.rm=TRUE); dx <- d; d$y <- 1/10*(usr[4L]-usr[3L])*d$y; d$y <- usr[4L]+d$y; polygon(d, col=dcol, border=col, lwd=dlwd, xpd=TRUE); # Estimate density of y d <- density(y, from=ry[1L], to=ry[2L]); n <- length(d$y); d$y[c(1L,n)] <- 0; d$y <- d$y / max(d$y, na.rm=TRUE); dy <- d; t <- d$x; d$x <- d$y; d$y <- t; d$x <- usr[1L]-1/10*(usr[2L]-usr[1L])*d$x; polygon(d, col=dcol, border=col, lwd=dlwd, xpd=TRUE); d <- dx; t <- d$x; d$x <- d$y; d$y <- t; d$x <- usr[1L]-1/10*(usr[2L]-usr[1L])*d$x; lines(d, col="black", lwd=0.618*dlwd, lty=2, xpd=TRUE); if (!add) { axis(side=1L); axis(side=4L); box(); } invisible(fit); }) # plotXYCurve.numeric() setMethodS3("plotXYCurve", "matrix", function(X, Y, col=seq_len(nrow(X)), lwd=2, dlwd=1, dcol=NA, xlim=NULL, ylim=xlim, xlab=NULL, ylab=NULL, curveFit=smooth.spline, ..., add=FALSE) { if (identical((X), dim(Y))) { throw("Argument 'X' and 'Y' have different dimensions."); } if (is.null(xlab)) xlab <- substitute(X); if (is.null(ylab)) ylab <- substitute(Y); ncol <- ncol(X); if (is.null(col)) { col <- seq_len(ncol); } else { col <- rep(col, length.out=ncol); } if (!is.null(lwd)) lwd <- rep(lwd, length.out=ncol); if (is.null(xlim)) { xlim <- range(X, na.rm=TRUE); } if (is.null(ylim)) { ylim <- range(Y, na.rm=TRUE); } for (kk in seq_len(ncol)) { plotXYCurve(X[,kk], Y[,kk], col=col[kk], lwd=lwd, dlwd=dlwd, dcol=dcol, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, curveFit=curveFit, ..., add=add || (kk > 1L)); } box(); invisible(); }) # plotXYCurve.matrix() ############################################################################ # HISTORY: # 2013-10-08 # o BUG FIX: Argument 'lwd' of plotXYCurve(X, ...) was ignored if 'X' # was a matrix. # o DOCUMENTATION: Now there is one combined plotXYCurve() help pages # for all data types. # 2008-04-14 # o Replaced a R.utils::doCall() with a "cleanup" do.call(). # 2007-02-04 # o Created. ############################################################################ aroma.light/R/normalizeQuantileRank.R0000644000175000017500000001313614136047216017464 0ustar nileshnilesh###########################################################################/** # @RdocGeneric normalizeQuantileRank # @alias normalizeQuantileRank.numeric # @alias normalizeQuantileRank.list # @alias normalizeQuantile # @alias normalizeQuantile.default # # @title "Normalizes the empirical distribution of one of more samples to a target distribution" # # \usage{ # @usage normalizeQuantileRank,numeric # @usage normalizeQuantileRank,list # @usage normalizeQuantile,default # } # # \description{ # @get "title". # # The average sample distribution is calculated either robustly or not # by utilizing either \code{weightedMedian()} or \code{weighted.mean()}. # A weighted method is used if any of the weights are different from one. # } # # \arguments{ # \item{x, X}{a @numeric @vector of length N or a @list of length N # with @numeric @vectors. # If a @list, then the @vectors may be of different lengths.} # \item{xTarget}{The target empirical distribution as a \emph{sorted} # @numeric @vector of length \eqn{M}. # If @NULL and \code{X} is a @list, then the target distribution is # calculated as the average empirical distribution of the samples.} # \item{ties}{Should ties in \code{x} be treated with care or not? # For more details, see "limma:normalizeQuantiles".} # \item{...}{Not used.} # } # # \value{ # Returns an object of the same shape as the input argument. # } # # \section{Missing values}{ # Missing values are excluded when estimating the "common" (the baseline). # Values that are @NA remain @NA after normalization. # No new @NAs are introduced. # } # # \section{Weights}{ # Currently only channel weights are support due to the way quantile # normalization is done. # If signal weights are given, channel weights are calculated from these # by taking the mean of the signal weights in each channel. # } # # @examples "../incl/normalizeQuantileRank.list.Rex" # # \author{ # Adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 \& 2006. # Original code by Ben Bolstad at Statistics Department, University of # California. # } # # \seealso{ # To calculate a target distribution from a set of samples, see # @see "averageQuantile". # For an alternative empirical density normalization methods, see # @see "normalizeQuantileSpline". # } # # @keyword "nonparametric" # @keyword "multivariate" # @keyword "robust" #*/########################################################################### setMethodS3("normalizeQuantileRank", "list", function(X, xTarget=NULL, ...) { # Get the target quantile for all channels (columns)? if (is.null(xTarget)) xTarget <- averageQuantile(X); # Normalizes the data nTarget <- length(xTarget); X <- lapply(X, FUN=function(x) { normalizeQuantileRank(x, xTarget=xTarget, ...); }) X; }) setMethodS3("normalizeQuantileRank", "numeric", function(x, xTarget, ties=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - n <- length(x); # Argument 'xTarget': if (!is.numeric(xTarget)) { throw("Argument 'xTarget' is not numeric: ", mode(xTarget)); } nTarget <- length(xTarget); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Different length of sample and target? # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nDiff <- (nTarget - n); if (nDiff > 0L) { # Add hoc fix for differences in lengths. naValue <- NA; storage.mode(naValue) <- storage.mode(x); x <- c(x, rep(naValue, times=nDiff)); n <- n + nDiff; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # For all columns, get for each sample quantile the value of # average sample distribution at that quantile. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - quantiles <- (0:(nTarget-1))/(nTarget-1); ok <- !is.na(x); nok <- sum(ok); if(nok < n) { # Get the sample quantiles for those values if (ties) { r <- rank(x[ok]); xNew <- (r-1)/(nok-1); } else { xNew <- (0:(nok-1))/(nok-1); } # Interpolate to get the xTarget's at positions specified by # 'quantile' using data points given by 'xNew' and 'xTarget'. if (!ties) { # Order and sort the values ok <- ((1:n)[ok])[order(x[ok])]; } x[ok] <- approx(x=quantiles, y=xTarget, xout=xNew, ties="ordered")$y; if (nDiff > 0L) { x <- x[1:(n-nDiff)]; } } else { if (ties || n != nTarget) { r <- rank(x); xNew <- (r-1)/(n-1); x <- approx(x=quantiles, y=xTarget, xout=xNew, ties="ordered")$y; } else { ok <- order(x); x[ok] <- xTarget; } } x; }) setMethodS3("normalizeQuantile", "default", function(x, ...) { normalizeQuantileRank(x, ...); }) ############################################################################## # HISTORY: # 2013-10-07 # o DOCUMENTATION: Merged the documentation for normalizeQuantileRank() # for numeric vectors and lists. # 2011-04-12 # o Now using NAs of the correct storage type. # 2008-04-14 # o Renamed normalizeQuantile() to normalizeQuantileRank(). Keeping the old # name for backward compatibility. # 2006-05-21 # o Now 'x' and 'xTarget' may be of different lengths. # 2006-05-15 # o Now the method can normalize vectors of length different from 'xTarget'. # 2006-05-12 # o Created from normalizeQuantile.matrix.R. It has been optimized for # memory. Hence, the normalization is done using a two-pass procedure. ############################################################################## aroma.light/R/pairedAlleleSpecificCopyNumbers.R0000644000175000017500000001177014136047216021367 0ustar nileshnilesh###########################################################################/** # @RdocGeneric pairedAlleleSpecificCopyNumbers # @alias pairedAlleleSpecificCopyNumbers.numeric # # @title "Calculating tumor-normal paired allele-specific copy number stratified on genotypes" # # \description{ # @get "title". # The method is a single-sample (single-pair) method. # It requires paired tumor-normal parent-specific copy number signals. # } # # \usage{ # @usage pairedAlleleSpecificCopyNumbers,numeric # } # # \arguments{ # \item{thetaT, betaT}{Theta and allele-B fraction signals for the tumor.} # \item{thetaN, betaN}{Total and allele-B fraction signals for the # matched normal.} # \item{muN}{An optional @vector of length J containing # normal genotypes calls in (0,1/2,1,@NA) for (AA,AB,BB).} # \item{...}{Not used.} # } # # \value{ # Returns a @data.frame with elements \code{CT}, \code{betaT} and \code{muN}. # } # # \seealso{ # This definition of calculating tumor-normal paired ASCN is related # to how the @see "normalizeTumorBoost" method calculates normalized # tumor BAFs. # } # # @author "PN, HB" #*/########################################################################### setMethodS3("pairedAlleleSpecificCopyNumbers", "numeric", function(thetaT, betaT, thetaN, betaN, muN=callNaiveGenotypes(betaN), ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'thetaT' & 'betaT': thetaT <- as.numeric(thetaT); betaT <- as.numeric(betaT); J <- length(thetaT); if (length(betaT) != J) { stop("The length of arguments 'betaT' and 'thetaT' differ: ", length(betaT), " != ", J); } # Argument: 'thetaN' & 'betaN': thetaN <- as.numeric(thetaN); betaN <- as.numeric(betaN); if (length(thetaN) != J) { stop("The length of arguments 'thetaN' and 'thetaT' differ: ", length(thetaN), " != ", J); } if (length(betaN) != J) { stop("The length of arguments 'betaN' and 'thetaN' differ: ", length(betaN), " != ", J); } # Argument: 'muN': if (length(muN) != J) { stop("The length of arguments 'muN' and 'betaN' differ: ", length(muN), " != ", J); } knownGenotypes <- c(0, 1/2, 1, NA); unknown <- which(!is.element(muN, knownGenotypes)); n <- length(unknown); if (n > 0L) { unknown <- unique(muN[unknown]); stop("Argument 'muN' contains unknown values: ", hpaste(unknown)); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate (thetaA,thetaB) for tumor and normal (for SNP only) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # SNPs are identifies as those loci that have non-missing 'betaTN' & 'muN' isSnp <- (!is.na(betaT) & !is.na(muN)); nbrOfSnps <- sum(isSnp); thetaTs <- thetaT[isSnp] * matrix(c(1-betaT[isSnp], betaT[isSnp]), ncol=2L); stopifnot(nrow(thetaTs) == nbrOfSnps); thetaNs <- thetaN[isSnp] * matrix(c(1-betaN[isSnp], betaN[isSnp]), ncol=2L); stopifnot(nrow(thetaNs) == nbrOfSnps); muNs <- muN[isSnp]; stopifnot(length(muNs) == nbrOfSnps); isHomAs <- (muNs == 0); isHomBs <- (muNs == 1); stopifnot(length(isHomAs) == nbrOfSnps); stopifnot(length(isHomBs) == nbrOfSnps); muNs <- NULL; # Not needed anymore # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Calculate tumor (CA,CB) conditioned on genotype (for SNP only) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CTs <- thetaTs / thetaNs; CTs[isHomAs,1L] <- 2*CTs[isHomAs,1L]; CTs[isHomAs,2L] <- 0; CTs[isHomBs,1L] <- 0; CTs[isHomBs,2L] <- 2*CTs[isHomBs,2L]; thetaTs <- thetaNs <- isHomAs <- isHomBs <- NULL; # Not needed anymore # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Translate (CA,CB) to (CT,betaT) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CT <- betaT <- rep(NA_real_, times=J); # Total CN ratios CT[isSnp] <- CTs[,1L] + CTs[,2L]; CT[!isSnp] <- 2 * thetaT[!isSnp] / thetaN[!isSnp]; # Tumor BAFs betaT[isSnp] <- CTs[,2L] / CT[isSnp]; CTs <- isSnp <- NULL; # Not needed anymore # Sanity checks stopifnot(length(CT) == J); stopifnot(length(betaT) == J); stopifnot(length(muN) == J); # Return value data <- data.frame(CT=CT, betaT=betaT, muN=muN); data; }) # pairedAlleleSpecificCopyNumbers() ############################################################################## # HISTORY: # 2014-03-30 [HB in Juvisy] # o Created from PN's description just to be on the same page. PN has argued # for this way of calculating ASCN's PN since our TumorBoost days (~2009). # PN has a high-level implementation elsewhere, but HB decided to do this # from scratch to get a low-level API similar to that of TumorBoost. ############################################################################## aroma.light/R/plotDensity.R0000644000175000017500000001530314136047216015461 0ustar nileshnilesh#########################################################################/** # @RdocGeneric plotDensity # @alias plotDensity.list # @alias plotDensity.data.frame # @alias plotDensity.matrix # @alias plotDensity.numeric # @alias plotDensity.density # # @title "Plots density distributions for a set of vectors" # # \description{ # @get "title". # } # # \usage{ # @usage plotDensity,data.frame # @usage plotDensity,matrix # @usage plotDensity,numeric # @usage plotDensity,list # } # # \arguments{ # \item{X}{A single of @list of @numeric @vectors or @see "stats::density" # objects, a @numeric @matrix, or a @numeric @data.frame.} # \item{W}{(optional) weights of similar data types and dimensions as # \code{X}.} # \item{xlim,ylim}{@character @vector of length 2. The x and y limits.} # \item{xlab,ylab}{@character string for labels on x and y axis.} # \item{col}{The color(s) of the curves.} # \item{lty}{The types of curves.} # \item{lwd}{The width of curves.} # \item{...}{Additional arguments passed to @see "stats::density", # @see "graphics::plot", and @see "graphics::lines".} # \item{add}{If @TRUE, the curves are plotted in the current plot, # otherwise a new is created.} # } # # \seealso{ # Internally, @see "stats::density" is used to estimate the # empirical density. # } # # @author "HB" #*/######################################################################### setMethodS3("plotDensity", "list", function(X, W=NULL, xlim=NULL, ylim=NULL, xlab=NULL, ylab="density (integrates to one)", col=1:length(X), lty=NULL, lwd=NULL, ..., add=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': nbrOfSamples <- length(X); # Argument 'W': if (is.numeric(W)) { nX <- sapply(X, FUN=length); if (any(nX != nX[1L])) { throw("If argument 'W' is a numeric vector or matrix, then all vectors of 'X' must of identical lengths, which is not the case."); } nX <- nX[1L]; if (is.vector(W)) { nW <- length(W); if (nW != nX) { throw("Length of argument 'W' and the length of the elements of 'X' does not match: ", nW, " != ", nX); } # Coerce into a list of weights of the same number of elements as 'X' W <- rep(list(W), times=nbrOfSamples); } else if (is.matrix(W)) { nW <- nrow(W); if (nW != nX) { throw("Number of rows of argument 'W' and the length of the elements of 'X' does not match: ", nW, " != ", nX); } # Coerce into a list of weights of the same number of elements as 'X' Wx <- vector("list", length=ncol(W)); for (kk in 1:ncol(W)) Wx[[kk]] <- W[,kk,drop=TRUE]; W <- Wx; Wx <- NULL; # Not needed anymore } } # if (is.numeric(W)) if (is.list(W)) { if (length(W) != nbrOfSamples) { throw("The lists of argument 'W' and 'X' do not have the same number of elements: ", length(W), " != ", nbrOfSamples); } for (kk in 1:nbrOfSamples) { w <- W[[kk]]; nW <- length(w); nX <- length(X[[kk]]); if (nW != nX) { throw(sprintf("Element #%d of arguments 'W' and 'X' are of different lengths: %d != %d", kk, nW, nX)); } if (any(w < 0)) throw("Argument 'W' contains negative weights."); w <- nW <- nX <- NULL; # Not needed anymore } } else if (!is.null(W)) { throw("Argument 'W' must be a list, a numeric vector, or a numeric matrix: ", class(W)[1L]); } # Argument 'xlab': if (is.null(xlab)) xlab <- substitute(X); # Argument 'col': if (is.null(col)) { col <- seq_len(nbrOfSamples); } else { col <- rep(col, length.out=nbrOfSamples); } # Argument 'lty': if (!is.null(lty)) lty <- rep(lty, length.out=nbrOfSamples); # Argument 'lwd': if (!is.null(lwd)) lwd <- rep(lwd, length.out=nbrOfSamples); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Generate all densities first and figure out the plot limits. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ds <- list(); xlimDef <- c(NA_real_,NA_real_); ylimDef <- c(0,NA_real_); for(kk in 1:nbrOfSamples) { x <- X[[kk]]; if (inherits(x, "density")) { d <- x; } else { w <- W[[kk]]; if (is.null(w)) { keep <- is.finite(x); x <- x[keep]; keep <- NULL; # Not needed anymore suppressWarnings({ d <- density(x, ...); }); x <- NULL; # Not needed anymore } else { keep <- is.finite(x) & is.finite(w); x <- x[keep]; w <- w[keep]; keep <- NULL; # Not needed anymore # Standardize to sum(w) == 1 w <- w / sum(w); suppressWarnings({ d <- density(x, weights=w, ...); }); x <- w <- NULL; # Not needed anymore } } ds[[kk]] <- d; xlimDef <- range(c(xlimDef, d$x), na.rm=TRUE); ylimDef <- range(c(ylimDef, d$y), na.rm=TRUE); } # for (kk ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Plot the densities # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (is.null(xlim)) { xlim <- xlimDef; } else { for (kk in 1:2) if (is.na(xlim[kk])) xlim[kk] <- xlimDef[kk] } if (is.null(ylim)) { ylim <- ylimDef; } else { for (kk in 1:2) if (is.na(ylim[kk])) ylim[kk] <- ylimDef[kk] } if (add == FALSE) { suppressWarnings({ plot(NA, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, ...); }) } for(kk in 1:nbrOfSamples) { suppressWarnings({ lines(ds[[kk]], col=col[kk], lty=lty[kk], lwd=lwd[kk], ...); }) } invisible(ds); }) # plotDensity() setMethodS3("plotDensity", "data.frame", function(X, ..., xlab=NULL) { # Argument 'xlab': if (is.null(xlab)) xlab <- substitute(X); plotDensity(as.list(X), ..., xlab=xlab); }) setMethodS3("plotDensity", "matrix", function(X, ..., xlab=NULL) { # Argument 'xlab': if (is.null(xlab)) xlab <- substitute(X); plotDensity(as.data.frame(X), ..., xlab=xlab); }) setMethodS3("plotDensity", "numeric", function(X, ..., xlab=NULL) { # Argument 'xlab': if (is.null(xlab)) xlab <- substitute(X); plotDensity(list(X), ..., xlab=xlab); }) setMethodS3("plotDensity", "density", function(X, ..., xlab=NULL) { # Argument 'xlab': if (is.null(xlab)) xlab <- substitute(X); plotDensity(list(X), ..., xlab=xlab); }) ############################################################################## # HISTORY: # 2014-03-25 # o Now plotDensity() supports weights via argument 'W'. # o Now plotDensity() also supports density() objects. # 2006-05-12 # o Created. ############################################################################## aroma.light/R/901.CalibrationAndNormalization.R0000644000175000017500000002333014136047216021073 0ustar nileshnilesh#########################################################################/** # @RdocDocumentation "1. Calibration and Normalization" # # \encoding{latin1} # # \description{ # In this section we give \emph{our} recommendation on how spotted # two-color (or multi-color) microarray data is best calibrated and # normalized. # } # # \section{Classical background subtraction}{ # We do \emph{not} recommend background subtraction in classical # means where background is estimated by various image analysis # methods. This means that we will only consider foreground signals # in the analysis. # # We estimate "background" by other means. In what is explain below, # only a global background, that is, a global bias, is estimated # and removed. # } # # \section{Multiscan calibration}{ # In Bengtsson et al (2004) we give evidence that microarray scanners # can introduce a significant bias in data. This bias, which is # about 15-25 out of 65535, \emph{will} introduce intensity dependency # in the log-ratios, as explained in Bengtsson & # \enc{Hssjer}{Hossjer} (2006). # # In Bengtsson et al (2004) we find that this bias is stable across # arrays (and a couple of months), but further research is needed # in order to tell if this is true over a longer time period. # # To calibrate signals for scanner biases, scan the same array at # multiple PMT-settings at three or more (K >= 3) different # PMT settings (preferably in decreasing order). # While doing this, \emph{do not adjust the laser power settings}. # Also, do the multiscan \emph{without} washing, cleaning or by other # means changing the array between subsequent scans. # Although not necessary, it is preferred that the array # remains in the scanner between subsequent scans. This will simplify # the image analysis since spot identification can be made once # if images aligns perfectly. # # After image analysis, read all K scans for the same array into the # two matrices, one for the red and one for the green channel, where # the K columns corresponds to scans and the N rows to the spots. # It is enough to use foreground signals. # # In order to multiscan calibrate the data, for each channel # separately call \code{Xc <- calibrateMultiscan(X)} where \code{X} # is the NxK matrix of signals for one channel across all scans. The # calibrated signals are returned in the Nx1 matrix \code{Xc}. # # Multiscan calibration may sometimes be skipped, especially if affine # normalization is applied immediately after, but we do recommend that # every lab check at least once if their scanner introduce bias. # If the offsets in a scanner is already estimated from earlier # multiscan analyses, or known by other means, they can readily be # subtracted from the signals of each channel. If arrays are still # multiscanned, it is possible to force the calibration method to # fit the model with zero intercept (assuming the scanner offsets # have been subtracted) by adding argument \code{center=FALSE}. # } # # \section{Affine normalization}{ # In Bengtsson & \enc{Hssjer}{Hossjer} (2006), we carry out a detailed # study on how biases in each channel introduce so called # intensity-dependent log-ratios among other systematic artifacts. # Data with (additive) bias in each channel is said to be \emph{affinely} # transformed. Data without such bias, is said to be \emph{linearly} # (proportionally) transform. Ideally, observed signals (data) is a # linear (proportional) function of true gene expression levels. # # We do \emph{not} assume proportional observations. The scanner bias # is real evidence that assuming linearity is not correct. # Affine normalization corrects for affine transformation in data. # Without control spots it is not possible to estimate the bias in each # of the channels but only the relative bias such that after # normalization the effective bias are the same in all channels. # This is why we call it normalization and not calibration. # # In its simplest form, affine normalization is done by # \code{Xn <- normalizeAffine(X)} where \code{X} is a Nx2 matrix with # the first column holds the foreground signals from the red channel and # the second holds the signals from the green channel. If three- or # four-channel data is used these are added the same way. The normalized # data is returned as a Nx2 matrix \code{Xn}. # # To normalize all arrays and all channels at once, one may put all # data into one big NxK matrix where the K columns hold the all channels # from the first array, then all channels from the second array and so # on. Then \code{Xn <- normalizeAffine(X)} will return the across-array # and across-channel normalized data in the NxK matrix \code{Xn} where # the columns are stored in the same order as in matrix \code{X}. # # Equal effective bias in all channels is much better. First of all, # any intensity-dependent bias in the log-ratios is removed \emph{for # all non-differentially expressed genes}. There is still an # intensity-dependent bias in the log-ratios for differentially expressed # genes, but this is now symmetric around log-ratio zero. # # Affine normalization will (by default and recommended) normalize # \emph{all} arrays together and at once. This will guarantee that # all arrays are "on the same scale". Thus, it \emph{not} recommended # to apply a classical between-array scale normalization afterward. # Moreover, the average log-ratio will be zero after an affine # normalization. # # Note that an affine normalization will only remove curvature in the # log-ratios at lower intensities. # If a strong intensity-dependent bias at high intensities remains, # this is most likely due to saturation effects, such as too high PMT # settings or quenching. # # Note that for a perfect affine normalization you \emph{should} # expect much higher noise levels in the \emph{log-ratios} at lower # intensities than at higher. It should also be approximately # symmetric around zero log-ratio. # In other words, \emph{a strong fanning effect is a good sign}. # # Due to different noise levels in red and green channels, different # PMT settings in different channels, plus the fact that the # minimum signal is zero, "odd shapes" may be seen in the log-ratio # vs log-intensity graphs at lower intensities. Typically, these # show themselves as non-symmetric in positive and negative log-ratios. # Note that you should not see this at higher intensities. # # If there is a strong intensity-dependent effect left after the # affine normalization, we recommend, for now, that a subsequent # curve-fit or quantile normalization is done. # Which one, we do not know. # # Why negative signals? # By default, 5\% of the normalized signals will have a non-positive # signal in one or both channels. \emph{This is on purpose}, although # the exact number 5\% is chosen by experience. The reason for # introducing negative signals is that they are indeed expected. # For instance, when measure a zero gene expression level, there is # a chance that the observed value is (should be) negative due to # measurement noise. (For this reason it is possible that the scanner # manufacturers have introduced scanner bias on purpose to avoid # negative signals, which then all would be truncated to zero.) # To adjust the ratio (or number) of negative signals allowed, use # for example \code{normalizeAffine(X, constraint=0.01)} for 1\% # negative signals. If set to zero (or \code{"max"}) only as much # bias is removed such that no negative signals exist afterward. # Note that this is also true if there were negative signals on # beforehand. # # Why not lowess normalization? # Curve-fit normalization methods such as lowess normalization are # basically designed based on linearity assumptions and will for this # reason not correct for channel biases. Curve-fit normalization # methods can by definition only be applied to one pair of channels # at the time and do therefore require a subsequent between-array # scale normalization, which is by the way very ad hoc. # # Why not quantile normalization? # Affine normalization can be though of a special case of quantile # normalization that is more robust than the latter. # See Bengtsson & \enc{Hssjer}{Hossjer} (2006) for details. # Quantile normalization is probably better to apply than curve-fit # normalization methods, but less robust than affine normalization, # especially at extreme (low and high) intensities. # For this reason, we do recommend to use affine normalization first, # and if this is not satisfactory, quantile normalization may be applied. # } # # \section{Linear (proportional) normalization}{ # If the channel offsets are zero, already corrected for, or estimated # by other means, it is possible to normalize the data robustly by # fitting the above affine model without intercept, that is, fitting # a truly linear model. This is done adding argument \code{center=FALSE} # when calling \code{normalizeAffine()}. # } # # @author "HB" #*/######################################################################### ############################################################################ # HISTORY: # 2011-02-05 # o DOCUMENTATION: Added paragraphs on how to do affine normalization # when channel offsets are known/zero. Same for multiscan calibration # when scanner offsets are known/zero. # 2006-06-29 # o Added to aroma.light instead. # 2005-02-02 # o Created. ############################################################################ aroma.light/R/normalizeAverage.R0000644000175000017500000000475414136047216016446 0ustar nileshnilesh###########################################################################/** # @RdocGeneric normalizeAverage # @alias normalizeAverage.list # @alias normalizeAverage.matrix # # @title "Rescales channel vectors to get the same average" # # \description{ # @get "title". # } # # \usage{ # @usage normalizeAverage,matrix # @usage normalizeAverage,list # } # # \arguments{ # \item{x}{A @numeric NxK @matrix (or @list of length K).} # \item{baseline}{An @integer in [1,K] specifying which channel should be # the baseline.} # \item{avg}{A @function for calculating the average of one channel.} # \item{targetAvg}{The average that each channel should have afterwards. # If @NULL, the baseline column sets the target average.} # \item{...}{Additional arguments passed to the \code{avg} @function.} # } # # \value{ # Returns a normalized @numeric NxK @matrix (or @list of length K). # } # # @author "HB" #*/########################################################################### setMethodS3("normalizeAverage", "matrix", function(x, baseline=1, avg=stats::median, targetAvg=2200, ...) { # Estimate the scale for each channel scale <- apply(x, MARGIN=2, FUN=avg, ...); # The scale of the baseline column scale1 <- scale[baseline]; # Standardize so that the 'baseline' column is not rescaled (has scale one). scale <- scale / scale1; # Rescale to target averages? if (!is.null(targetAvg)) { rho <- (scale1 / targetAvg); scale <- rho * scale; } # Rescale so that all channels have the same scale for (cc in 1:ncol(x)) { x[,cc] <- x[,cc] / scale[cc]; } x; }, private=TRUE) setMethodS3("normalizeAverage", "list", function(x, baseline=1, avg=stats::median, targetAvg=2200, ...) { # Estimate the scale for each channel scale <- lapply(x, FUN=avg, ...); scale <- unlist(scale, use.names=FALSE); scale1 <- scale[baseline]; # Standardize so that the 'baseline' channel has scale one. scale <- scale / scale1; # Rescale to target averages? if (!is.null(targetAvg)) { rho <- (scale1 / targetAvg); scale <- rho * scale; } # Rescale so that all channels have the same scale for (cc in 1:length(x)) { x[[cc]] <- x[[cc]] / scale[cc]; } x; }, private=TRUE) ############################################################################ # HISTORY: # 2007-06-04 # o Corrected minor ineffective typo in code. # 2007-03-29 # o Added Rdoc comments. # 2006-05-08 # o Created. ############################################################################ aroma.light/R/print.SmoothSplineLikelihood.R0000644000175000017500000000332214136047216020724 0ustar nileshnilesh###########################################################################/** # @class SmoothSplineLikelihood # @RdocMethod print # # @title "Prints an SmoothSplineLikelihood object" # # \description{ # @get "title". A SmoothSplineLikelihood object is returned by # \code{\link{likelihood.smooth.spline}()}. # } # # \usage{ # @usage print,SmoothSplineLikelihood # } # # \arguments{ # \item{x}{Object to be printed.} # \item{digits}{Minimal number of significant digits to print.} # \item{...}{Not used.} # } # # \value{ # Returns nothing. # } # # @author "HB" # # @keyword internal #*/########################################################################### setMethodS3("print", "SmoothSplineLikelihood", function(x, digits=getOption("digits"), ...) { # To please R CMD check... object <- x; s <- paste("Likelihood of smoothing spline:", format(object, digits=digits), "\n"); base <- attr(object, "base"); s <- paste(s, "Log base:", format(base, digits=digits), "\n") wrss <- attr(object, "wrss"); s <- paste(s, "Weighted residuals sum of square:", format(wrss, digits=digits), "\n"); penalty <- attr(object, "penalty"); s <- paste(s, "Penalty:", format(penalty, digits=digits), "\n"); lambda <- attr(object, "lambda"); s <- paste(s, "Smoothing parameter lambda:", format(lambda, digits=digits), "\n"); roughness <- attr(object, "roughness"); s <- paste(s, "Roughness score:", format(roughness, digits=digits), "\n"); cat(s); invisible(object); }) ############################################################################ # HISTORY: # 2005-06-03 # o Added Rdoc comments. # o Extracted from likelihood.smooth.spline.R. ############################################################################ aroma.light/R/likelihood.smooth.spline.R0000644000175000017500000001320614136047216020067 0ustar nileshnilesh###########################################################################/** # @class smooth.spline # @RdocMethod likelihood # # @title "Calculate the log likelihood of a smoothing spline given the data" # # @synopsis # # \arguments{ # \item{object}{The smooth.spline object.} # \item{x, y}{The x and y values for which the (weighted) likelihood will # be calculated. If \code{x} is of type \code{xy.coords} any value of # argument \code{y} will be omitted. If \code{x==NULL}, the x and y values # of the smoothing spline will be used.} # \item{w}{The weights for which the (weighted) likelihood will be # calculated. If @NULL, weights equal to one are assumed.} # \item{base}{The base of the logarithm of the likelihood. If @NULL, # the non-logged likelihood is returned.} # \item{rel.tol}{The relative tolerance used in the call to # \code{integrate}.} # \item{...}{Not used.} # } # # \description{ # Calculate the (log) likelihood of a spline given the data used to fit # the spline, \eqn{g}. The likelihood consists of two main parts: # 1) (weighted) residuals sum of squares, and 2) a penalty term. The # penalty term consists of a \emph{smoothing parameter} \eqn{lambda} # and a \emph{roughness measure} of the spline # \eqn{J(g) = \int g''(t) dt}. Hence, the overall log likelihood is # \deqn{\log L(g|x) = (y-g(x))'W(y-g(x)) + \lambda J(g)} # In addition to the overall likelihood, all its separate # components are also returned. # # Note: when fitting a smooth spline with \eqn{(x,y)} values where the # \eqn{x}'s are \emph{not} unique, \code{smooth.spline} will replace # such \eqn{(x,y)}'s with a new pair \eqn{(x,y')} where \eqn{y'} is a # reweighted average on the original \eqn{y}'s. It is important to # be aware of this. In such cases, the resulting \code{smooth.spline} # object does \emph{not} contain all \eqn{(x,y)}'s and therefore this # function will not calculate the weighted residuals sum of square on # the original data set, but on the data set with unique \eqn{x}'s. # See examples below how to calculate the likelihood for the spline with # the original data. # } # # \value{ # Returns the overall (log) likelihood of class # \code{SmoothSplineLikelihood}, a class with the following attributes: # \item{wrss}{the (weighted) residual sum of square} # \item{penalty}{the penalty which is equal to \code{-lambda*roughness}.} # \item{lambda}{the smoothing parameter} # \item{roughness}{the value of the roughness functional given the # specific smoothing spline and the range of data} # } # # \details{ # The roughness penalty for the smoothing spline, \eqn{g}, fitted # from data in the interval \eqn{[a,b]} is defined as # \deqn{J(g) = \int_a^b g''(t) dt} # which is the same as # \deqn{J(g) = g'(b) - g'(a)} # The latter is calculated internally by using # @see "stats::predict.smooth.spline". # } # # @examples "../incl/likelihood.smooth.spline.Rex" # # \seealso{ # @see "stats::smooth.spline" and @see "robustSmoothSpline". # } # # @author # # @keyword smooth # @keyword internal #*/########################################################################### setMethodS3("likelihood", "smooth.spline", function(object, x=NULL, y=NULL, w=NULL, base=exp(1), rel.tol=.Machine$double.eps^(1/8), ...) { requireNamespace("stats") || throw("Package not loaded: stats") smooth.spline <- stats::smooth.spline g <- object; if (is.null(x)) { x <- g$x; y <- g$yin; w <- g$w; yg <- g$y; } else { xy <- xy.coords(x, y); if (is.element("w", names(x))) w <- x$w; x <- xy$x; y <- xy$y; if (is.null(w)) w <- rep(1, times=length(x)); yg <- NULL; ok <- (!is.na(x) & !is.na(y) & !is.na(w)); if (any(ok == FALSE)) { x <- x[ok]; y <- y[ok]; z <- z[ok]; } } # The weighted residuals sum of square if (is.null(yg)) yg <- predict(g, x)$y; wrss <- sum(w * (y-yg)^2); # The smoothing parameter lambda <- g$lambda # The roughness score J(g) = \int_a^b (g''(t))^2 dt gbis <- smooth.spline(predict(g, x, deriv=2)); ab <- range(x, na.rm=TRUE); Jg <- integrate(function(x) predict(gbis, x=x)$y, lower=ab[1], upper=ab[2], rel.tol=rel.tol, stop.on.error=FALSE)$value # The penalty term penalty <- -lambda * Jg; # The log likelihood l <- -(wrss + penalty); # Return the correct logarithm (if any) if (missing(base) || (!is.null(base) && base == exp(1))) { } else if (is.null(base)) { l <- exp(l); } else { l <- l*log(exp(1), base=base);; } attr(l, "base") <- base; attr(l, "wrss") <- wrss; attr(l, "lambda") <- lambda; attr(l, "roughness") <- Jg; attr(l, "penalty") <- penalty; class(l) <- "SmoothSplineLikelihood"; l; }) ############################################################################ # HISTORY: # 2007-01-01 # o Removed any code to make method backward compatibility with # R < 1.9.0, which was before 'modreg' was merged into 'stats'. # 2005-06-03 # o now returns an object of class SmoothSplineLikelihood. # 2005-02-20 # o Now using setMethodS3() and added '...' to please R CMD check. # 2002-04-21 # o Updated due to modreg is merged into stats from R v1.9.0. # 2002-03-04 # o Returns an object of class likelihood. smooth.spline instead of a list. # o Added the option to explicitly specify x, y and w. # o Rename from logLikelihood(...) to likelihood(..., base=exp(1)). # o BUG FIX: Forgot to take the square in the integral of J(g). # 2002-03-02 # o Added Rdoc details about case with non unique x values. # 2002-03-01 # o Wrote the Rdoc comments # o Created. ############################################################################ aroma.light/R/calibrateMultiscan.R0000644000175000017500000002437014136047216016755 0ustar nileshnilesh#########################################################################/** # @RdocGeneric calibrateMultiscan # @alias calibrateMultiscan.matrix # # \encoding{latin1} # # @title "Weighted affine calibration of a multiple re-scanned channel" # # \description{ # @get "title". # } # # \usage{ # @usage calibrateMultiscan,matrix # } # # \arguments{ # \item{X}{An NxK @matrix (K>=2) where the columns represent the # multiple scans of one channel (a two-color array contains two # channels) to be calibrated.} # \item{weights}{If @NULL, non-weighted normalization is done. # If data-point weights are used, this should be a @vector of length # N of data point weights used when estimating the normalization # function. # } # \item{typeOfWeights}{A @character string specifying the type of # weights given in argument \code{weights}. # } # \item{method}{A @character string specifying how the estimates are # robustified. See @see "iwpca" for all accepted values.} # \item{constraint}{Constraint making the bias parameters identifiable. # See @see "fitIWPCA" for more details.} # \item{satSignal}{Signals equal to or above this threshold is considered # saturated signals.} # \item{...}{Other arguments passed to @see "fitIWPCA" and in # turn @see "iwpca", e.g. \code{center} (see below).} # \item{average}{A @function to calculate the average signals between calibrated scans.} # \item{deviance}{A @function to calculate the deviance of the signals between calibrated scans.} # \item{project}{If @TRUE, the calibrated data points projected onto the # diagonal line, otherwise not. Moreover, if @TRUE, argument # \code{average} is ignored.} # \item{.fitOnly}{If @TRUE, the data will not be back-transform.} # } # # \value{ # If \code{average} is specified or \code{project} is @TRUE, # an Nx1 @matrix is returned, otherwise an NxK @matrix is returned. # If \code{deviance} is specified, a deviance Nx1 @matrix is returned # as attribute \code{deviance}. # In addition, the fitted model is returned as attribute \code{modelFit}. # } # # \section{Negative, non-positive, and saturated values}{ # Affine multiscan calibration applies also to negative values, which are # therefor also calibrated, if they exist. # # Saturated signals in any scan are set to @NA. Thus, they will not be # used to estimate the calibration function, nor will they affect an # optional projection. # } # # \section{Missing values}{ # Only observations (rows) in \code{X} that contain all finite values are # used in the estimation of the calibration functions. Thus, # observations can be excluded by setting them to @NA. # } # # \section{Weighted normalization}{ # Each data point/observation, that is, each row in \code{X}, which is a # vector of length K, can be assigned a weight in [0,1] specifying how much # it should \emph{affect the fitting of the calibration function}. # Weights are given by argument \code{weights}, # which should be a @numeric @vector of length N. Regardless of weights, # all data points are \emph{calibrated} based on the fitted calibration # function. # } # # \section{Robustness}{ # By default, the model fit of multiscan calibration is done in \eqn{L_1} # (\code{method="L1"}). This way, outliers affect the parameter estimates # less than ordinary least-square methods. # # When calculating the average calibrated signal from multiple scans, # by default the median is used, which further robustify against outliers. # # For further robustness, downweight outliers such as saturated signals, # if possible. # # Tukey's biweight function is supported, but not used by default because # then a "bandwidth" parameter has to selected. This can indeed be done # automatically by estimating the standard deviation, for instance using # MAD. However, since scanner signals have heteroscedastic noise # (standard deviation is approximately proportional to the non-logged # signal), Tukey's bandwidth parameter has to be a function of the # signal too, cf. @see "stats::loess". We have experimented with this # too, but found that it does not significantly improve the robustness # compared to \eqn{L_1}. # Moreover, using Tukey's biweight as is, that is, assuming homoscedastic # noise, seems to introduce a (scale dependent) bias in the estimates # of the offset terms. # } # # \section{Using a known/previously estimated offset}{ # If the scanner offsets can be assumed to be known, for instance, # from prior multiscan analyses on the scanner, then it is possible # to fit the scanner model with no (zero) offset by specifying # argument \code{center=FALSE}. # Note that you cannot specify the offset. Instead, subtract it # from all signals before calibrating, e.g. # \code{Xc <- calibrateMultiscan(X-e, center=FALSE)} # where \code{e} is the scanner offset (a scalar). # You can assert that the model is fitted without offset by # \code{stopifnot(all(attr(Xc, "modelFit")$adiag == 0))}. # } # # \details{ # Fitting is done by iterated re-weighted principal component analysis # (IWPCA). # } # # @author # # \references{ # [1] @include "../incl/BengtssonH_etal_2004.bib.Rdoc" \cr # } # # \examples{\dontrun{# For an example, see help(normalizeAffine).}} # # \seealso{ # @see "1. Calibration and Normalization". # @see "normalizeAffine". # } #*/######################################################################### setMethodS3("calibrateMultiscan", "matrix", function(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1", constraint="diagonal", satSignal=2^16-1, ..., average=median, deviance=NULL, project=FALSE, .fitOnly=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (ncol(X) < 2L) stop("Multiscan calibratation requires at least two scans: ", ncol(X)); if (nrow(X) < 3L) stop("Multiscan calibratation requires at least three observations: ", nrow(X)); # Argument: 'satSignal' if (satSignal < 0) stop("Argument 'satSignal' is negative: ", satSignal); # Argument: 'typeOfWeights' typeOfWeights <- match.arg(typeOfWeights); # Argument: 'weights' datapointWeights <- NULL; if (!is.null(weights)) { # If 'weights' is an object of a class with as.double(), cast it. weights <- as.double(weights); if (anyMissing(weights)) stop("Argument 'weights' must not contain NA values."); if (any(weights < 0 | weights > 1)) { stop("Argument 'weights' out of range [0,1]: ", paste(weights[weights < 0.0 | weights > 1.0], collapse=", ")); } weights <- as.vector(weights); if (length(weights) == 1L) { weights <- rep(weights, length.out=nrow(X)); } else if (length(weights) != nrow(X)) { stop("Argument 'weights' does not have the same length as the number of data points (rows) in the matrix: ", length(weights), " != ", nrow(X)); } datapointWeights <- weights; } # Argument 'average': if (!is.null(average) && !is.function(average)) { throw("Argument 'average' must be a function or NULL: ", class(average)[1]); } # Argument 'deviance': if (!is.null(deviance) && !is.function(deviance)) { throw("Argument 'deviance' must be a function or NULL: ", class(deviance)[1]); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Prepare the data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Use non-saturated observations (non-finite values are taken care of by # the fitIWPCA() function. X[(X >= satSignal)] <- NA_real_; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Fit the model # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fit <- fitIWPCA(X, w=datapointWeights, method=method, constraint=constraint, ...); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. Backtransform # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (.fitOnly == FALSE) { X <- backtransformAffine(X, a=fit, project=project); if (project == FALSE && !is.null(average)) { X <- apply(X, MARGIN=1L, FUN=average, na.rm=TRUE); X <- as.matrix(X); } if (!is.null(deviance)) { deviance <- apply(X, MARGIN=1L, FUN=deviance, na.rm=TRUE); attr(X, "deviance") <- as.matrix(deviance); } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 5. Return the backtransformed data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - attr(X, "modelFit") <- fit; X; }) # calibrateMultiscan() ############################################################################ # HISTORY: # 2013-09-26 # o Now utilizing anyMissing(). # 2011-02-05 # o DOCUMENTATION: Added section on how to calibrate when scanner offsets # are supposed to be known/zero. # o DOCUMENTATION: Fixed broken links to help for iwpca(). # 2005-06-03 # o Added argument 'typeOfWeights' to make it similar to other normalization # methods, although only "datapoint" weights are allowed. # 2005-02-13 # o Made argument 'method="L1"' explicit and wrote a Rdoc comment about it # to document the fact that we have deliberately choosen not to use # "symmetric" Tukey's biweight. # 2005-02-04 # o Put arguments 'average' and 'deviance' back again. It is much more # userfriendly. Averaging with median() is now the default. # 2005-02-01 # o Added argument '.fitOnly'. # 2005-01-24 # o Added argument 'weights' (instead of passing 'w' to fitIWPCA()). # o Saturated values are not used to estimate the calibration function nor # are the used if data is projected. # 2004-12-28 # o Added Rdoc comments on weights. # 2004-06-28 # o BUG FIX: Missing braces in Rdoc comments. # 2004-05-18 # o Removed averaging etc. That is now in its own function rowAverages(). # o The only difference between calibrateMultiscanSpatial() and # calibrateMultiscan() is how the parameters are fitted. # 2004-05-14 # o Cleaned up. Making use of new backtransformAffine(), which makes the # code clearer. Explicit arguments that were just passed to iwpca() etc # are now passed as "..." to make the documentation simpler and less # confusing for the end user. Experts will follow "..." to iwpca(). ############################################################################ aroma.light/R/findPeaksAndValleys.R0000644000175000017500000000764014136047216017037 0ustar nileshnilesh###########################################################################/** # @RdocGeneric findPeaksAndValleys # @alias findPeaksAndValleys.density # @alias findPeaksAndValleys.numeric # # @title "Finds extreme points in the empirical density estimated from data" # # \description{ # @get "title". # } # # \usage{ # @usage findPeaksAndValleys,density # @usage findPeaksAndValleys,numeric # } # # \arguments{ # \item{x}{A @numeric @vector containing data points or # a @see "stats::density" object.} # \item{...}{Arguments passed to @see "stats::density". # Ignored if \code{x} is a @see "stats::density" object.} # \item{tol}{A non-negative @numeric threshold specifying the minimum # density at the extreme point in order to accept it.} # \item{na.rm}{If @TRUE, missing values are dropped, otherwise not.} # } # # \value{ # Returns a @data.frame (of class 'PeaksAndValleys') containing # of "peaks" and "valleys" filtered by \code{tol}. # } # # @examples "../incl/findPeaksAndValleys.Rex" # # @author # # \seealso{ # This function is used by @see "callNaiveGenotypes". # } # # @keyword internal #*/########################################################################### setMethodS3("findPeaksAndValleys", "density", function(x, tol=0, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'x': d <- x; # Argument 'tol': tol <- as.double(tol); stopifnot(length(tol) == 1); stopifnot(tol >= 0); delta <- diff(d$y); n <- length(delta); isPeak <- (delta[-n] > 0 & delta[-1] < 0); isValley <- (delta[-n] < 0 & delta[-1] > 0); isPeakOrValley <- (isPeak | isValley); idxs <- which(isPeakOrValley); types <- c("valley", "peak")[isPeak[idxs]+1]; names(idxs) <- types; x <- d$x[idxs]; y <- d$y[idxs]; res <- data.frame(type=types, x=x, density=y); class(res) <- c("PeaksAndValleys", class(res)); # Filter valleys by density? if (tol > 0) { res <- subset(res, density >= tol); } res; }) # findPeaksAndValleys() setMethodS3("findPeaksAndValleys", "numeric", function(x, ..., tol=0, na.rm=TRUE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'na.rm': na.rm <- as.logical(na.rm); stopifnot(length(na.rm) == 1); # Argument 'tol': tol <- as.double(tol); stopifnot(length(tol) == 1); stopifnot(tol >= 0); d <- density(x, na.rm=na.rm, ...); findPeaksAndValleys(d, tol=tol); }) # findPeaksAndValleys() ############################################################################ # HISTORY: # 2011-03-03 [HB] # o BUG FIX: findPeaksAndValleys(x, to) were 'x' is numeric would use # partial match and interpret 'to' as argument 'tol' and not part of # '...' passed to density(). This problem was solved by placing '...' # before argument 'tol'. Thanks Oscar Rueda at the Cancer Reasearch UK # for reporting and identify this bug. # 2010-10-08 [HB] # o Now findPeaksAndValleys() returns a object of class PeaksAndValleys, # which extends data.frame. # 2010-10-06 [HB] # o Added findPeaksAndValleys() for the 'density' class, which then # findPeaksAndValleys() for 'numeric' utilizes. # 2010-04-04 [HB] # o Made findPeaksAndValleys() an internal function in Rd. # o Updated could to validate arguments with using R.utils::Arguments. # o Corrected a non-defined Rdoc tag. # 2009-11-03 [HB] # o Added Rdoc comments with an example(). # 2009-03-06 [HB] # o Created for doing quick naive genotyping of some TCGA normal samples in # order to highlight the centers of the clouds in a tumor-normal fracB # scatter plots. ############################################################################ aroma.light/R/normalizeCurveFit.R0000644000175000017500000002607214136047216016620 0ustar nileshnilesh#########################################################################/** # @RdocGeneric normalizeCurveFit # @alias normalizeLoess # @alias normalizeLowess # @alias normalizeSpline # @alias normalizeRobustSpline # @alias normalizeCurveFit.matrix # @alias normalizeLoess.matrix # @alias normalizeLowess.matrix # @alias normalizeSpline.matrix # @alias normalizeRobustSpline.matrix # # \encoding{latin1} # # @title "Weighted curve-fit normalization between a pair of channels" # # \description{ # @get "title". # # This method will estimate a smooth function of the dependency # between the log-ratios and the log-intensity of the two channels and # then correct the log-ratios (only) in order to remove the dependency. # This is method is also known as \emph{intensity-dependent} or # \emph{lowess normalization}. # # The curve-fit methods are by nature limited to paired-channel data. # There exist at least one method trying to overcome this limitation, # namely the cyclic-lowess [1], which applies the paired # curve-fit method iteratively over all pairs of channels/arrays. # Cyclic-lowess is not implemented here. # # We recommend that affine normalization [2] is used instead of curve-fit # normalization. # } # # \usage{ # @usage normalizeCurveFit,matrix # @usage normalizeLoess,matrix # @usage normalizeLowess,matrix # @usage normalizeSpline,matrix # @usage normalizeRobustSpline,matrix # } # # \arguments{ # \item{X}{An Nx2 @matrix where the columns represent the two channels # to be normalized.} # \item{weights}{If @NULL, non-weighted normalization is done. # If data-point weights are used, this should be a @vector of length # N of data point weights used when estimating the normalization # function. # } # \item{typeOfWeights}{A @character string specifying the type of # weights given in argument \code{weights}. # } # \item{method}{@character string specifying which method to use when # fitting the intensity-dependent function. # Supported methods: # \code{"loess"} (better than lowess), # \code{"lowess"} (classic; supports only zero-one weights), # \code{"spline"} (more robust than lowess at lower and upper # intensities; supports only zero-one weights), # \code{"robustSpline"} (better than spline). # } # \item{bandwidth}{A @double value specifying the bandwidth of the # estimator used. # } # \item{satSignal}{Signals equal to or above this threshold will not # be used in the fitting. # } # \item{...}{Not used.} # } # # \value{ # A Nx2 @matrix of the normalized two channels. # The fitted model is returned as attribute \code{modelFit}. # } # # \details{ # A smooth function \eqn{c(A)} is fitted through data in \eqn{(A,M)}, # where \eqn{M=log_2(y_2/y_1)} and \eqn{A=1/2*log_2(y_2*y_1)}. Data is # normalized by \eqn{M <- M - c(A)}. # # Loess is by far the slowest method of the four, then lowess, and then # robust spline, which iteratively calls the spline method. # } # # \section{Negative, non-positive, and saturated values}{ # Non-positive values are set to not-a-number (@NaN). # Data points that are saturated in one or more channels are not used # to estimate the normalization function, but they are normalized. # } # # \section{Missing values}{ # The estimation of the normalization function will only be made # based on complete non-saturated observations, i.e. observations that # contains no @NA values nor saturated values as defined by \code{satSignal}. # } # # \section{Weighted normalization}{ # Each data point, that is, each row in \code{X}, which is a # vector of length 2, can be assigned a weight in [0,1] specifying how much # it should \emph{affect the fitting of the normalization function}. # Weights are given by argument \code{weights}, which should be a @numeric # @vector of length N. Regardless of weights, all data points are # \emph{normalized} based on the fitted normalization function. # # Note that the lowess and the spline method only support zero-one # \{0,1\} weights. # For such methods, all weights that are less than a half are set to zero. # } # # \section{Details on loess}{ # For @see "stats::loess", the arguments \code{family="symmetric"}, # \code{degree=1}, \code{span=3/4}, # \code{control=loess.control(trace.hat="approximate"}, # \code{iterations=5}, \code{surface="direct")} are used. # } # # @author "HB" # # \references{ # [1] M. \enc{strand}{Astrand}, # Contrast Normalization of Oligonucleotide Arrays, # Journal Computational Biology, 2003, 10, 95-102. \cr # [2] @include "../incl/BengtssonHossjer_2006.bib.Rdoc" \cr # } # # \examples{ # @include "../incl/normalizeCurveFit.matrix.Rex" # } # # \seealso{ # @see "normalizeAffine". # } #*/######################################################################### setMethodS3("normalizeCurveFit", "matrix", function(X, weights=NULL, typeOfWeights=c("datapoint"), method=c("loess", "lowess", "spline", "robustSpline"), bandwidth=NULL, satSignal=2^16-1, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (ncol(X) != 2) stop("Curve-fit normalization requires two channels only: ", ncol(X)); if (nrow(X) < 3) stop("Curve-fit normalization requires at least three observations: ", nrow(X)); # Argument: 'satSignal' if (satSignal < 0) stop("Argument 'satSignal' is negative: ", satSignal); # Argument: 'method' method <- match.arg(method); zeroOneWeightsOnly <- (method %in% c("lowess", "spline")); # Argument: 'typeOfWeights' typeOfWeights <- match.arg(typeOfWeights); # Argument: 'weights' datapointWeights <- NULL; if (!is.null(weights)) { # If 'weights' is an object of a class with as.double(), cast it. weights <- as.double(weights); if (anyMissing(weights)) stop("Argument 'weights' must not contain NA values."); if (any(weights < 0 | weights > 1)) { stop("Argument 'weights' out of range [0,1]: ", paste(weights[weights < 0.0 | weights > 1.0], collapse=", ")); } if (zeroOneWeightsOnly && any(weights > 0 & weights < 1)) { weights <- round(weights); warning("Weights were rounded to {0,1} since '", method, "' normalization supports only zero-one weights."); } weights <- as.vector(weights); if (length(weights) == 1) { weights <- rep(weights, length.out=nrow(X)); } else if (length(weights) != nrow(X)) { stop("Argument 'weights' does not have the same length as the number of data points (rows) in the matrix: ", length(weights), " != ", nrow(X)); } datapointWeights <- weights; } # if (!is.null(weights)) # Argument: 'bandwidth' if (is.null(bandwidth)) { bandwidths <- c("loess"=0.75, "lowess"=0.3, "robustSpline"=0.75, "spline"=0.75); bandwidth <- bandwidths[method]; } else if (!is.numeric(bandwidth) || bandwidth <= 0 || bandwidth > 1) { stop("Argument 'bandwidth' must be in [0,1): ", bandwidth); } else if (length(bandwidth) != 1) { stop("Argument 'bandwidth' must be a scalar: ", paste(bandwidth, collapse=", ")); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Prepare data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Convert non-positive signals to NaN. If not done here, the transform # (R,G) -> (A,M) -> (R,G) will no it. X[X <= 0] <- NaN; # Use only positive non-saturated observations to estimate the # normalization function isValid <- (is.finite(X) & (X <= satSignal)); isValid <- (isValid[,1] & isValid[,2]); Y <- X[isValid,]; if (!is.null(datapointWeights)) datapointWeights <- datapointWeights[isValid]; M <- log(Y[,1]/Y[,2], base=2); A <- log(Y[,1]*Y[,2], base=2)/2; # Not needed anymore Y <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Estimate the model # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (method == "lowess") { incl <- if (!is.null(datapointWeights)) (datapointWeights > 0) else TRUE; fit <- lowess(x=A[incl], y=M[incl], f=bandwidth, ...); fit$predictM <- function(newA) approx(fit, xout=newA, ties=mean)$y; } else if (method == "loess") { fit <- loess(formula=M ~ A, weights=datapointWeights, family="symmetric", degree=1, span=bandwidth, control=loess.control(trace.hat="approximate", iterations=5, surface="direct"), ...); fit$predictM <- function(newA) predict(fit, newdata=newA); } else if (method == "spline") { incl <- if (!is.null(datapointWeights)) (datapointWeights > 0) else TRUE; fit <- smooth.spline(x=A[incl], y=M[incl], spar=bandwidth, ...); fit$predictM <- function(newA) predict(fit, x=newA)$y; } else if (method == "robustSpline") { fit <- robustSmoothSpline(x=A, y=M, w=datapointWeights, spar=bandwidth, ...); fit$predictM <- function(newA) predict(fit, x=newA)$y; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. Normalize # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize all data M <- log(X[,1]/X[,2], base=2); A <- log(X[,1]*X[,2], base=2)/2; ok <- is.finite(A); M[ok] <- M[ok] - fit$predictM(A[ok]); # Not needed anymore ok <- NULL; X[,1] <- as.matrix(sqrt(2^(2*A+M))); X[,2] <- as.matrix(sqrt(2^(2*A-M))); # Not needed anymore A <- M <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 5. Return the normalized data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - attr(X, "modelFit") <- fit; X; }) # normalizeCurveFit() setMethodS3("normalizeLowess", "matrix", function(X, ...) { normalizeCurveFit(X, method="lowess", ...); }) setMethodS3("normalizeLoess", "matrix", function(X, ...) { normalizeCurveFit(X, method="loess", ...); }) setMethodS3("normalizeSpline", "matrix", function(X, ...) { normalizeCurveFit(X, method="spline", ...); }) setMethodS3("normalizeRobustSpline", "matrix", function(X, ...) { normalizeCurveFit(X, method="robustSpline", ...); }) ############################################################################ # HISTORY: # 2013-09-26 # o Now utilizing anyMissing(). # 2005-06-03 # o Added argument 'typeOfWeights' to make it similar to other normalization # methods, although only "datapoint" weights are allowed. # o Removed argument '.fitOnly'. # o renamed all "robust.spline" to "robustSpline". # 2005-03-23 # o Updated normalizeCurveFit() so that approx() does not give warnings # about 'Collapsing to unique x values' when doing lowess normalization. # 2005-02-02 # o Zero-one weights are now round off by round(w). # o BUG FIX: Forgot to adjust weights vector in normalizeCurveFit() when # removing non-finite values from data. # 2005-02-01 # o Added argument '.fitOnly'. # 2005-01-24 # o Create an Rdoc example with MvsA and MvsM comparisons. # 2005-01-23 # o Added aliases normalizeLowess() and normalizeLoess(). # o Created from normalizeCurveFit() in MAData(). ############################################################################ aroma.light/R/fitPrincipalCurve.R0000644000175000017500000001116414136047216016575 0ustar nileshnilesh#########################################################################/** # @RdocGeneric fitPrincipalCurve # @alias fitPrincipalCurve.matrix # # @title "Fit a principal curve in K dimensions" # # \description{ # @get "title". # } # # \usage{ # @usage fitPrincipalCurve,matrix # } # # \arguments{ # \item{X}{An NxK @matrix (K>=2) where the columns represent the dimension.} # \item{...}{Other arguments passed to @see "princurve::principal_curve".} # \item{verbose}{A @logical or a @see "R.utils::Verbose" object.} # } # # \value{ # Returns a principal_curve object (which is a @list). # See @see "princurve::principal_curve" for more details. # } # # \section{Missing values}{ # The estimation of the normalization function will only be made # based on complete observations, i.e. observations that contains no @NA # values in any of the channels. # } # # @author "HB" # # \references{ # [1] Hastie, T. and Stuetzle, W, \emph{Principal Curves}, JASA, 1989.\cr # [2] @include "../incl/BengtssonH_etal_2009.bib.Rdoc" \cr # } # # @examples "../incl/fitPrincipalCurve.matrix.Rex" # # \seealso{ # @see "backtransformPrincipalCurve". # @see "princurve::principal_curve". # } #*/######################################################################### setMethodS3("fitPrincipalCurve", "matrix", function(X, ..., verbose=FALSE) { # princurve v1.1-9 and before contains bugs. /HB 2008-05-26 # princurve v2.0.0 replaced princurve.curve with princurve_curve. /HB 2018-09-04 use("princurve (>= 2.1.2)") principal_curve <- princurve::principal_curve # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - n <- nrow(X); p <- ncol(X); # Argument 'verbose': verbose <- Arguments$getVerbose(verbose); if (verbose) { pushState(verbose); on.exit(popState(verbose)); } verbose && enter(verbose, "Fitting principal curve"); verbose && cat(verbose, "Data size: ", n, "x", p); verbose && enter(verbose, "Identifying missing values"); # princurve::principal_curve() does not handle missing values. keep <- rep(TRUE, times=n); for (cc in seq_len(p)) { keep <- keep & is.finite(X[,cc]); } anyMissing <- (!all(keep)); if (anyMissing) { X <- X[keep,, drop=FALSE]; } verbose && exit(verbose); verbose && cat(verbose, "Data size after removing non-finite data points: ", nrow(X), "x", p); verbose && enter(verbose, "Calling principal_curve()"); trace <- as.logical(verbose); t <- system.time({ fit <- principal_curve(X, ..., trace=trace); }); attr(fit, "processingTime") <- t; verbose && printf(verbose, "Converged: %s\n", fit$converged); verbose && printf(verbose, "Number of iterations: %d\n", fit$num_iterations); verbose && printf(verbose, "Processing time/iteration: %.1fs (%.1fs/iteration)\n", t[3], t[3]/fit$num_iterations); verbose && exit(verbose); # Expand, iff missing values were dropped if (anyMissing) { values <- matrix(NA_real_, nrow=n, ncol=p); values[keep,] <- fit$s; dimnames(values) <- dimnames(fit$s); fit$s <- values; values <- rep(NA_real_, times=n); for (ff in c("ord", "lambda")) { values[keep] <- fit[[ff]]; fit[[ff]] <- values; } } verbose && exit(verbose); class(fit) <- c("PrincipalCurve", class(fit)); fit; }) # fitPrincipalCurve() ########################################################################### # HISTORY: # 2013-04-18 # o BUG FIX: fitPrincipalCurve() would not preserve dimension names # if data contain missing values. # 2011-04-12 # o CLEANUP: Removed internal patch of principal.curve(). If an older # version than princurve v1.1-10 is used, an informative error is # thrown requesting an update. The internal patch is part of the # offical princurve v1.1-10 release (since 2009-10-04). # 2009-11-01 # o Now fitPrincipalCurve() bug-fixed princurve v1.1-10. If earlier # version are available, it used the internal patch. # 2009-07-15 # o Added attribute 'processingTime' to the fit object returned by # fitPrincipalCurve(). # 2009-01-12 # o Updated code such that R.utils::Verbose is optional. # 2008-10-08 # o Removed argument 'fixDimension'. That constrain is taken care of # by backtransformPrincipalCurve(). # o Now the fitted object is of class PrincipalCurve that extends the # princurve::principal.curve class. # 2008-10-07 # o Added Rdoc comments and an example. # o Removed implementation for data.frame:s. # 2008-10-03 # o Added argument 'fixDimension'. # 2008-05-27 # o Added fitPrincipalCurve(). # o Created. ########################################################################### aroma.light/R/999.package.R0000644000175000017500000000776414136047216015103 0ustar nileshnilesh#########################################################################/** # @RdocPackage aroma.light # # \encoding{latin1} # # \description{ # @eval "getDescription(aroma.light)" # } # # \section{Installation}{ # To install this package, see # \url{https://bioconductor.org/packages/release/bioc/html/aroma.light.html}. # } # # \section{To get started}{ # For scanner calibration: # \enumerate{ # \item see @see "calibrateMultiscan" - scan the same array two or more times to calibrate for scanner effects and extended dynamical range. # } # # To normalize multiple single-channel arrays all with the same number of probes/spots: # \enumerate{ # \item @see "normalizeAffine" - normalizes, on the intensity scale, for differences in offset and scale between channels. # \item @see "normalizeQuantileRank", @see "normalizeQuantileSpline" - normalizes, on the intensity scale, for differences in empirical distribution between channels. # } # # To normalize multiple single-channel arrays with varying number probes/spots: # \enumerate{ # \item @see "normalizeQuantileRank", @see "normalizeQuantileSpline" - normalizes, on the intensity scale, for differences in empirical distribution between channels. # } # # To normalize two-channel arrays: # \enumerate{ # \item @see "normalizeAffine" - normalizes, on the intensity scale, for differences in offset and scale between channels. This will also correct for intensity-dependent affects on the log scale. # \item @see "normalizeCurveFit" - Classical intensity-dependent normalization, on the log scale, e.g. lowess normalization. # } # # To normalize three or more channels: # \enumerate{ # \item @see "normalizeAffine" - normalizes, on the intensity scale, for differences in offset and scale between channels. This will minimize the curvature on the log scale between any two channels. # } # } # # \section{Further readings}{ # Several of the normalization methods proposed in [1]-[7] are # available in this package. # } # # \section{How to cite this package}{ # Whenever using this package, please cite one or more of [1]-[7]. # } # # \section{Wishlist}{ # Here is a list of features that would be useful, but which I have # too little time to add myself. Contributions are appreciated. # \itemize{ # \item At the moment, nothing. # } # # If you consider to contribute, make sure it is not already # implemented by downloading the latest "devel" version! # } # # @author "*" # # \section{License}{ # The releases of this package is licensed under # GPL version 2 or newer. # # NB: Except for the \code{robustSmoothSpline()} method, # it is alright to distribute the rest of the package under # LGPL version 2.1 or newer. # # The development code of the packages is under a private licence # (where applicable) and patches sent to the author fall under the # latter license, but will be, if incorporated, released under the # "release" license above. # } # # \references{ # Some of the reference below can be found at # \url{https://www.aroma-project.org/publications/}.\cr # # [1] H. Bengtsson, \emph{Identification and normalization of plate effects # in cDNA microarray data}, Preprints in Mathematical Sciences, # 2002:28, Mathematical Statistics, Centre for Mathematical Sciences, # Lund University, 2002.\cr # # [2] @include "../incl/BengtssonH_2003.bib.Rdoc" \cr # # [3] H. Bengtsson, \emph{aroma - An R Object-oriented Microarray # Analysis environment}, Preprints in Mathematical Sciences (manuscript # in preparation), Mathematical Statistics, Centre for Mathematical # Sciences, Lund University, 2004.\cr # # [4] @include "../incl/BengtssonH_etal_2004.bib.Rdoc" \cr # # [5] @include "../incl/BengtssonHossjer_2006.bib.Rdoc" \cr # # [6] @include "../incl/BengtssonH_etal_2008.bib.Rdoc" \cr # # [7] @include "../incl/BengtssonH_etal_2009.bib.Rdoc" \cr # # [8] @include "../incl/BengtssonNeuvial_2010.bib.Rdoc" \cr # } #*/######################################################################### aroma.light/R/999.NonDocumentedObjects.R0000644000175000017500000000226614136047216017554 0ustar nileshnilesh###########################################################################/** # @RdocDocumentation "Non-documented objects" # # % Plot functions # @alias lines.XYCurveFit # # % Matrix operations # @alias rowAverages # @alias rowAverages.matrix # # % Simple linear-algebra # @alias projectUontoV # @alias scalarProduct # @alias tr # # % Miscellaneous statistical functions # @alias likelihood # @alias predict.lowess # # \description{ # This page contains aliases for all "non-documented" objects that # \code{R CMD check} detects in this package. # # Almost all of them are \emph{generic} functions that have specific # document for the corresponding method coupled to a specific class. # Other functions are re-defined by \code{setMethodS3()} to # \emph{default} methods. Neither of these two classes are non-documented # in reality. # The rest are deprecated methods. # } # # @keyword internal #*/########################################################################### ############################################################################ # HISTORY: # 2005-02-10 # o Created to please R CMD check. ############################################################################ aroma.light/R/wpca.R0000644000175000017500000002367414136047216014107 0ustar nileshnilesh#########################################################################/** # @RdocGeneric wpca # @alias wpca.matrix # # @title "Light-weight Weighted Principal Component Analysis" # # \usage{ # @usage wpca,matrix # } # # \description{ # Calculates the (weighted) principal components of a matrix, that is, # finds a new coordinate system (not unique) for representing the given # multivariate data such that # i) all dimensions are orthogonal to each other, and # ii) all dimensions have maximal variances. # } # # \arguments{ # \item{x}{An NxK @matrix.} # \item{w}{An N @vector of weights for each row (observation) in # the data matrix. If @NULL, all observations get the same weight, # that is, standard PCA is used.} # \item{center}{If @TRUE, the (weighted) sample mean column @vector is # subtracted from each column in \code{mat}, first. # If data is not centered, the effect will be that a linear subspace # that goes through the origin is fitted.} # \item{scale}{If @TRUE, each column in \code{mat} is # divided by its (weighted) root-mean-square of the # centered column, first.} # \item{method}{If \code{"dgesdd"} LAPACK's divide-and-conquer # based SVD routine is used (faster [1]). # If \code{"dgesvd"}, LAPACK's QR-decomposition-based routine is used. # } # \item{swapDirections}{If @TRUE, the signs of eigenvectors # that have more negative than positive components are inverted. # The signs of corresponding principal components are also inverted. # This is only of interest when for instance visualizing or comparing # with other PCA estimates from other methods, because the # PCA (SVD) decomposition of a matrix is not unique. # } # \item{...}{Not used.} # } # # \value{ # Returns a @list with elements: # \item{pc}{An NxK @matrix where the column @vectors are the # principal components (a.k.a. loading vectors, # spectral loadings or factors etc).} # \item{d}{An K @vector containing the eigenvalues of the # principal components.} # \item{vt}{An KxK @matrix containing the eigenvector of the # principal components.} # \item{xMean}{The center coordinate.} # # It holds that \code{x == t(t(fit$pc \%*\% fit$vt) + fit$xMean)}. # } # # \section{Method}{ # A singular value decomposition (SVD) is carried out. # Let X=\code{mat}, then the SVD of the matrix is \eqn{X = U D V'}, where # \eqn{U} and \eqn{V} are orthogonal, and \eqn{D} is a diagonal matrix # with singular values. The principal returned by this method are \eqn{U D}. # # Internally \code{La.svd()} (or \code{svd()}) of the \pkg{base} # package is used. # For a popular and well written introduction to SVD see for instance [2]. # } # # \examples{ # @include "../incl/wpca.matrix.Rex" # # if (dev.cur() > 1) dev.off() # # @include "../incl/wpca2.matrix.Rex" # } # # @author # # \references{ # [1] J. Demmel and J. Dongarra, \emph{DOE2000 Progress Report}, 2004. # \url{https://people.eecs.berkeley.edu/~demmel/DOE2000/Report0100.html} \cr # [2] Todd Will, \emph{Introduction to the Singular Value Decomposition}, # UW-La Crosse, 2004. \url{http://websites.uwlax.edu/twill/svd/} \cr # } # # \seealso{ # For a iterative re-weighted PCA method, see @see "iwpca". # For Singular Value Decomposition, see @see "base::svd". # For other implementations of Principal Component Analysis functions see # (if they are installed): # @see "stats::prcomp" in package \pkg{stats} and \code{pca()} in package # \pkg{pcurve}. # } # # @keyword "algebra" #*/######################################################################### setMethodS3("wpca", "matrix", function(x, w=NULL, center=TRUE, scale=FALSE, method=c("dgesdd", "dgesvd"), swapDirections=FALSE, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'x' x <- as.matrix(x); # The dimensions of 'x' N <- nrow(x); K <- ncol(x); # Standardizes the weights to [0,1] such that they sum to 1. if (!is.null(w)) { w <- rep(w, length.out=N); w <- w/sum(w); if (anyMissing(w)) stop("Argument 'w' has missing values."); } ## Argument 'method': method <- match.arg(method, choices = c("dgesdd", "dgesvd")) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Weighted or non-weighted centering and rescaling of the data # # Note: The following split of (center == TRUE) and (center == FALSE) # is to minimize memory usage. In other words, the codes is longer, # but more memory efficient. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (center || scale) { if (is.null(w)) { # Calculates the standard column means xMean <- colMeans(x, na.rm=TRUE); # a K vector } else { # Calculates the weighted column means (recall that sum(w) == 1) xMean <- as.vector(w %*% x); # a K vector } if (center) { # Centers the data directly by subtracting the column means for (kk in 1:ncol(x)) x[,kk] <- x[,kk] - xMean[kk]; } else { # ...or calculates the centered data for rescaling xc <- x; for (kk in 1:ncol(x)) xc[,kk] <- x[,kk] - xMean[kk]; } if (scale) { if (is.null(w)) { # Non-weighted root-mean-squares rms <- function(v) { # v - column vector of length N v <- v[!is.na(v)]; sqrt(sum(v^2)/max(1, length(v)-1)); } } else { # Weighted root-mean-squares rms <- function(v) { # v - column vector of length N ok <- !is.na(v); v <- w[ok]*v[ok]; sqrt(sum(v^2)/max(1, length(v)-1)); } } if (center) { xRMS <- apply(x, MARGIN=2, FUN=rms); } else { xRMS <- apply(xc, MARGIN=2, FUN=rms); # Not needed anymore xc <- NULL; } for (kk in 1:ncol(x)) x[,kk] <- x[,kk] / xRMS[kk]; # Not needed anymore xRMS <- rms <- NULL; } } else { xMean <- rep(0, length=K); } # Weight the observations? if (!is.null(w)) { x <- sqrt(w)*x; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Singular Value Decomposition, i.e. X = U D V' # # "We compare DGESVD, the original QR-based routines from # LAPACK 1.0, with DGESDD, the new divide-and-conquer based # routine from LAPACK 3.0. The table below shows the speeds # on several machines. The new routine is 5.7 to 16.8 times # faster than the old routine. Part of the speed results # from doing many fewer operations, and the rest comes from # doing them faster (a high Mflop rate)." [1] # [1] J. Demmel and J. Dongarra, DOE2000 Progress Report, # http://www.cs.berkeley.edu/~demmel/DOE2000/Report0100.html # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (method == "dgesdd" || method == "dgesvd") { duvt <- La.svd(x); } else { stop(sprintf("Unknown LAPACK or LINPACK routine to solve SVD: %s", method)); } # 'duvt' is a list with the follwing components: # # u - a NxK matrix whose columns contain the left singular # vectors (eigenvectors) of 'x'. # It holds that t(u) %*% u == I # d - a K vector containing the singular value of # each principal component on its diagonal. # It holds that d[1] >= d[2] >= ... d[K] >= 0. # vt - a KxK transposed matrix whose columns contain the right # singular vectors (eigenvector) of 'x'. # It holds that t(v) %*% v == I # Not need anymore, in case a local copy has been created! x <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. The PCA principal components # # (a.k.a. loading vectors, spectral loadings or factors). # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - d <- duvt$d; vt <- duvt$vt; pc <- duvt$u; # Not need anymore duvt <- NULL; # Note: D == diag(duvt$d) is memory expensive since the dimensions of D # is the same as the dimensions of 'x'. Thus, it unwise to do: # pc <- duvt$u %*% diag(duvt$d); for (kk in seq_len(N)) pc[kk,] <- pc[kk,] * d; if (!is.null(w)) { # Rescale the principal components pc <- pc / sqrt(w); # Not need anymore w <- NULL; } if (swapDirections) { swap <- apply(vt, MARGIN=1, FUN=function(z) sum(sign(z)) < 0); # Which eigenvectors should swap signs? swap <- which(swap); for (kk in swap) { vt[kk,] <- -vt[kk,]; pc[,kk] <- -pc[,kk]; } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 4. Return the parameter estimates # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - res <- list(pc=pc, d=d, vt=vt, xMean=xMean); class(res) <- "WPCAFit"; res; }) # wpca() ############################################################################ # HISTORY: # 2015-05-24 # o Removed obsolete method="dsvdc"; only needed in R (< 1.7.0). # 2013-09-26 # o Now utilizing anyMissing(). # 2006-06-26 # o Function would not work in R v2.4.0 devel, because argument 'method' was # removed from La.svd(). # 2006-04-25 # o Updated the URL to Todd Will's webpage. # 2005-02-20 # o Added '...' to please R CMD check. # 2005-02-20 # o Now using setMethodS3() and added '...' to please R CMD check. # 2005-01-24 # o Added a check for missing values of argument 'w'. # 2004-05-14 # o Made into a method of class matrix instead of a stand-alone function. # 2003-03-09 # o Created! Verified that it gives similar results as acp(). ############################################################################ aroma.light/R/averageQuantile.R0000644000175000017500000001167214136047216016265 0ustar nileshnilesh###########################################################################/** # @RdocGeneric averageQuantile # @alias averageQuantile.list # @alias averageQuantile.matrix # # @title "Gets the average empirical distribution" # # \usage{ # @usage averageQuantile,list # @usage averageQuantile,matrix # } # # \description{ # @get "title" for a set of samples. # } # # \arguments{ # \item{X}{A @list with K @numeric @vectors, or a @numeric NxK @matrix. # If a @list, the @vectors may be of different lengths.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length equal to the longest @vector # in argument \code{X}. # } # # \section{Missing values}{ # Missing values are excluded. # } # # \seealso{ # @see "normalizeQuantileRank". # @see "normalizeQuantileSpline". # @see "stats::quantile". # } # # \author{ # Parts adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 # \& 2006. Original code by Ben Bolstad at Statistics Department, # University of California. # } # # @keyword "nonparametric" # @keyword "multivariate" # @keyword "robust" #*/########################################################################### setMethodS3("averageQuantile", "list", function(X, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': nbrOfChannels <- length(X); # Nothing to do? if(nbrOfChannels == 1L) return(X); nbrOfObservations <- unlist(lapply(X, FUN=length), use.names=FALSE); maxNbrOfObservations <- max(nbrOfObservations); # Nothing to do? if(maxNbrOfObservations == 1L) return(X); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get the sample quantile for all channels (columns) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Construct the sample quantiles quantiles <- (0:(maxNbrOfObservations-1L))/(maxNbrOfObservations-1L); # Create a vector to hold the target distribution xTarget <- vector("double", length=maxNbrOfObservations); for (cc in 1:nbrOfChannels) { Xcc <- X[[cc]]; # Order and sort the values Scc <- sort(Xcc, na.last=NA); # The number of non-NA observations nobs <- length(Scc); # Too few data points? if(nobs < maxNbrOfObservations) { # Get the sample quantiles for those values bins <- (0:(nobs-1L))/(nobs-1L); # Interpolate to get the values at positions specified by # 'quantile' using data points given by 'bins' and 'Scc'. Scc <- approx(x=bins, y=Scc, xout=quantiles, ties="ordered")$y; bins <- NULL; # Not needed anymore } # Incremental mean xTarget <- xTarget + Scc; Scc <- NULL; # Not needed anymore } Xcc <- NULL; # Not needed anymore xTarget <- xTarget/nbrOfChannels; xTarget; }) # averageQuantile.list() setMethodS3("averageQuantile", "matrix", function(X, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfChannels <- ncol(X); # Nothing to do? if(nbrOfChannels == 1L) return(X); nbrOfObservations <- nrow(X); # Nothing to do? if(nbrOfObservations == 1L) return(X); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Get the sample quantile for all channels (columns) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Construct the sample quantiles quantiles <- (0:(nbrOfObservations-1L))/(nbrOfObservations-1L); # Create a vector to hold the target distribution xTarget <- vector("double", length=nbrOfObservations); for (cc in 1:nbrOfChannels) { Xcc <- X[,cc,drop=TRUE]; # Order and sort the values Scc <- sort(Xcc, na.last=NA); # The number of non-NA observations nobs <- length(Scc); # Too few data points? if(nobs < nbrOfObservations) { # Get the sample quantiles for those values bins <- (0:(nobs-1L))/(nobs-1L); # Interpolate to get the values at positions specified by # 'quantile' using data points given by 'bins' and 'Scc'. Scc <- approx(x=bins, y=Scc, xout=quantiles, ties="ordered")$y; bins <- NULL; # Not needed anymore } # Incremental mean xTarget <- xTarget + Scc; Scc <- NULL; # Not needed anymore } Xcc <- NULL; # Not needed anymore xTarget <- xTarget/nbrOfChannels; xTarget; }) # averageQuantile.matrix() ############################################################################## # HISTORY: # 2013-10-08 # o Added averageQuantile() for matrices. # 2007-01-22 # o BUG FIX: averageQuantile.list() did not deal with vectors of different # length correctly. Thanks Alicia Oshlack, WEHI. # 2006-05-12 # o Created from normalizeQuantile.matrix.R. It has been optimized for # memory. Hence, the normalization is done using a two-pass procedure. ############################################################################## aroma.light/R/backtransformXYCurve.matrix.R0000644000175000017500000000216314136047216020570 0ustar nileshnileshsetMethodS3("backtransformXYCurve", "matrix", function(X, fit, targetChannel=1, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (ncol(X) != 2) { stop("Curve-fit normalization requires two channels only: ", ncol(X)); } # Argument 'targetChannel': # targetChannel <- Arguments$getIndex(targetChannel, range=c(1,ncol(X))); # Allocate result XN <- X; # Predict using only finite covariates (otherwise an error) keep <- which(is.finite(X[,targetChannel])); # Nothing to do? if (length(keep) > 0) { X <- X[keep,,drop=FALSE]; xN <- fit$predictY(X[,targetChannel]); delta <- xN - X[,targetChannel]; # Not needed anymore xN <- NULL; XN[keep,-targetChannel] <- X[,-targetChannel] - delta; # Not needed anymore keep <- delta <- NULL; } XN; }) ############################################################################ # HISTORY: # 2009-07-15 # o Created. ############################################################################ aroma.light/R/predict.lowess.R0000644000175000017500000000054214136047216016107 0ustar nileshnileshsetMethodS3("predict", "lowess", function(object, newdata=NULL, ties=mean, ...) { approx(object, xout=newdata, ties=ties, ...)$y; }, private=TRUE) # predict() ############################################################################ # HISTORY: # 2006-11-28 # o Created. ############################################################################ aroma.light/R/normalizeTumorBoost.R0000644000175000017500000002616614136047216017212 0ustar nileshnilesh###########################################################################/** # @RdocGeneric normalizeTumorBoost # @alias normalizeTumorBoost.numeric # # @title "Normalizes allele B fractions for a tumor given a match normal" # # \description{ # TumorBoost [1] is a normalization method that normalizes the allele B # fractions of a tumor sample given the allele B fractions and genotypes # of a matched normal. # The method is a single-sample (single-pair) method. # It does not require total copy-number estimates. # The normalization is done such that the total copy number is # unchanged afterwards. # } # # \usage{ # @usage normalizeTumorBoost,numeric # } # # \arguments{ # \item{betaT, betaN}{Two @numeric @vectors each of length J with # tumor and normal allele B fractions, respectively.} # \item{muN}{An optional @vector of length J containing # normal genotypes calls in (0,1/2,1,@NA) for (AA,AB,BB).} # \item{preserveScale}{If @TRUE, SNPs that are heterozygous in the # matched normal are corrected for signal compression using an estimate # of signal compression based on the amount of correction performed # by TumorBoost on SNPs that are homozygous in the matched normal.} # \item{flavor}{A @character string specifying the type of # correction applied.} # \item{...}{Not used.} # } # # \value{ # Returns a @numeric @vector of length J containing the normalized # allele B fractions for the tumor. # Attribute \code{modelFit} is a @list containing model fit parameters. # } # # \details{ # Allele B fractions are defined as the ratio between the allele B signal # and the sum of both (all) allele signals at the same locus. # Allele B fractions are typically within [0,1], but may have a slightly # wider support due to for instance negative noise. # This is typically also the case for the returned normalized # allele B fractions. # } # # \section{Flavors}{ # This method provides a few different "flavors" for normalizing the # data. The following values of argument \code{flavor} are accepted: # \itemize{ # \item{v4: (default) The TumorBoost method, i.e. Eqns. (8)-(9) in [1].} # \item{v3: Eqn (9) in [1] is applied to both heterozygous and homozygous # SNPs, which effectively is v4 where the normalized allele B # fractions for homozygous SNPs becomes 0 and 1.} # \item{v2: ...} # \item{v1: TumorBoost where correction factor is forced to one, i.e. # \eqn{\eta_j=1}. As explained in [1], this is a suboptimal # normalization method. See also the discussion in the # paragraph following Eqn (12) in [1].} # } # } # # \section{Preserving scale}{ # \emph{As of \pkg{aroma.light} v1.33.3 (March 30, 2014), # argument \code{preserveScale} no longer has a default value and has # to be specified explicitly. This is done in order to change the # default to @FALSE in a future version, while minimizing the risk # for surprises.} # # Allele B fractions are more or less compressed toward a half, e.g. # the signals for homozygous SNPs are slightly away from zero and one. # The TumorBoost method decreases the correlation in allele B fractions # between the tumor and the normal \emph{conditioned on the genotype}. # What it does not control for is the mean level of the allele B fraction # \emph{conditioned on the genotype}. # # By design, most flavors of the method will correct the homozygous SNPs # such that their mean levels get close to the expected zero and # one levels. However, the heterozygous SNPs will typically keep the # same mean levels as before. # One possibility is to adjust the signals such as the mean levels of # the heterozygous SNPs relative to that of the homozygous SNPs is # the same after as before the normalization. # # If argument \code{preserveScale=TRUE}, then SNPs that are heterozygous # (in the matched normal) are corrected for signal compression using # an estimate of signal compression based on the amount of correction # performed by TumorBoost on SNPs that are homozygous # (in the matched normal). # # The option of preserving the scale is \emph{not} discussed in the # TumorBoost paper [1], which presents the \code{preserveScale=FALSE} # version. # } # # @examples "../incl/normalizeTumorBoost.Rex" # # @author "HB, PN" # # \references{ # [1] @include "../incl/BengtssonNeuvial_2010.bib.Rdoc" \cr # } #*/########################################################################### setMethodS3("normalizeTumorBoost", "numeric", function(betaT, betaN, muN=callNaiveGenotypes(betaN), preserveScale=FALSE, flavor=c("v4", "v3", "v2", "v1"), ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'betaT' & 'betaN': betaT <- as.numeric(betaT); betaN <- as.numeric(betaN); J <- length(betaT); if (length(betaN) != J) { stop("The length of arguments 'betaT' and 'betaN' differ: ", length(betaN), " != ", J); } # Argument: 'muN': if (length(muN) != J) { stop("Argument 'muN' does not match the number of loci: ", length(muN), " != ", J); } knownGenotypes <- c(0, 1/2, 1, NA); unknown <- which(!is.element(muN, knownGenotypes)); n <- length(unknown); if (n > 0) { unknown <- unique(muN[unknown]); stop("Argument 'muN' contains unknown values: ", hpaste(unknown)); } # Argument: 'preserveScale': preserveScale <- as.logical(preserveScale); # Argument: 'flavor': flavor <- match.arg(flavor); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Extract data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Identify set to be updated toUpdate <- which(is.finite(betaT) & is.finite(betaN) & is.finite(muN)); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Estimate delta # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - delta <- (betaN - muN); if (flavor == "v1") { b <- 1; } else if (flavor == "v2") { b <- rep(1, times=length(delta)); isDown <- (betaT < betaN); isBetaNZero <- (betaN == 0); isBetaNOne <- (betaN == 1); idxs <- which(isDown & !isBetaNZero); b[idxs] <- betaT[idxs]/betaN[idxs]; idxs <- which(!isDown & !isBetaNOne); b[idxs] <- (1-betaT[idxs])/(1-betaN[idxs]); # Not needed anymore isDown <- idxs <- NULL; # Treat the case when the estimated SNP effect is zero # Then we want the normalized value to be exactly zero or one. idxs <- which(delta == 0); } else if (flavor == "v3") { b <- rep(1, times=length(delta)); isHomA <- (muN == 0); isHomB <- (muN == 1); isHet <- (!isHomA & !isHomB); isDown <- (betaT < betaN); isBetaNZero <- (betaN == 0); isBetaNOne <- (betaN == 1); idxs <- which((isHet & isDown & !isBetaNZero) | (isHomA & !isBetaNZero)); b[idxs] <- betaT[idxs]/betaN[idxs]; idxs <- which((isHet & !isDown & !isBetaNOne) | (isHomB & !isBetaNOne)); b[idxs] <- (1-betaT[idxs])/(1-betaN[idxs]); # Not needed anymore isDown <- isHet <- isHomA <- isHomB <- idxs <- NULL; } else if (flavor == "v4") { # This is the published TumorBoost normalization method b <- rep(1, times=length(delta)); isHet <- (muN != 0 & muN != 1); isDown <- (betaT < betaN); idxs <- which(isHet & isDown); b[idxs] <- betaT[idxs]/betaN[idxs]; idxs <- which(isHet & !isDown); b[idxs] <- (1-betaT[idxs])/(1-betaN[idxs]); # Not needed anymore isDown <- isHet <- idxs <- NULL; } delta <- b * delta; # Sanity check stopifnot(length(delta) == J); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Normalize # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # In very rare cases delta can be non-finite while betaT is. # This can happen whenever muN or betaN is non-finite. Then: # ok <- is.finite(delta); # ok <- which(ok); # betaTN[ok] <- betaT[ok] - delta[ok]; # It can be debated whether one should correct a SNP in this case, for # which betaTN then become non-finite too. If not correcting, we will # end up with betaTN == betaT value which is not from the same mixture # distribution as the other corrected values. # For now, we ignore this. /HB 2010-08-19 [on the flight to SFO] betaTN <- betaT - delta; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Preserve scale of heterozygotes relative to homozygotes # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (preserveScale) { isHom <- (muN == 0 | muN == 1); idxs <- which(isHom); # Signal compression in homozygous SNPs before TBN eta <- median(abs(betaT[idxs]-1/2), na.rm=TRUE); # Signal compression in homozygous SNPs after TBN etaC <- median(abs(betaTN[idxs]-1/2), na.rm=TRUE); # Correction factor sf <- etaC/eta; # Correct isHet <- !isHom; isDown <- (betaTN < 1/2); idxs <- which(isHet & isDown); betaTN[idxs] <- 1/2 - sf * (1/2 - betaTN[idxs]); idxs <- which(isHet & !isDown); betaTN[idxs] <- 1/2 + sf * (betaTN[idxs] - 1/2); # Not needed anymore isDown <- isHom <- isHet <- idxs <- eta <- etaC <- NULL; } else { sf <- NA_real_; } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Return normalized data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - modelFit <- list( method = "normalizeTumorBoost", flavor = flavor, delta = delta, preserveScale = preserveScale, scaleFactor = sf ); attr(betaTN, "modelFit") <- modelFit; # Sanity check stopifnot(length(betaTN) == J); betaTN; }) # normalizeTumorBoost() ############################################################################ # HISTORY: # 2014-03-30 # o Argument 'preserveScale' for normalizeTumorBoost() is now required. # o Swapped the order of argument 'flavor' and 'preserveScale'. # 2010-09-23 # o CLEANUP: normalizeTumorBoost() now uses which() instead of # whichVector() of 'R.utils'. The former used to be significantly # slower than the latter, but that is no longer the case. # 2010-08-04 # o Added argument 'preserveScale' to normalizeTumorBoost(). # 2010-03-18 # o BUG FIX: For flavors "v2" and "v3" NaN:s could be introduced if betaN # was exactly zero or exactly one. # 2009-07-08 # o Now the arguments are 'betaT', 'betaN' and 'muN'. # o Added an example() with real data. # 2009-07-06 # o Created from process() of TumorBoostNormalization in aroma.cn. # o Added model 'flavor' "v4" which corrects heterozygots according to "v2" # and homozygotes according to "v1". # o Added model 'flavor' "v3". Suggested by PN last night over a Guinness # at the pub after a long day of hard work. # 2009-06-22 # o Added model 'flavor' "v2". # 2009-06-08 # o The constructor of TumorBoostNormalization now only takes an # AromaUnitGenotypeCallSet for argument 'gcN'. It no longer takes an # AromaUnitFracBCnBinarySet object. # 2009-05-17 # o Now the constructor of TumorBoostNormalization asserts that there are # no stray arguments. # 2009-04-29 # o Created. ############################################################################ aroma.light/R/plotMvsA.R0000644000175000017500000000447714136047216014722 0ustar nileshnilesh#########################################################################/** # @RdocGeneric plotMvsA # @alias plotMvsA.matrix # # @title "Plot log-ratios vs log-intensities" # # \description{ # @get "title". # } # # \usage{ # @usage plotMvsA,matrix # } # # \arguments{ # \item{X}{Nx2 @matrix with two channels and N observations.} # \item{Alab,Mlab}{Labels on the x and y axes.} # \item{Alim,Mlim}{Plot range on the A and M axes.} # \item{aspectRatio}{Aspect ratio between \code{Mlim} and \code{Alim}.} # \item{pch}{Plot symbol used.} # \item{...}{Additional arguments accepted by @see "graphics::points".} # \item{add}{If @TRUE, data points are plotted in the current plot, # otherwise a new plot is created.} # } # # \details{ # Red channel is assumed to be in column one and green in column two. # Log-ratio are calculated taking channel one over channel two. # } # # \value{ # Returns nothing. # } # # @author "HB" #*/######################################################################### setMethodS3("plotMvsA", "matrix", function(X, Alab="A", Mlab="M", Alim=c(0,16), Mlim=c(-1,1)*diff(Alim)*aspectRatio, aspectRatio=1, pch=".", ..., add=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': if (ncol(X) != 2) { throw("Argument 'X' must have exactly two columns: ", ncol(X)); } if (!add) { plot(NA, xlab=Alab, ylab=Mlab, xlim=Alim, ylim=Mlim); } # Do not plot (or generate false) M vs A for non-positive signals. X[X <= 0] <- NA; R <- as.double(X[,1]); G <- as.double(X[,2]); M <- log(R/G, base=2); A <- log(R*G, base=2)/2; points(A,M, pch=pch, ...); }) ############################################################################ # HISTORY: # 2011-06-26 # o Added argument 'aspectRatio' to plotMvsA(). It can be used to adjust # the range of the 'Mlim' argument relative to the 'Alim' argument. # 2005-09-06 # o Coercing to doubles to avoid overflow when multiplying to integers. # o Now non-positive signals are excluded. # 2005-06-11 # o BUG FIX: Used 'rg' instead of 'X' in R <- rg[,1] and G <- rg[,2]. # 2005-06-03 # o Created from the normalizeQuantile.matrix.Rex example. ############################################################################ aroma.light/R/distanceBetweenLines.R0000644000175000017500000001001114136047216017231 0ustar nileshnilesh#########################################################################/** # @RdocDefault distanceBetweenLines # # @title "Finds the shortest distance between two lines" # # \description{ # @get "title". # # Consider the two lines # # \eqn{x(s) = a_x + b_x*s} and \eqn{y(t) = a_y + b_y*t} # # in an K-space where the offset and direction @vectors are \eqn{a_x} # and \eqn{b_x} (in \eqn{R^K}) that define the line \eqn{x(s)} # (\eqn{s} is a scalar). Similar for the line \eqn{y(t)}. # This function finds the point \eqn{(s,t)} for which \eqn{|x(s)-x(t)|} # is minimal. # } # # @synopsis # # \arguments{ # \item{ax,bx}{Offset and direction @vector of length K for line \eqn{z_x}.} # \item{ay,by}{Offset and direction @vector of length K for line \eqn{z_y}.} # \item{...}{Not used.} # } # # \value{ # Returns the a @list containing # \item{ax,bx}{The given line \eqn{x(s)}.} # \item{ay,by}{The given line \eqn{y(t)}.} # \item{s,t}{The values of \eqn{s} and \eqn{t} such that # \eqn{|x(s)-y(t)|} is minimal.} # \item{xs,yt}{The values of \eqn{x(s)} and \eqn{y(t)} # at the optimal point \eqn{(s,t)}.} # \item{distance}{The distance between the lines, i.e. \eqn{|x(s)-y(t)|} # at the optimal point \eqn{(s,t)}.} # } # # @author "HB" # # @examples "../incl/distanceBetweenLines.Rex" # # \references{ # [1] M. Bard and D. Himel, \emph{The Minimum Distance Between Two # Lines in n-Space}, September 2001, Advisor Dennis Merino.\cr # [2] Dan Sunday, \emph{Distance between 3D Lines and Segments}, # Jan 2016, \url{https://www.geomalgorithms.com/algorithms.html}.\cr # } # # @keyword "algebra" #*/######################################################################### setMethodS3("distanceBetweenLines", "default", function(ax, bx, ay, by, ...) { if (length(ax) != length(bx)) { stop(sprintf("The length of the offset vector 'ax' (%d) and direction vector 'bx' (%d) are not equal.", length(ax), length(bx))); } if (length(ay) != length(by)) { stop(sprintf("The length of the offset vector 'ay' (%d) and direction vector 'by' (%d) are not equal.", length(ay), length(by))); } if (length(ax) != length(ay)) { stop(sprintf("The line x(s) and y(t) are of different dimensions: %d vs %d", length(ax), length(ay))); } if (length(ax) <= 1) stop(sprintf("The lines must be in two or more dimensions: %d", length(ax))); ax <- as.vector(ax); bx <- as.vector(bx); ay <- as.vector(ay); by <- as.vector(by); if (length(ax) == 2) { # Find (s,t) such that x(s) == y(t) where # x(s) = a_x + b_x*s # y(t) = a_y + b_y*t e <- (ax-ay); f <- e/by; g <- bx/by; s <- (f[2]-f[1])/(g[1]-g[2]); t <- f[1] + g[1]*s; d <- 0; } else { # Consider the two lines in an K-space # x(s) = a_x + b_x*t (line 1) # y(t) = a_y + b_y*s (line 2) # where s and t are scalars and the other vectors in R^K. # Some auxillary calculations A <- sum(bx*bx); B <- 2*(sum(bx*ax)-sum(bx*ay)); C <- 2*sum(bx*by); D <- 2*(sum(by*ay)-sum(by*ax)); E <- sum(by*by); F <- sum(ax*ax) + sum(ay*ay); # Shortest distance between the two lines (points) G <- C^2-4*A*E; d2 <- (B*C*D+B^2*E+C^2*F+A*(D^2-4*E*F))/G; d <- sqrt(d2); # The points that are closest to each other. t <- (2*A*D+B*C)/G; # t is on y(t) s <- (C*t-B)/(2*A); # s is on x(s) } # Get the coordinates of the two points on x(s) and y(t) that # are closest to each other. xs <- ax + bx*s; yt <- ay + by*t; list(ax=ax, bx=bx, ay=ay, by=by, s=s, t=t, xs=xs, yt=yt, distance=d); }) # distanceBetweenLines() ############################################################################ # HISTORY: # 2005-06-03 # o Made into a default method. # 2003-12-29 # o Added Rdoc comments. # o Created by generalizing from formet RGData$fitIWPCA() in com.braju.smax. ############################################################################ aroma.light/R/plotMvsMPairs.R0000644000175000017500000000416014136047216015722 0ustar nileshnilesh#########################################################################/** # @RdocGeneric plotMvsMPairs # @alias plotMvsMPairs.matrix # # @title "Plot log-ratios vs log-ratios for all pairs of columns" # # \description{ # @get "title". # } # # \usage{ # @usage plotMvsMPairs,matrix # } # # \arguments{ # \item{X}{Nx2K @matrix where N is the number of observations and # 2K is an even number of channels.} # \item{xlab,ylab}{Labels on the x and y axes.} # \item{xlim,ylim}{Plot range on the x and y axes.} # \item{pch}{Plot symbol used.} # \item{...}{Additional arguments accepted by @see "graphics::points".} # \item{add}{If @TRUE, data points are plotted in the current plot, # otherwise a new plot is created.} # } # # \details{ # Log-ratio are calculated by over paired columns, e.g. column 1 and 2, # column 3 and 4, and so on. # } # # \value{ # Returns nothing. # } # # @author "HB" #*/######################################################################### setMethodS3("plotMvsMPairs", "matrix", function(X, xlab="M", ylab="M", xlim=c(-1,1)*6, ylim=xlim, pch=".", ..., add=FALSE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument 'X': if (ncol(X)/2 != round(ncol(X)/2)) throw("Argument 'X' must have an even number of columns: ", ncol(X)); if (!add) { plot(NA, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim); } # Do not plot (or generate false) M vs A for non-positive signals. X[X <= 0] <- NA; npairs <- ncol(X)/2; for (kk in npairs-1) { R <- X[,2*kk-1]; G <- X[,2*kk]; M1 <- log(R/G, base=2); R <- X[,2*(kk+1)-1]; G <- X[,2*(kk+1)]; M2 <- log(R/G, base=2); points(M1,M2, pch=pch, ...); } }) ############################################################################ # HISTORY: # 2005-09-06 # o Now non-positive signals are excluded. # 2005-06-11 # o BUG FIX: Used 'rg' instead of 'X'. # 2005-06-03 # o Created from the normalizeQuantile.matrix.Rex example. ############################################################################ aroma.light/R/fitXYCurve.R0000644000175000017500000001631514136047216015217 0ustar nileshnilesh#########################################################################/** # @RdocGeneric fitXYCurve # @alias fitXYCurve.matrix # @alias backtransformXYCurve # @alias backtransformXYCurve.matrix # # @title "Fitting a smooth curve through paired (x,y) data" # # \description{ # @get "title". # } # # \usage{ # @usage fitXYCurve,matrix # } # # \arguments{ # \item{X}{An Nx2 @matrix where the columns represent the two channels # to be normalized.} # \item{weights}{If @NULL, non-weighted normalization is done. # If data-point weights are used, this should be a @vector of length # N of data point weights used when estimating the normalization # function. # } # \item{typeOfWeights}{A @character string specifying the type of # weights given in argument \code{weights}. # } # \item{method}{@character string specifying which method to use when # fitting the intensity-dependent function. # Supported methods: # \code{"loess"} (better than lowess), # \code{"lowess"} (classic; supports only zero-one weights), # \code{"spline"} (more robust than lowess at lower and upper # intensities; supports only zero-one weights), # \code{"robustSpline"} (better than spline). # } # \item{bandwidth}{A @double value specifying the bandwidth of the # estimator used. # } # \item{satSignal}{Signals equal to or above this threshold will not # be used in the fitting. # } # \item{...}{Not used.} # } # # \value{ # A named @list structure of class \code{XYCurve}. # } # # \section{Missing values}{ # The estimation of the function will only be made based on complete # non-saturated observations, i.e. observations that contains no @NA # values nor saturated values as defined by \code{satSignal}. # } # # \section{Weighted normalization}{ # Each data point, that is, each row in \code{X}, which is a # vector of length 2, can be assigned a weight in [0,1] specifying how much # it should \emph{affect the fitting of the normalization function}. # Weights are given by argument \code{weights}, which should be a @numeric # @vector of length N. # # Note that the lowess and the spline method only support zero-one # \{0,1\} weights. # For such methods, all weights that are less than a half are set to zero. # } # # \section{Details on loess}{ # For @see "stats::loess", the arguments \code{family="symmetric"}, # \code{degree=1}, \code{span=3/4}, # \code{control=loess.control(trace.hat="approximate"}, # \code{iterations=5}, \code{surface="direct")} are used. # } # # @author "HB" # # \examples{ # @include "../incl/fitXYCurve.matrix.Rex" # } #*/######################################################################### setMethodS3("fitXYCurve", "matrix", function(X, weights=NULL, typeOfWeights=c("datapoint"), method=c("loess", "lowess", "spline", "robustSpline"), bandwidth=NULL, satSignal=2^16-1, ...) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Verify the arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Argument: 'X' if (ncol(X) != 2) { stop("Curve-fit normalization requires two channels only: ", ncol(X)); } if (nrow(X) < 3) { stop("Curve-fit normalization requires at least three observations: ", nrow(X)); } # Argument: 'satSignal' if (satSignal < 0) { stop("Argument 'satSignal' is negative: ", satSignal); } # Argument: 'method' method <- match.arg(method); zeroOneWeightsOnly <- (method %in% c("lowess", "spline")); # Argument: 'typeOfWeights' typeOfWeights <- match.arg(typeOfWeights); # Argument: 'weights' datapointWeights <- NULL; if (!is.null(weights)) { # If 'weights' is an object of a class with as.double(), cast it. weights <- as.double(weights); if (anyMissing(weights)) stop("Argument 'weights' must not contain NA values."); if (any(weights < 0 | weights > 1)) { stop("Argument 'weights' out of range [0,1]: ", paste(weights[weights < 0.0 | weights > 1.0], collapse=", ")); } if (zeroOneWeightsOnly && any(weights > 0 & weights < 1)) { weights <- round(weights); warning("Weights were rounded to {0,1} since '", method, "' normalization supports only zero-one weights."); } weights <- as.vector(weights); if (length(weights) == 1) { weights <- rep(weights, length.out=nrow(X)); } else if (length(weights) != nrow(X)) { stop("Argument 'weights' does not have the same length as the number of data points (rows) in the matrix: ", length(weights), " != ", nrow(X)); } datapointWeights <- weights; } # if (!is.null(weights)) # Argument: 'bandwidth' if (is.null(bandwidth)) { bandwidths <- c("loess"=0.75, "lowess"=0.3, "robustSpline"=0.75, "spline"=0.75); bandwidth <- bandwidths[method]; } else if (!is.numeric(bandwidth) || bandwidth <= 0 || bandwidth > 1) { stop("Argument 'bandwidth' must be in [0,1): ", bandwidth); } else if (length(bandwidth) != 1) { stop("Argument 'bandwidth' must be a scalar: ", paste(bandwidth, collapse=", ")); } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Prepare data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Use only positive non-saturated observations to estimate the # normalization function isValid <- (is.finite(X) & (X <= satSignal)); isValid <- (isValid[,1] & isValid[,2]); X <- X[isValid,]; x <- X[,1,drop=TRUE]; y <- X[,2,drop=TRUE]; if (!is.null(datapointWeights)) { datapointWeights <- datapointWeights[isValid]; } # Not needed anymore X <- isValid <- NULL; # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. Fit the curve # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (method == "lowess") { keep <- if (!is.null(datapointWeights)) (datapointWeights > 0) else TRUE; x <- x[keep]; y <- y[keep]; # Not needed anymore keep <- NULL; fit <- lowess(x=x, y=y, f=bandwidth, ...); fit$predictY <- function(x) approx(fit, xout=x, ties=mean)$y; } else if (method == "loess") { fit <- loess(formula=y ~ x, weights=datapointWeights, family="symmetric", degree=1, span=bandwidth, control=loess.control(trace.hat="approximate", iterations=5, surface="direct"), ...); fit$predictY <- function(x) predict(fit, newdata=x); } else if (method == "spline") { keep <- if (!is.null(datapointWeights)) (datapointWeights > 0) else TRUE; x <- x[keep]; y <- y[keep]; # Not needed anymore keep <- NULL; fit <- smooth.spline(x=x, y=y, spar=bandwidth, ...); fit$predictY <- function(x) predict(fit, x=x)$y; } else if (method == "robustSpline") { fit <- robustSmoothSpline(x=x, y=y, w=datapointWeights, spar=bandwidth, ...); fit$predictY <- function(x) predict(fit, x=x)$y; } class(fit) <- c("XYCurveFit", class(fit)); fit; }) # fitXYCurve() ############################################################################ # HISTORY: # 2013-10-08 # o DOCUMENTATION: Added backtransformXYCurve() alias to this help page. # 2013-09-26 # o Now utilizing anyMissing(). # 2009-07-15 # o Created from normalizeCurveFit.R. ############################################################################ aroma.light/R/backtransformAffine.R0000644000175000017500000001446114136047216017114 0ustar nileshnilesh#########################################################################/** # @RdocGeneric backtransformAffine # @alias backtransformAffine.matrix # # @title "Reverse affine transformation" # # \description{ # @get "title". # } # # \usage{ # @usage backtransformAffine,matrix # } # # \arguments{ # \item{X}{An NxK @matrix containing data to be backtransformed.} # \item{a}{A scalar, @vector, a @matrix, or a @list. # First, if a @list, it is assumed to contained the elements \code{a} # and \code{b}, which are the used as if they were passed as separate # arguments. # If a @vector, a matrix of size NxK is created which is then filled # \emph{row by row} with the values in the vector. Commonly, the # vector is of length K, which means that the matrix will consist of # copies of this vector stacked on top of each other. # If a @matrix, a matrix of size NxK is created which is then filled # \emph{column by column} with the values in the matrix (collected # column by column. Commonly, the matrix is of size NxK, or NxL with # L < K and then the resulting matrix consists of copies sitting # next to each other. # The resulting NxK matrix is subtracted from the NxK matrix \code{X}. # } # \item{b}{A scalar, @vector, a @matrix. # A NxK matrix is created from this argument. For details see # argument \code{a}. # The NxK matrix \code{X-a} is divided by the resulting NxK matrix. # } # \item{project}{ # returned (K values per data point are returned). # If @TRUE, the backtransformed values "\code{(X-a)/b}" are projected # onto the line L(a,b) so that all columns # will be identical. # } # \item{...}{Not used.} # } # # \value{ # The "\code{(X-a)/b}" backtransformed NxK @matrix is returned. # If \code{project} is @TRUE, an Nx1 @matrix is returned, because # all columns are identical anyway. # } # # \section{Missing values}{ # Missing values remain missing values. If projected, data points that # contain missing values are projected without these. # } # # @examples "../incl/backtransformAffine.matrix.Rex" # #*/######################################################################### setMethodS3("backtransformAffine", "matrix", function(X, a=NULL, b=NULL, project=FALSE, ...) { # Dimensions of 'X' nobs <- nrow(X); ndims <- ncol(X); if (ndims == 1L) { stop("Can not fit affine multiscan model. Matrix must contain at least two columns (scans): ", ndims); } # If argument 'a' is a list assume it contains the elements 'a' and 'b'. if (is.list(a)) { b <- a$b; a <- a$a; } # If 'a' and/or 'b' are vector convert them to row matrices. if (is.vector(a)) { # Create a full matrix and filled row by row with 'a' a <- matrix(a, nrow=nobs, ncol=ndims, byrow=TRUE); } else if (is.matrix(a)) { # Create a full matrix and filled column by column by the columns in 'a' t <- a; a <- matrix(NA_real_, nrow=nobs, ncol=ndims); for (cc in 1:ndims) { # Loop over the columns in a0 too. col <- ((cc-1) %% ncol(t)) + 1L; value <- rep(t[,col], length.out=nobs); a[,cc] <- value; } # Not needed anymore t <- NULL; } else if (!is.null(a)) { stop(paste("Unknown data type of argument 'a':", class(a)[1])); } if (!project) { if (is.vector(b)) { # Create a full matrix and filled row by row with 'b' b <- matrix(b, nrow=nobs, ncol=ndims, byrow=TRUE); } else if (is.matrix(b)) { # Create a full matrix and filled column by column by the columns in 'b' t <- b; b <- matrix(NA_real_, nrow=nobs, ncol=ndims); for (cc in 1:ndims) { # Loop over the columns in a0 too. col <- ((cc-1) %% ncol(t)) + 1L; value <- rep(t[,col], length.out=nobs); b[,cc] <- value; } } else if (!is.null(b)) { stop(paste("Unknown data type of argument 'b':", class(b)[1])); } } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Subtract the bias and rescale # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if (!is.null(a)) X <- X - a; ######################################################################## # Alternative 2 # # i) Translate the fitted line L to L' such that L' goes through # (a,a,...,a) and project the data y onto L' to obtain ytilde # ii) from which we can calculate xtilde = (ytilde - a) / b # iii) Since all xtilde are the same take the first component to be our # estimate xhat in y = a + b*xhat. ######################################################################## if (project) { # In theory: # ytilde <- projectUontoV(y-a,b) + a; # xtilde <- (ytilde-a)/b; # In practice: X <- t(X); # 'b' standardized to a unit vector such that == 1. v <- b / sqrt(sum(b^2)); # projectUontoV(): U should be an NxK matrix and v an N vector. X <- projectUontoV(X,v, na.rm=TRUE); X <- X[1,] / b[1]; # Note that here 'b' is a vector! } else { if (!is.null(b)) X <- X / b; # Note that here 'b' is a matrix! } as.matrix(X); }) # backtransformAffine() ############################################################################ # HISTORY: # 2011-04-12 # o Now using as.double(NA) instead of NA. # 2006-06-03 # o Minor to merge two different threads of this code. # o Method passes the tests in the example code (again). # 2005-01-24 # o Now missing values are excluded before projection. # 2005-01-08 # o Now, if project is TRUE, only one column is returned. # o Now the method is guaranteed to return a matrix by calling as.matrix(). # o The average of projected data is now no the same scale as the average on # non-projected data. Before the data was max(b) times too large. # o Added argument 'project' to the Rdoc comments. # o Added test in example to assert that the same matrix is returned if # projection on an identity transformation is applied. # 2004-06-28 # o BUG FIX: Applied projection when project was FALSE and vice versa. # Projection did not work because 'b' was expanded into a full matrix. # Now this is only done if project == FALSE. # 2004-05-14 # o Created. Extracted and generalize code from calibrateMultiscan(), # normalizeAffine() and calibrateMultiscanSpatial(). ############################################################################ aroma.light/R/medianPolish.R0000644000175000017500000001261314136047216015560 0ustar nileshnilesh#########################################################################/** # @RdocGeneric medianPolish # @alias medianPolish.matrix # # @title "Median polish" # # \description{ # @get "title". # } # # \usage{ # @usage medianPolish,matrix # } # # \arguments{ # \item{X}{N-times-K @matrix} # \item{tol}{A @numeric value greater than zero used as a threshold # to identify when the algorithm has converged.} # \item{maxIter}{Maximum number of iterations.} # \item{na.rm}{If @TRUE (@FALSE), @NAs are exclude (not exclude). # If @NA, it is assumed that \code{X} contains no @NA values.} # \item{.addExtra}{If @TRUE, the name of argument \code{X} is returned # and the returned structure is assigned a class. This will make the # result compatible what @see "stats::medpolish" returns.} # \item{...}{Not used.} # } # # \value{ # Returns a named @list structure with elements: # \item{overall}{The fitted constant term.} # \item{row}{The fitted row effect.} # \item{col}{The fitted column effect.} # \item{residuals}{The residuals.} # \item{converged}{If @TRUE, the algorithm converged, otherwise not.} # } # # \details{ # The implementation of this method give identical estimates as # @see "stats::medpolish", but is about 3-5 times more efficient when # there are no @NA values. # } # # @author "HB" # # @examples "../incl/medianPolish.matrix.Rex" # # \seealso{ # @see "stats::medpolish". # } # # @keyword algebra #*/######################################################################### setMethodS3("medianPolish", "matrix", function(X, tol=0.01, maxIter=10L, na.rm=NA, ..., .addExtra=TRUE) { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Local functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ns <- getNamespace("matrixStats") .psortKM <- get(".psortKM", mode="function", envir=ns); dim <- dim(X); nrow <- dim[1L]; ncol <- dim[2L]; if (.addExtra) { name <- deparse(substitute(X)); } # Overall effects t <- 0; # Row effects r <- vector("double", length=nrow); # Column effects c <- vector("double", length=ncol); hasNa <- (!is.na(na.rm) && anyMissing(X)); if (hasNa) { oldSum <- 0; for (ii in 1:maxIter) { # Fit the row effects rdelta <- rowMedians(X, na.rm=na.rm); X <- X - rdelta; r <- r + rdelta; # Fit the overall effects delta <- median(c, na.rm=na.rm); c <- c - delta; t <- t + delta; # Fit the column effects cdelta <- colMedians(X, na.rm=na.rm); X <- X - matrix(cdelta, nrow=nrow, ncol=ncol, byrow=TRUE); c <- c + cdelta; # Fit the overall effects delta <- median(r, na.rm=na.rm); r <- r - delta; t <- t + delta; # Fit the overall effects newSum <- sum(abs(X), na.rm=na.rm); converged <- (newSum == 0 || abs(newSum - oldSum) < tol * newSum); if (converged) break; oldSum <- newSum; } # for (ii ...) } else { # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Optimized code for the case where there are no NAs # # Compared to medpolish(..., na.rm=FALSE), this version is about # 3-4 times faster. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rhalf <- (nrow+1)/2; if (nrow %% 2 == 1) { # Get x(rhalf), where x(k) is k:th sorted value in x. rMedian <- function(x) .psortKM(x, k=rhalf); } else { # Average x(rhalf) and x(rhalf+1). rMedian <- function(x) sum(.psortKM(x, k=rhalf+1L, m=2L))/2; } chalf <- (ncol+1)/2; if (ncol %% 2 == 1) { # Get x(chalf), where x(k) is k:th sorted value in x. cMedian <- function(x) .psortKM(x, k=chalf); } else { # Average x(chalf) and x(chalf+1). cMedian <- function(x) sum(.psortKM(x, k=chalf+1L, m=2L))/2; } oldSum <- 0; for (ii in 1:maxIter) { # Fit the row effects rdelta <- apply(X, MARGIN=1L, FUN=cMedian); X <- X - rdelta; r <- r + rdelta; # Fit the overall effects delta <- cMedian(c); c <- c - delta; t <- t + delta; # Fit the column effects cdelta <- apply(X, MARGIN=2L, FUN=rMedian); X <- X - matrix(cdelta, nrow=nrow, ncol=ncol, byrow=TRUE); c <- c + cdelta; # Fit the overall effects delta <- rMedian(r); r <- r - delta; t <- t + delta; # Fit the overall effects newSum <- sum(abs(X), na.rm=FALSE); converged <- (newSum == 0 || abs(newSum - oldSum) < tol * newSum); if (converged) break; oldSum <- newSum; } # for (ii ...) } res <- list(overall=t, row=r, col=c, residuals=X, converged=converged); if (.addExtra) { res$name <- name; class(res) <- c("medianPolish", "medpolish"); } res; }) # medianPolish() ############################################################################ # HISTORY: # 2013-09-26 # o CLEANUP: No longer utilizes ':::'. # o SPEEDUP: Now utilizing anyMissing() and (col|row)Medians() of the # 'matrixStats' package. # o TWEAKS: Using integer (e.g. 1L) where possible # 2012-09-12 # o ROBUSTNESS: Replaced an .Internal(psort(...)) call with a call to # matrixStats:::.psortKM() in medianPolish(). # 2012-04-16 # o Added local function psortGet() to medianPolish(). # 2006-05-16 # o Created from stats::medpolish(). ############################################################################ aroma.light/R/sampleTuples.R0000644000175000017500000000331514136047216015621 0ustar nileshnilesh#########################################################################/** # @RdocDefault sampleTuples # # @title "Sample tuples of elements from a set" # # \description{ # @get "title". # The elements within a sampled tuple are unique, i.e. no two elements # are the same. # } # # @synopsis # # \arguments{ # \item{x}{A set of elements to sample from.} # \item{size}{The number of tuples to sample.} # \item{length}{The length of each tuple.} # \item{...}{Additional arguments passed to @see "base::sample".} # } # # \value{ # Returns a NxK @matrix where N = \code{size} and K = \code{length}. # } # # @author "HB" # # @examples "../incl/sampleTuples.Rex" # # \seealso{ # @see "base::sample". # } # # @keyword utilities #*/######################################################################### setMethodS3("sampleTuples", "default", function(x, size, length, ...) { # Argument 'x': if (length(x) < 1L) throw("Argument 'x' must be a vector of length one or greater."); # Argument 'size': if (size < 0) throw("Argument 'size' must be a non-negative integer: ", size); # Argument 'length': if (length < 1) throw("Argument 'length' must be one or greater: ", length); # Sample tuples naValue <- NA; storage.mode(naValue) <- storage.mode(x); tuples <- matrix(naValue, nrow=size, ncol=length); for (kk in seq_len(size)) { tuples[kk,] <- sample(x, size=length, ...); } tuples; }) ############################################################################ # HISTORY: # 2011-04-12 # o Now using NAs of the correct storage type. # 2005-07-25 # o Created generic sampleTuples(). # 2005-04-07 # o Created. ############################################################################ aroma.light/R/lines.XYCurveFit.R0000644000175000017500000000072714136047216016270 0ustar nileshnileshsetMethodS3("lines", "XYCurveFit", function(x, xNew=NULL, ...) { # To please R CMD check fit <- x; if (is.null(xNew)) { xNew <- fit$x; xNew <- sort(xNew); xNew <- xNew[!duplicated(xNew)]; } y <- fit$predictY(xNew); lines(x=xNew, y=y, ...); }) # lines() ############################################################################ # HISTORY: # 2009-07-15 # o Created. ############################################################################ aroma.light/R/normalizeQuantileRank.matrix.R0000644000175000017500000002374614136047216020777 0ustar nileshnilesh###########################################################################/** # @set "class=matrix" # @RdocMethod normalizeQuantileRank # # @title "Normalizes the empirical distribution of a set of samples to a common target distribution" # # \usage{ # @usage normalizeQuantileRank,matrix # } # # \description{ # @get "title". # # The average sample distribution is calculated either robustly or not # by utilizing either \code{weightedMedian()} or \code{weighted.mean()}. # A weighted method is used if any of the weights are different from one. # } # # \arguments{ # \item{X}{a numerical NxK @matrix with the K columns representing the # channels and the N rows representing the data points.} # \item{robust}{If @TRUE, the (weighted) median function is used for # calculating the average sample distribution, otherwise the # (weighted) mean function is used.} # \item{ties}{Should ties in \code{x} be treated with care or not? # For more details, see "limma:normalizeQuantiles".} # \item{weights}{If @NULL, non-weighted normalization is done. # If channel weights, this should be a @vector of length K specifying # the weights for each channel. # If signal weights, it should be an NxK @matrix specifying the # weights for each signal. # } # \item{typeOfWeights}{A @character string specifying the type of # weights given in argument \code{weights}.} # \item{...}{Not used.} # } # # \value{ # Returns an object of the same shape as the input argument. # } # # \section{Missing values}{ # Missing values are excluded when estimating the "common" (the baseline). # Values that are @NA remain @NA after normalization. # No new @NAs are introduced. # } # # \section{Weights}{ # Currently only channel weights are support due to the way quantile # normalization is done. # If signal weights are given, channel weights are calculated from these # by taking the mean of the signal weights in each channel. # } # # @examples "../incl/normalizeQuantileRank.matrix.Rex" # # \author{ # Adopted from Gordon Smyth (\url{http://www.statsci.org/}) in 2002 \& 2006. # Original code by Ben Bolstad at Statistics Department, University of # California. # Support for calculating the average sample distribution using (weighted) # mean or median was added by Henrik Bengtsson. # } # # \seealso{ # @see "stats::median", @see "matrixStats::weightedMedian", # @see "base::mean" and @see "stats::weighted.mean". # @see "normalizeQuantileSpline". # } # # @keyword "nonparametric" # @keyword "multivariate" # @keyword "robust" #*/########################################################################### setMethodS3("normalizeQuantileRank", "matrix", function(X, ties=FALSE, robust=FALSE, weights=NULL, typeOfWeights=c("channel", "signal"), ...) { zeroOneWeightsOnly <- TRUE; # Until supported otherwise. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Validate arguments # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nbrOfChannels <- ncol(X); if(nbrOfChannels == 1L) return(X); nbrOfObservations <- nrow(X); if(nbrOfObservations == 1L) return(X); # Argument 'typeOfWeights': typeOfWeights <- match.arg(typeOfWeights); # Argument 'weights': channelWeights <- NULL; signalWeights <- NULL; if (!is.null(weights)) { # If 'weights' is an object of a class with as.double(), cast it. dim <- dim(weights); weights <- as.double(weights); dim(weights) <- dim; if (anyMissing(weights)) stop("Argument 'weights' must not contain NA values."); if (any(weights < 0 | weights > 1)) { stop("Argument 'weights' out of range [0,1]: ", paste(weights[weights < 0.0 | weights > 1.0], collapse=", ")); } if (typeOfWeights == "channel") { if (length(weights) == 1L) { weights <- rep(weights, length.out=nbrOfObservations); } else if (length(weights) != nbrOfObservations) { stop("Argument 'weights' (channel weights) does not have the same length as the number of rows in the matrix: ", length(weights), " != ", nbrOfChannels); } channelWeights <- weights; } else if (typeOfWeights == "signal") { if (!identical(dim(weights), dim(X))) { stop("Dimension of argument 'weights' (signal weights) does not match dimension of argument 'X': (", paste(dim(weights), collapse=","), ") != (", paste(dim(X), collapse=","), ")"); } # Calculate channel weights channelWeights <- colMeans(weights); if (zeroOneWeightsOnly && any(weights > 0 & weights < 1)) { weights <- round(weights); warning("Weights were rounded to {0,1} since quantile normalization supports only zero-one weights."); } signalWeights <- weights; } } # if (!is.null(weights)) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 0. Setup # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - maxNbrOfObservations <- nbrOfObservations; # Create a list S to hold the sorted values for each channels S <- matrix(NA_real_, nrow=maxNbrOfObservations, ncol=nbrOfChannels); # Create a list O to hold the ordered indices for each channels O <- vector("list", length=nbrOfChannels); # A vector specifying the number of observations in each column nbrOfFiniteObservations <- rep(maxNbrOfObservations, times=nbrOfChannels); # Construct the sample quantiles quantiles <- (0:(maxNbrOfObservations-1L))/(maxNbrOfObservations-1L); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 1. Get the sample quantile for all channels (columns) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (cc in seq_len(nbrOfChannels)) { values <- X[,cc,drop=TRUE]; if (!is.null(signalWeights)) { values[signalWeights[,cc] == 0] <- NA; } # Order and sort the values Scc <- sort(values, index.return=TRUE); # The number of non-NA observations nobs <- length(Scc$x); # Has NAs? if(nobs < nbrOfObservations) { nbrOfFiniteObservations[cc] <- nobs; isOk <- !is.na(values); # Get the sample quantiles for those values bins <- (0:(nobs-1L))/(nobs-1L); # Record the order position for these values. O[[cc]] <- ((1:nbrOfObservations)[isOk])[Scc$ix]; # Interpolate to get the values at positions specified by # 'quantile' using data points given by 'bins' and 'Scc$x'. Scc <- approx(x=bins, y=Scc$x, xout=quantiles, ties="ordered")$y; } else { O[[cc]] <- Scc$ix; Scc <- Scc$x; } S[,cc] <- Scc; } # for (cc ...) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 2. Calculate the average sample distribution, of each quantile # across all columns. This can be done robustly or not and # with weights or not. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - useWeighted <- (!is.null(channelWeights) & any(channelWeights != 1)); if (robust) { if (useWeighted) { xTarget <- rowWeightedMedians(S, w=channelWeights, na.rm=TRUE); } else { xTarget <- rowMedians(S, na.rm=TRUE); } } else { if (useWeighted) { xTarget <- rowWeightedMeans(S, w=channelWeights, na.rm=TRUE); } else { xTarget <- rowMeans(S, na.rm=TRUE); } } # Assert that xTarget is of then same length as number of observations stopifnot(length(xTarget) == maxNbrOfObservations); # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 3. For all columns, get for each sample quantile the value of # average sample distribution at that quantile. # # Input: X[r,c], xTarget[r], O[[c]][r], nbrOfFiniteObservations[c]. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - for (cc in seq_len(nbrOfChannels)) { # Get the number of non-NA observations nobs <- nbrOfFiniteObservations[cc]; # Has NAs? if(nobs < nbrOfObservations) { # Get the NAs isOk <- !is.na(X[,cc]); # Get the sample quantiles for those values if (ties) { r <- rank(X[isOk,cc]); bins <- (r-1)/(nobs-1L); } else { bins <- (0:(nobs-1L))/(nobs-1L); } # Interpolate to get the m's at positions specified by # 'quantile' using data points given by 'bins' and 'xTarget'. if (ties) { idx <- isOk; } else { idx <- O[[cc]]; } X[idx,cc] <- approx(x=quantiles, y=xTarget, xout=bins, ties="ordered")$y; } else { if (ties) { r <- rank(X[,cc]); bins <- (r-1L)/(nobs-1L); X[,cc] <- approx(x=quantiles, y=xTarget, xout=bins, ties="ordered")$y; } else { X[O[[cc]],cc] <- xTarget; } } } # for (cc ...) X; }) ############################################################################## # HISTORY: # 2013-09-26 # o Now utilizing anyMissing(), rowMedians(), rowWeightedMedians(), and # rowWeightedMeans(). # 2011-04-12 # o Now using as.double(NA) instead of NA. # 2008-04-14 # o Renamed normalizeQuantile() to normalizeQuantileRank(). Keeping the old # name for backward compatibility. # 2006-05-12 # o Updated according to Gordon Smyth's package. # 2006-02-08 # o Rd bug fix: Fixed broken links to help for median() and weighted.mean(). # 2005-06-03 # o Replaced arguments 'channelWeights' and 'signalWeights' with 'weights' # and 'typeOfWeights'. # o Added Rdoc help on weights. # 2005-03-23 # o Updated normalizeQuantile() so that approx() does not give warnings # about 'Collapsing to unique x values' when doing lowess normalization. # 2005-02-02 # o Zero-one weights are now round off by round(w). # 2005-02-01 # o Added argument 'signalWeights'. # o Added validation of argument 'channelWeights'. # 2005-01-27 # o Renamed argument 'A' to 'X'. # o Renamed argument 'weights' to 'channelWeights'. # o Making use of setMethodS3(). Added some more Rdoc comments. # o Moved from R.basic to aroma. # 2003-04-13 # o Updated the Rdoc comments. # 2002-10-24 # o Adapted from source code by Gordon Smyth's smagks package. ############################################################################## aroma.light/NEWS0000644000175000017500000007263114136047216013325 0ustar nileshnileshPackage: aroma.light ==================== Version: 3.23.1 [2021-08-19] DOCUMENTATION: * Update several citation URLs that were either broken or redirects elsewhere. Version: 3.22.0 [2021-05-19] * The version number was bumped for the Bioconductor release version, which is now BioC 3.13 for R (>= 4.0.3). Version: 3.20.0 [2020-10-27] * The version number was bumped for the Bioconductor release version, which is now BioC 3.12 for R (>= 4.0.0). Version: 3.18.0 [2020-04-27] * The version number was bumped for the Bioconductor release version, which is now BioC 3.11 for R (>= 3.6.1). Version: 3.17.1 [2019-12-09] CODE REFACTORING: * Now importing throw() from R.oo instead of R.methodsS3. Version: 3.17.0 [2019-10-30] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.11 for R (>= 3.6.1). Version: 3.16.0 [2019-10-30] * The version number was bumped for the Bioconductor release version, which is now BioC 3.10 for R (>= 3.6.1). Version: 3.15.1 [2019-08-28] BUG FIXES: * wpca() for matrices had a 'length > 1 in coercion to logical' bug. Version: 3.15.0 [2019-05-02] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.10 for R (>= 3.6.0). Version: 3.14.0 [2019-05-02] * The version number was bumped for the Bioconductor release version, which is now BioC 3.9 for R (>= 3.6.0). Version: 3.13.0 [2018-10-30] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.9 for R (>= 3.6.0). Version: 3.12.0 [2018-10-30] * The version number was bumped for the Bioconductor release version, which is now BioC 3.8 for R (>= 3.5.0). Version: 3.11.2 [2018-09-04] CODE REFACTORING: * fitPrincipalCurve() now requires princurve (>= 2.1.2) and was updated to make use of new principcal_curve class instead of deprecated principcal.curve class. This update "should not" affect the results, but see https://github.com/dynverse/princurve/issues/8 for information of what has changed in the princurve package in this respect. Version: 3.11.1 [2018-08-28] * Updated installation instructions in README.md. Version: 3.11.0 [2018-04-30] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.8 for R (>= 3.6.0). Version: 3.10.0 [2018-04-30] * The version number was bumped for the Bioconductor release version, which is now BioC 3.7 for R (>= 3.5.0). Version: 3.9.1 [2017-12-19] NEW FEATURES: * robustSmoothSpline() now supports using Tukey's biweight (in addition to already exising L1) estimators. See argument 'method'. Thanks to Aaron Lun at the Cancer Research UK Cambridge Institute for adding this feature. Version: 3.9.0 [2017-10-30] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.7 for R (>= 3.5.0). Version: 3.8.0 [2017-10-30] * The version number was bumped for the Bioconductor release version, which is now BioC 3.6 for R (>= 3.4.0). Version: 3.7.0 [2017-04-23] * The version number was bumped for the Bioconductor devel version, which is now BioC 3.6 for R (>= 3.4.0). Version: 3.6.0 [2017-04-23] * The version number was bumped for the Bioconductor release version, which is now BioC 3.5 for R (>= 3.4.0). Version: 3.5.1 [2017-04-14] SIGNIFICANT CHANGES: * robustSmoothSpline() uses a re-weighted re-iterative algorithm that fits a smooth spline using stats::smooth.spline(), calculates the residuals and which are used to fit a re-weighted smooth spline and so on until converence. Due to updates to stats::smooth.spline() in R (>= 3.4.0) it is no longer feasible to maintain a highly optimized version of the algorithm, because it was based on internal stats::smooth.spline() code that has no completely changed. Instead the re-iterative algorithm calls stats::smooth.spline() as is, which slows it down. More importantly, it will now give slightly different estimates. SOFTWARE QUALITY: * In addition to continous integration (CI) tests and nightly Bioconductor tests, the package is now also tested regularly against all reverse package depencies available on CRAN and Bioconductor. Version: 3.5.0 [2016-10-18] * The version number was bumped for the Bioconductor devel version, which is now BioC v3.5 for R (>= 3.4.0). Version: 3.4.0 [2016-10-18] * The version number was bumped for the Bioconductor release version, which is now BioC v3.4 for R (>= 3.3.1). Version: 3.3.2 [2016-09-16] CODE REFACTORING: * Using NA_real_ (not just NA) everywhere applicable. BUG FIXES: * robustSmoothSpline() gave an error since R-devel (>= 3.4.0 r70682). Version: 3.3.1 [2016-08-10] CODE REFACTORING: * CLEANUP: Using seq_len() and seq_along() everywhere (Issue #8) Version: 3.3.0 [2016-05-03] * The version number was bumped for the Bioconductor devel version, which is now BioC v3.4 for R (>= 3.3.0). Version: 3.2.0 [2016-05-03] * The version number was bumped for the Bioconductor release version, which is now BioC v3.3 for R (>= 3.3.0). Version: 3.1.1 [2016-01-06] * Package requires R (>= 2.15.2). CODE REFACTORING: * CLEANUP: robustSmoothSpline() no longer generates messages that ".nknots.smspl() is now exported; use it instead of n.knots()" for R (>= 3.1.1). Version: 3.1.0 [2015-10-23] * The version number was bumped for the Bioconductor devel version, which is now BioC v3.3 for R (>= 3.3.0). Version: 3.0.0 [2015-10-13] * The version number was bumped for the Bioconductor release version, which is now BioC v3.2 for R (>= 3.2.2). Version: 2.99.0 [2015-10-06] * No changes. Version: 2.9.0 [2015-09-17] SIGNIFICANT CHANGES: * Argument 'preserveScale' for normalizeTumorBoost() now defaults to FALSE. Since 1.33.3 (2014-04-30) it had no default and prior to that it was TRUE. Version: 2.5.3 [2015-09-13] SOFTWARE QUALITY: * ROBUSTNESS: Explicitly importing core R functions. BUG FIXES: * rowAverages() and normalizeAverages() would give an error if some of the argument default functions are overridden by non-functions of the same name in the calling environment. Version: 2.5.2 [2015-06-16] SOFTWARE QUALITY: * Relaxed package test for backtransformPrincipalCurve(). Version: 2.5.1 [2015-05-24] * Bumped package dependencies. DEPRECATED AND DEFUNCT: * CLEANUP: Removed obsolete wpca(..., method = "dsvdc"); was only needed for backward compatibility with R (< 1.7.0). Version: 2.5.0 [2015-04-16] * The version number was bumped for the Bioconductor devel version, which is now BioC v3.2 for R (>= 3.3.0). Version: 2.4.0 [2015-04-16] * The version number was bumped for the Bioconductor release version, which is now BioC v3.1 for R (>= 3.2.0). Version: 2.3.3 [2015-02-18] NEW FEATURES: * If a value of argument 'xlim' or 'ylim' for plotDensity() is NA, then it defaults to the corresponding extreme value of the data, e.g. plotDensity(x, xlim = c(0, NA)). Version: 2.3.2 [2015-02-17] SOFTWARE QUALITY: * ROBUSTNESS: Added package tests. Code coverage is 76%. CODE REFACTORING: * CLEANUP: Using requestNamespace() instead of request(). Version: 2.3.1 [2014-12-08] * Same updates as in 2.2.1. Version: 2.3.0 [2014-10-13] * The version number was bumped for the Bioconductor devel version, which is now BioC v3.1 for R (>= 3.2.0). Version: 2.2.1 [2014-12-08] CODE REFACTORING: * Minor code cleanup. Version: 2.2.0 [2014-10-13] * The version number was bumped for the Bioconductor release version, which is now BioC v3.0 for R (>= 3.1.1). Version: 2.1.2 [2014-09-23] * Minor tweaks due to the move to GitHub. Version: 2.1.1 [2014-09-16] SOFTWARE QUALITY: * Fixed some new R CMD check NOTEs. CODE REFACTORING: * CLEANUP: Now importing R.utils (instead of only suggesting it). * IMPORTANT/CLEANUP: The matrixStats package is no longer attached with this package. In other words, you now might have to add library('matrixStats') to your scripts. Version: 2.1.0 [2014-04-11] * The version number was bumped for the Bioconductor devel version, which is now BioC v2.15 for R (>= 3.1.0). Version: 2.0.0 [2014-04-11] * The version number was bumped for the Bioconductor release version, which is now BioC v2.14 for R (>= 3.1.0). Version: 1.99.3 [2014-03-31] * Bumped the version such that the next release will be 2.0.0. Version: 1.33.3 [2014-03-30] SIGNIFICANT CHANGES: * Argument 'preserveScale' for normalizeTumorBoost() is now required. The goal with this is to in a future version migrate to use preserveScale = FALSE as the default (was preserveScale = TRUE) in order to avoid introducing a a global bias in the tumor allele B fraction of heterozygous SNPs. The examples use preserveScale = FALSE now. NEW FEATURES: * Added pairedAlleleSpecificCopyNumbers(). Version: 1.33.2 [2014-03-25] NEW FEATURES: * Now plotDensity() supports weights via argument 'W'. Version: 1.33.1 [2014-03-25] NEW FEATURES: * Now plotDensity() also supports density() objects. CODE REFACTORING: * CLEANUP: robustSmoothSpline() no longer uses DUP = FALSE in an internal .Fortran() call. * Bumped up package dependencies. Version: 1.33.0 [2013-10-14] * The version number was bumped for the Bioc devel version. Version: 1.32.0 [2012-10-14] * The version number was bumped for the Bioconductor release version, which is now Bioc v2.13 for R (>= 3.0.0). Version: 1.31.10 [2013-10-08] NEW FEATURES: * Added averageQuantile() for matrices in addition to lists. PERFORMANCE: * SPEEDUP: Now normalizeQuantileSpline(..., sortTarget = TRUE) sorts the target only once for lists of vectors just as done for matrices. DOCUMENTATION: * Merged the documentation for normalizeQuantileSpline() for all data types into one help page. Same for plotXYCurve(). CODE REFACTORING: * Bumped up package dependencies. BUG FIXES: * Argument 'lwd' of plotXYCurve(X, ...) was ignored if 'X' was a matrix. Version: 1.31.9 [2013-10-07] NEW FEATURES: * Now library(aroma.light, quietly = TRUE) attaches the package completely silently without any messages. * Now the 'aroma.light' Package object is also available when the package is only loaded (but not attached). DOCUMENTATION: * Merged the documentation for normalizeQuantileRank() for numeric vectors and lists. * Now documention S3 methods under their corresponding generic function. Version: 1.31.8 [2013-10-02] DOCUMENTATION: * More generic functions are now "aliased" under relevant corresponding methods. Version: 1.31.7 [2013-09-27] PERFORMANCE: * SPEEDUP: Now all package functions utilizes 'matrixStats' functions where possible, e.g. anyMissing(), colMins(), and rowWeightedMedians(). CODE REFACTORING: * Bumped up package dependencies. Version: 1.31.6 [2013-09-25] CODE REFACTORING: * CLEANUP: Package no longer use a fallback attachment of the 'R.oo' package upon attachment. Version: 1.31.5 [2013-09-23] SOFTWARE QUALITY: * ROBUSTNESS: Now properly declaring all S3 methods in the NAMESPACE file. PERFORMANCE: * SPEEDUP/CLEANUP: normalizeTumorBoost() now uses which() instead of whichVector() of 'R.utils'. Before R (< 2.11.0), which() used to be 10x slower than whichVector(), but now it's 3x faster. CODE REFACTORING: * CLEANUP: Now only using 'Authors@R' in DESCRIPTION, which is possible since R (>= 2.14.0). Hence the new requirement on the version of R. * Bumped up package dependencies. Version: 1.31.4 [2013-09-10] SOFTWARE QUALITY: * CLEANUP: Now package explicitly imports what it needs from matrixStats. CODE REFACTORING: * Bumped up package dependencies. Version: 1.31.3 [2013-05-25] PERFORMANCE: * SPEEDUP: Removed all remaining gc() calls, which were in normalizeQuantileSpline(). * SPEEDUP: Replaced all rm() calls with NULL assignments. CODE REFACTORING: * Updated the package dependencies. Version: 1.31.2 [2013-05-20] * Same updates as in v1.30.2. Version: 1.31.1 [2011-04-18] * Same updates as in v1.30.1. Version: 1.31.0 [2013-04-03] * The version number was bumped for the Bioc devel version. Version: 1.30.5 [2013-09-25] SOFTWARE QUALITY: * Backport from v1.31.5: Declaring all S3 methods in NAMESPACE. CODE REFACTORING: * Backport from v1.31.5: normalizeTumorBoost() now uses which(), which also removes one dependency on 'R.utils'. Version: 1.30.4 [2013-09-25] SOFTWARE QUALITY: * Backport from v1.31.4: Now package explicitly imports what it needs from matrixStats. Version: 1.30.3 [2013-09-25] PERFORMANCE: * Backport from v1.31.3: Removal of all gc() calls and removal of variables is now faster. BUG FIX: * Removed one stray str() debug output in robustSmoothSpline(). Version: 1.30.2 [2013-05-20] * CRAN POLICY: Now all Rd \usage{} lines are at most 90 characters long. Version: 1.30.1 [2013-04-18] NEW FEATURES: * Now backtransformPrincipalCurve() preserves dimension names. BUG FIXES: * backtransformPrincipalCurve() gave an error if the pricipal curve was fitted using data with missing values. * fitPrincipalCurve() would not preserve dimension names if data contain missing values. Version: 1.30.0 [2012-04-03] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.12 for R (>= 3.0.0). Version: 1.29.0 [2012-10-01] * The version number was bumped for the Bioc devel version. Version: 1.28.0 [2012-10-01] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.11 for R (>= 2.15.1). Version: 1.27.1 [2012-09-12] SOFTWARE QUALITY: * ROBUSTNESS: Replaced an .Internal(psort(...)) call in medianPolish() with a call to matrixStats:::.psortKM(). Version: 1.27.0 [2012-08-30] CODE REFACTORING: * CLEANUP: Removed weightedMedian(), which has been moved to the matrixStats package. * BACKWARD COMPATIBILITY: Now package depends on the matrixStats (>= 0.5.2) package, so that weightedMedian() is still available when loading this package. In future releases, matrixStats will be downgraded to only be a suggested package. Version: 1.26.1 [2012-08-30] CODE REFACTORING: * Updated the package dependencies. BUG FIXES: * robustSmoothSpline() would not work with most recent R devel versions. Version: 1.26.0 [2012-08-19] SIGNIFICANT CHANGES: * Changed the license of aroma.light to GPL (>= 2) from LGPL (>= 2), because some of the implementation was adopted from GPL (>= 2) code, i.e. robustSmoothSpline() uses code from stats::smooth.spline(). SOFTWARE QUALITY: * R CMD check no longer warns about some examples depending on the R.basic package. Version: 1.25.4 [2012-08-19] SOFTWARE QUALITY: * WORKAROUND: Now robustSmoothSpline() robustly locates the proper native R fit function for smooth splines, which vary with different releases of R. Version: 1.25.3 [2012-04-16] CODE REFACTORING: * Package no longer depends on R.methodsS3, only imports. Version: 1.25.2 [2012-04-16] SOFTWARE QUALITY: * 'R CMD check' no longer complaints about .Internal() calls. Version: 1.25.1 [2012-04-16] NEW FEATURES: * Added support for fitNaiveGenotypes(..., flavor = "fixed"). * GENERALIZATION: Now fitNaiveGenotypes() returns also 'flavor' and 'tau'. The latter are the genotype threshholds used by the caller. CODE REFACTORING: * CLEANUP: Dropped argument 'flavor' of callNaiveGenotypes(); it is instead passed to fitNaiveGenotypes() via '...'. Version: 1.25.0 [2012-03-30] * The version number was bumped for the Bioconductor devel version. Version: 1.24.0 [2012-03-30] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.10 for R (>= 2.15.0). Version: 1.23.0 [2011-10-31] * The version number was bumped for the Bioconductor devel version. Version: 1.22.0 [2011-10-31] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.9 for R (>= 2.14.0). Version: 1.21.2 [2011-10-10] CODE REFACTORING: * Updated robustSmoothSpline() such that it works with the new "uniqueness" scheme of smooth.spline() in R v2.14.0 and newer. It is tricky, because robustSmoothSpline() is a reiterative algorithm which requires that the choosen "unique" x:s does not change in each iteration. Previously, 'signif(x, 6)' was used to identify unique x:s, which gives the same set of values when called twice, whereas this is not true for the new choice with 'round((x - mean(x))/tol)'. Version: 1.21.1 [2011-06-26] NEW FEATURES: * Added argument 'aspectRatio' to plotMvsA(). It can be used to adjust the range of the 'Mlim' argument relative to the 'Alim' argument. Version: 1.21.0 [2011-04-13] * The version number was bumped for the Bioconductor devel version. Version: 1.20.0 [2010-04-13] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.8 for R (>= 2.13.0). Version: 1.19.6 [2011-04-12] CODE REFACTORING: * CLEANUP: Removed internal patch of principal.curve(). If an older version than princurve v1.1-10 is used, an informative error is thrown requesting an update. The internal patch is part of the offical princurve v1.1-10 release (since 2009-10-04). * Now all methods allocate objects with NAs of the appropriate mode. KNOWN ISSUES: * Recent updates to smooth.spline() in R v2.14.0 causes robustSmoothSpline() to break in some cases. Version: 1.19.5 [2011-04-08] NEW FEATURES: * Now weightedMedian() returns NA:s of the same mode as argument 'x'. Version: 1.19.4 [2011-03-03] * Same updates as in v1.18.4. Version: 1.19.3 [2011-02-05] * Same updates as in v1.18.3. Version: 1.19.2 [2010-10-22] * Same updates as in v1.18.2. Version: 1.19.1 [2010-10-18] * Same updates as in v1.18.1. Version: 1.19.0 [2010-10-18] * The version number was bumped for the Bioconductor devel version. Version: 1.18.4 [2011-03-03] BUG FIXES: * findPeaksAndValleys(x, to) were 'x' is numeric would use partial match and interpret 'to' as argument 'tol' and not part of '...' passed to density(). This problem was solved by placing '...' before argument 'tol'. Thanks Oscar Rueda at the Cancer Reasearch UK for reporting on and identifying this bug. Version: 1.18.3 [2011-02-05] DOCUMENTATION: * Added paragraphs on how to do affine normalization when channel offsets are known/zero. Same for multiscan calibration when scanner offsets are known/zero. * Fixed broken links to help for iwpca(). Version: 1.18.2 [2010-10-22] DOCUMENTATION: * Minor clarifications to the help page on "1. Calibration and Normalization". This page is now also referenced in help("calibrateMultiscan"). Version: 1.18.1 [2010-10-18] NEW FEATURES: * Argument 'censorAt' for fitNaiveGenotypes() has new default. * These updates were supposed to be in v1.17.7, but we forgot to commit them to the BioC repository before the new BioC release. BUG FIXES: * fitNaiveGenotypes(..., subsetToFit = ) would throw an exception reporting "Some elements of argument 'subsetToFit' is out of range ...". Version: 1.18.0 [2010-10-18] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.7 for R (>= 2.12.0). Version: 1.17.6 [2010-10-08] NEW FEATURES: * Now findPeaksAndValleys() returns a object of class PeaksAndValleys, which extends data.frame. Version: 1.17.5 [2010-10-07] NEW FEATURES: * Added optional argument 'fit' to callNaiveGenotypes() for passing a model fit returned by fitNaiveGenotypes(). If not specified, callNaiveGenotypes() will call fitNaiveGenotypes() internally. * Added fitNaiveGenotypes(), which previously was only internal of callNaiveGenotypes(). Version: 1.17.4 [2010-10-06] NEW FEATURES: * Added findPeaksAndValleys() for the 'density' class, which then findPeaksAndValleys() for 'numeric' utilizes. Version: 1.17.3 [2010-09-18] SOFTWARE QUALITY: * ROBUSTNESS: Now normalizeFragmentLength() asserts that arguments 'fragmentLengths' and 'y' contain at least some finite values and specifies the same number of units. In addition, the method also gives more informative error messages in case it cannot fit the normalization function due to non-finite values. Version: 1.17.2 [2010-08-04] NEW FEATURES: * Added argument 'preserveScale' to normalizeTumorBoost() to rescale the calibrated allele B fractions for heterozygous SNPs such that the compression relative to the homozgygotes is preserved. Version: 1.17.1 [2010-07-23] * Same updates as in release version v1.16.1. Version: 1.17.0 [2010-04-22] * The version number was bumped for the Bioconductor devel version. Version: 1.16.1 [2010-07-23] NEW FEATURES: * Now callNaiveGenotypes() returns the model estimates as attribute 'modelFit'. This feature was supposed to be in v1.16.0. Version: 1.16.0 [2010-04-22] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.6 for R (>= 2.11.0). Version: 1.15.4 [2010-04-08] SOFTWARE QUALITY: * R devel assumes ASCII encoding unless specified. Added explicit Latin-1 encoding to the DESCRIPTION file to R CMD check to pass. Version: 1.15.3 [2010-04-04] NEW FEATURES: * Added normalizeDifferencesToAverage(), normalizeTumorBoost(), callNaiveGenotypes() and internal findPeaksAndValleys(), which all were moved from the aroma.cn package. Version: 1.15.2 [2010-03-12] BUG FIXES: * The example of fitPrincipalCurve() used a non-existing argument name in the calls to substitute(). Thanks to Brian Ripley at University of Oxford for reporting this. Version: 1.15.1 [2009-11-01] CODE REFACTORING: * Now fitPrincipalCurve() only uses the internal bug-fix patch if a version earlier than princurve v1.1-10 is installed. Version: 1.15.0 [2009-10-27] * The version number was bumped for the Bioconductor devel version. Version: 1.14.0 [2009-10-27] * The version number was bumped for the Bioconductor release version, which now is Bioc v2.5 for R (>= 2.10.0). Version: 1.13.6 [2009-10-20] DOCUMENTATION: * FIX: CITATION file reverted to that of v1.13.4. Version: 1.13.5 [2009-10-08] DOCUMENTATION: * CITATION file [incorrectly] updated by BioC maintainers. Version: 1.13.4 [2009-09-23] DOCUMENTATION: * Fixed a few broken Rd links. Version: 1.13.3 [2009-07-15] NEW FEATURES: * ADDED: fit- and backtransformXYCurve(). * Added attribute 'processingTime' to the fit object returned by fitPrincipalCurve(). Version: 1.13.2 [2009-05-29] * Incorporating the same updates as in release v1.12.2. Version: 1.13.1 [2009-05-13] * Incorporating the same updates as in release v1.12.1. Version: 1.13.0 [2009-04-20] * The version number was bumped for the Bioconductor devel version. Version: 1.12.2 [2009-05-29] CODE REFACTORING: * Replacing old HOWTOCITE with a standard CITATION file. BUG FIXES: * Previous bug fix in backtransformPrincipalCurve() regarding argument 'dimension' broke the initial purpose of this argument. Since both use cases are still of interest, how the subsetting is done is now based on whether the number of dimensions of the input data and the model fit match or not. See help and example code for 'backtransformPrincipalCurve.matrix'. Version: 1.12.1 [2009-05-13] BUG FIXES: * backtransformPrincipalCurve(..., dimensions) did not subset the 'X' matrix. Also, the method now returns a matrix of the same number of columns requested. The Rd example now illustrates this. Thanks to Pierre Neuvial, UC Berkeley for the troublshooting and fix. Version: 1.12.0 [2009-04-20] * The version number was bumped for the Bioconductor release version. Version: 1.11.2 [2009-02-08] BUG FIXES: * An error was thrown in backtransformPrincipalCurve() when argument 'dimensions' was specified. Version: 1.11.1 [2009-01-12] NEW FEATURES: * Added fit- & backtransformPrincipalCurve(). Version: 1.11.0 [2008-10-21] * The version number was bumped for the Bioconductor devel version. Version: 1.10.0 [2008-10-21] * The version number was bumped for the Bioconductor release version. Version: 1.9.2 [2008-09-11] NEW FEATURES: * Added argument 'onMissing' to normalizeFragmentLength() for specifying how to normalize (if at all) data points for which the fragment lengths are unknown. For backward compatibility, we start off by having it "ignore" by default. CODE REFACTORING: * MEMORY OPTIMIZATION: robustSmoothSpline() is now cleaning out more variables when no longer needed. Version: 1.9.1 [2008-05-10] * Incorporating the same updates as in release v1.8.1. Version: 1.9.0 [2008-04-29] * The version number was bumped for the Bioconductor devel version. Version: 1.8.1 [2008-05-10] BUG FIXES: * If the 'subsetToFit' of normalizeFragmentLength() was shorter than the number of data points, an exception was thrown. The test was supposed to assert that the subset was not greater than the number of data points. Version: 1.8.0 [2008-04-29] * The version number was bumped for the Bioconductor release version. Version: 1.7.2 [2008-04-14] NEW FEATURES: * Added normalizeFragmentLength(). * Added normalizeQuantileSpline(). * Renamed normalizeQuantile() to normalizeQuantileRank(). * Added plotXYCurve(). * Added predict() for the 'lowess' class. Version: 1.7.1 [2007-11-28] NEW FEATURES: * The startup message when loading the package is generated with packageStartupMessage() so that it can be suppressed. DOCUMENTATION: * TYPO: Corrected a spelling error in the help pages. SOFTWARE QUALITY: * Package passes R CMD check R v2.6.1. CODE REFACTORING: * Package now only suggest the R.oo package, and instead depends on the new R.methodsS3. Version: 1.7.0 [2007-10-08] * The version number was bumped for the Bioconductor devel version. Version: 1.6.0 [2007-10-08] * The version number was bumped for the Bioconductor release version. Version: 1.5.2 [2007-08-10] SOFTWARE QUALITY: * Package pass R CMD check R v2.6.0. Version: 1.5.1 [2007-06-08] (this was mistakenly versioned 1.4.1) NEW FEATURES: * Added normalizeAverage(). SOFTWARE QUALITY: * Package pass R CMD check R v2.6.0 with Rd encoding errors. Version: 1.5.0 [2007-05-09] * The version number was bumped for the Bioconductor devel version. Version: 1.4.0 [2007-05-09] * The version number was bumped up with the Bioconductor release. Version: 1.3.1 [2007-01-15] CODE REFACTORING: * Removed code to use 'modreg' for backward compatibility with R < 1.9.0. * Added R.utils to Suggests field of DESCRIPTION. Version: 1.3.0 [2006-10-??] * The devel version number was bumped up with the Bioconductor release. Version: 1.2.0 [2006-10-03] * The version number was bumped up with the Bioconductor release. Version: 1.1.0 [2006-07-20] SIGNIFICANT CHANGES: * Added to Bioconductor v1.9. NEW FEATURES: * Added some trial RSP pages. Try browseRsp() in the R.rsp package. Version: 0.1.7 [2006-06-27] CODE REFACTORING: * Made the package truely standalone except from R.oo. Previously package R.basic was used in some of the examples. Version: 0.1.6 [2006-05-22] NEW FEATURES: * Added medianPolish() which is much faster than stats::medpolish() when there are no NA. * Added plotDensity() for list of vectors as well as for matrices. * Added normalizeQuantile() for lists of vectors as well as for a single vector of numerics. To calculate the target quantile there is a new function averageQuantile(), which is also for lists of vectors. It latter does not support robust estimatation of the average, because it safes memory. * Updated normalizeQuantile() for matrices according to the updates in the limma package. CODE REFACTORING: * Added a namespace for the package. * Added 'biocViews' since the package will eventually be added to the Bioconductor project. Version: 0.1.5 [2006-04-21] PERFORMANCE: * Minor speedup to weightedMedian(), e.g. negative weights do no longer give and error, but are treated as zero instead. This removes some overhead of the function. Also, if it is known that there are no NAs that can be specified by na.rm = NA, which will skip NA checks. Version: 0.1.4 [2006-03-28] DOCUMENTATION: * Updated broken Rd links. * Updated the references to publications. Version: 0.1.3 [2006-01-22] NEW FEATURES: * Now fitIWPCA() does not return the data matrix. This is to save memory. The calling algorithm can equally well add the data if it is needed. DOCUMENTATION: * Added help on the returned parameters of fitIWPCA(). Version: 0.1.2 [2005-09-06] NEW FEATURES: * All plot methods displaying log-ratios now assures that no fake log-ratios are calculated due to two negative raw signals. Similarily, methods display log-intensities now assures that the log-intensities are calculated as doubles to avoid possible overflow warnings for too large integers. Version: 0.1.1 [2005-07-26] NEW FEATURES: * Added sampleCorrelations() and sampleTuples(). * Now argument 'interpolate' of weightedMedian() defaults to TRUE only if 'ties' is NULL. Version: 0.1.0 [2005-06-03] SIGNIFICANT CHANGES: * Created. Most of the matrix methods were copied from the R.basic and the aroma packages. The purpose of this package is to provide a standalone package, which does not require any of the aroma classes. This will allow the methods to be used by end users as is, or be utilized in other packages. aroma.light/inst/0000755000175000017500000000000014136047216013572 5ustar nileshnilesharoma.light/inst/CITATION0000644000175000017500000001607314136047216014736 0ustar nileshnileshcitHeader("Please cite aroma.light one or more of approprite reference below"); citEntry( # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # BibTeX entry: # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - entry = "Article", author = "H. Bengtsson and Pierre Neuvial and Terence P Speed", title = "TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays", journal = "BMC Bioinformatics", year = "2010", month = "May", volume = "11", number = "245", doi = "10.1186/1471-2105-11-245", url = "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-245", # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Plain-text citation: # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - textVersion = paste(sep="", "H. Bengtsson, P. Neuvial and T.P. Speed. 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", "Calibration and assessment of channel-specific biases in microarray data with extended dynamical range, ", "BMC Bioinformatics, ", "2004" ) ); citEntry( # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # BibTeX entry: # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - entry="TechReport", author = "H. Bengtsson", title = "{aroma} - {A}n {R} {O}bject-oriented {M}icroarray {A}nalysis environment", type = "Preprint in Mathematical Sciences", institution = "Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Sweden", year = "2004", number = "18", url = "https://lup.lub.lu.se/search/publication/929031", # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Plain-text citation: # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - textVersion = paste(sep="", "H. Bengtsson. ", "aroma - An R Object-oriented Microarray Analysis environment, ", "Preprint 2004:18, ", "Centre for Mathematical Sciences, Lund University, ", "2004" ) ); aroma.light/inst/WORDLIST0000644000175000017500000000116714136047216014771 0ustar nileshnileshAchim affine Affine affinely al AppVeyor ASCN backtransform backtransformed BAFs Bergh Bioinformatics BMC Bolstad Carvalho cDNA Centre CMD Crosse Demmel Dongarra downweight DSC Eqn Eqns et Friedrich Hastie heterozygous Himel homozygous Hornik Irizarry iteratively IWPCA KxE KxK LAPACK's Leisch licence limma loadings loess Loess Lund Lx macOS multiscan Multiscan multiscanned MxL normalizeQuantiles Nx NxD NxK NxL oo Pawitan PeaksAndValleys Ploner PMID PMT pre Pre Preprints rescale Rescales resolutioncopy robustified robustify SmoothSplineLikelihood Smyth Spellman Stuetzle suboptimal tricube TumorBoost UW Wishlist WPCA Zeileis aroma.light/inst/rsp/0000755000175000017500000000000014136047216014376 5ustar nileshnilesharoma.light/inst/rsp/style.css0000644000175000017500000000753014136047216016255 0ustar nileshnilesh/********************************************************************* This HTML style sheet makes your webpage more conforming to braju.com's graphical profile. 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Pre-processing of signals

Although the below discussion applies to a pair of single-channel arrays, for simplicity consider a two-color experiment (C=2) where each array has I features (spots). Let xc,i the (unknown) true biological signal for feature i=1,2,...,I in sample/channel c=1,2,...,C. This is the quantity that we wish to estimate.

With microarrays, we obtain estimates of xc,i called feature signals. Denote these by yc,i. These are not direct measurements, but rather transformed signals due to additive background, scale effects, non-linear effects, and other systematic effects, which we all summarize in a function fc(xc,i). With random errors ec,i, we measure:

yc,i = fc(xc,i) + ec,i.

The most general objective in microarray analysis is to obtain an estimate x*c,i of the true signal, or at least an estimate y*c,i which is close to the true signal up to a less important scale factor (d). If we can estimate fc(), then we can back transform the data to get:

y*c,i = d * fc-1(yc,i)

If we manage to do this, we know that y*c,i is proportional to xc,i, which is often sufficent since we only need to know the relative signal, e.g. when the true signals doubles, our estimate doubles too. Note that this is typically not the case for yc,i. This is why we pre-process measure signals. Simply speaking, background subtraction, scanner calibration, and normalization is about estimating fc() and back-transforming signals to obtain y*c,i.

For the definitions and the difference between calibration and normalization, see the introduction of H. Bengtsson (2004).

aroma.light/inst/rsp/src/0000755000175000017500000000000014136047216015165 5ustar nileshnilesharoma.light/inst/rsp/src/simpleHeader.html.rsp0000644000175000017500000000125314136047216021261 0ustar nileshnilesh<%-- H e a d e r --%> <% uri <- getRequestUri(request); uriPath <- strsplit(uri, split="/")[[1]][-1]; if (regexpr("./$", uri) != -1) { upPath <- "../"; } else if (regexpr("./index.html.rsp$", uri) != -1) { upPath <- "../"; uriPath <- uriPath[-length(uriPath)]; } else { upPath <- "index.html.rsp"; uriPath <- uriPath[-length(uriPath)]; } %>
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Admin: shutdown

aroma.light/inst/rsp/src/simpleFooter.html.rsp0000644000175000017500000000145514136047216021333 0ustar nileshnilesh<%-- F o o t e r --%> <% pd <- packageDescription(pkg); isLoaded <- (pkg %in% gsub("^package:", "", search())); %>
<%=pd$Package%> v<%=pd$Version%> (<%=pd$Date%>) (details, help pages) <% if (!isLoaded) { %> [load package] <% } %>
Powered by R.rsp v<%=getVersion(R.rsp)%> and Webcuts.

aroma.light/inst/rsp/src/simpleHead.html.rsp0000644000175000017500000000062614136047216020735 0ustar nileshnilesh <%=title%> aroma.light/inst/rsp/references.html.rsp0000644000175000017500000000366614136047216020223 0ustar nileshnilesh

References

H. Bengtsson, J. Vallon-Christersson & G. Jnsson, Calibration and assessment of channel-specific biases in microarray data with extended dynamical range, BMC Bioinformatics, 2004, 5:177. [PMID: 15541170]

H. Bengtsson, Low-level Analysis of Microarray Data, Doctoral Thesis in Mathematical Sciences 2004:6, Mathematical Statistics, Lund University, 2004.

A. Bengtsson & H. Bengtsson, Microarray image analysis: background estimation using quantile and morphological filters, BMC Bioinformatics, 2006, 7:96. [PMID: 16504173]

H. Bengtsson & O. Hssjer, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100. [PMID: 16509971]

H. Lyng, A. Badiee, D.H. Svendsrud, E. Hovig, O. Myklebost, T. Stokke. Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction, BMC Genomics, 2004, 5:10. [PMID: 15018648]

P. Rydn, H. Andersson, M. Landfors, L. Nslund,B. Hartmanov, L. Noppa, & A. Sjstedt, Evaluation of microarray data normalization procedures using spike-in experiments, BMC Bioinformatics, 2006, 7:300. [PMID: 16774679]

aroma.light/inst/rsp/.rspPlugins0000644000175000017500000000004714136047216016546 0ustar nileshnilesharoma.light::rsp/index.rsp aroma.light aroma.light/inst/rsp/MultiscanCalibration/0000755000175000017500000000000014136047216020505 5ustar nileshnilesharoma.light/inst/rsp/MultiscanCalibration/MultiscanCalibration.html.rsp0000644000175000017500000001565714136047216026323 0ustar nileshnilesh

Multiscan calibration

In this section we give recommendation on how to calibrate data for scanner biases. It applies to any type of microarray platform and scanner.

In Bengtsson et al. (2004) we give evidence that microarray scanners can introduce a significant bias in data. This bias, which is about 15-25 out of 65535, will introduce intensity dependency in the log-ratios, as explained in Bengtsson & Hssjer (2006). We found the bias to rather stable across arrays and time (Bengtsson et al. 2004), but further research is needed to extrapolate this to be true over a longer period of time. An example of multiscan calibration where the same two-channel array has been scanned at four different PMT voltages is illustrated in the below figure.

Between-scan (A,M) plot Between-scan (A,M) plot
Figure: Multiscan calibration where the same array has been scanned at four different PMT voltages (800V, 700V, 600V, and 500V). Data shown is for the red channel. Left: Between-scan (A,M) plot, that is, between-scan log-ratio versus log-intensity plot before calibration of the six different PMT-pairs. Scanner bias was estimated to be aR=15.7. Minimum observed log-intensity is Amin = log2(aR)=3.97, which is confirmed by the graph. Right: Between-scan (A,M) plot after calibration, that is, subtracting scanner biases and rescaling. Except for noise and some scanner saturation, all (A,M) pairs overlap quite well. Some saturated signals (seen at high intensities) and other outliers (a few) can be seen. The calibration is robustified against these.

Scan protocol

To calibrate signals for scanner biases, scan the same array at multiple PMT settings (in decreasing order) at three or more PMT settings, e.g. 800V, 700V, 600V and 500V. Do not worry that you get too weak or too strong signals at the extreme PMT gains; together, all scans will extend the dynamical range and saturated signals will be ignored. It is important that you do this without washing, cleaning or by other means changing the array between subsequent scans. Although not necessary, it is prefered that the array remains in the scanner between subsequent scans. This will simplify the image analysis since spot identification can be made once, preferably using the strongest scan, if the images align perfectly.

Tips for Windows users

It may be helpful to use a script language for automating the user's interaction with the scanner software. For Windows users, we recommend the free AutoIt software which easily can send keystrokes, mouse clicks etc. to any Windows application. If you write a script for your scanner software, we are happy to included it here for everybodies convenience.

In <%=pkg%>

After image analysis, for each array and for each channel, read the signals from scans k=1,2,...,K with features n=1,2,...,N into an NxK matrix, say, X. It is enough to use foreground signals. Then calibrate it by: 

Xc <- calibrateMultiscan(X)
<%topic <- "calibrateMultiscan.matrix"%> [help] [example]

By default, this will return an Nx1 matrix. Repeat this for each channel.

Extended dynamical range

Because the same array is scanned at several different sensitivity settings, the effect will be an extended dynamical range and decreased measurement noise. In Bengtsson et al. (2004), we extended the effective range on the Axon GenePix 4000A scanner 40 times, that is, the range of signals incresed from [0,65535] to more than [0,2600000].

Decreased scanner noise and increased sensitivity

The standard deviation of the scanner noise, decrease roughly by the square root of the number of scans, e.g. with four scans the standard deviation halves.

A comparison study of different scan and normalization strategies by Rydn et al. (2006) show that the multiscan calibration method has a much higher sensitivity (88%) compared to most other methods (68-85%). In this particular study sensitivity refers to the ability to detect differentially expressed (DE) genes.

Robustness

Saturated signals (due to scanner saturation) are identified to be those with signal above a certain level (argument satSignal). Saturated signals are excluded when estimating the calibration function. Moreover, the estimate of the calibration parameters is robust (the estimate is done in L1 instead of L2) making it even less sensitive to outliers, which can be signals from semi-saturated spots that are not identified as saturated because not all pixels in the spot are saturated. Finally, when calculating the average calibrated scan, the median is used (by default), which further robustify against saturated signals and other outliers.

Is multiscan calibration really necessary?

In Bengtsson et al. (2004) we give evidence that the scanner may introduce channel biases and from experience we have found that much of the intensity dependent effects (between channels) are removed by the multiscan calibration. However, if affine normalization is applied as a first normalization step, multiscan calibration may be skipped, although it is always better to do it.

Other researchers have reported scanner bias (on the same range) too. For this reason we strongly recommend that you test your scanner at least once according to the above protocol. Please feel free to forward the estimates of your scanner's channel biases to us. This will help us to reach a strong concensus of the exact reason for the bias and to see if the biases are stable in time and between labs. The results will be shared here.

Use multiscan calibration when you cannot normalize by other means

Moreover, due to the experimental design, but also the layout of the array, some researches find it hard or even impossible to normalize data in a sound way. We see that much of the intensity dependent effects are due to scanner biases. Since multiscan calibration does not rely on assumptions such as "the majority of the genes are non-differentially expressed" and so on, it can be applied safely to any microarrays.

aroma.light/inst/rsp/R/0000755000175000017500000000000014136047216014577 5ustar nileshnilesharoma.light/inst/rsp/R/weightedCurveFitNormalization.Rex0000644000175000017500000000446614136047216023310 0ustar nileshnileshlibrary(aroma) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsA <- function(rg, xlab="A", ylab="M", Alim=c(0,16), main="", ..., add=FALSE) { if (!add) { plot(NA, xlab=xlab, ylab=ylab, xlim=Alim, ylim=c(-1,1)*diff(Alim)) title(main=main) } R <- rg[,"R"] G <- rg[,"G"] M <- log(R/G, base=2) A <- log(R*G, base=2)/2 points(A,M, pch=".", ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Load data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # One array was scanned four times at four different PMT settings. rg0 <- RGData$read("PMT-RGData.dat", path=aroma$dataPath); setLayout(rg0, Layout(4,4,17,17)); # Not really necessary! setSlideName(rg0, c("500V","600V","700V","800V")); keepSlides(rg0, slide="600V") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # "Add" some outliers # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - outliers <- 1:500 rg0$G[outliers,] <- runif(n=length(outliers), min=40000, max=55000) Alim <- c(-2,15) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - png("../figures/NormalizeCurveFit-raw.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rg0), xlim=Alim) dev.off() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Non-weighted curve-fit normalization # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgNW <- clone(rg0) normalizeCurveFit(rgNW) png("../figures/NormalizeCurveFit-nonweighted.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rgNW), xlim=Alim) dev.off() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Weighted curve-fit normalization # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgW <- clone(rg0); # Down-weight outliers weights <- rep(1, nbrOfSpots(rg0)) weights[outliers] <- 1/1000 setProbeWeights(rgW, weights=weights) normalizeCurveFit(rgW) png("../figures/NormalizeCurveFit-weighted.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rgW), xlim=Alim) dev.off() aroma.light/inst/rsp/R/weightedAffineNormalization.Rex0000644000175000017500000000444614136047216022747 0ustar nileshnileshlibrary(aroma) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Functions # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plotMvsA <- function(rg, xlab="A", ylab="M", Alim=c(0,16), main="", ..., add=FALSE) { if (!add) { plot(NA, xlab=xlab, ylab=ylab, xlim=Alim, ylim=c(-1,1)*diff(Alim)) title(main=main) } R <- rg[,"R"] G <- rg[,"G"] M <- log(R/G, base=2) A <- log(R*G, base=2)/2 points(A,M, pch=".", ...) } # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Load data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # One array was scanned four times at four different PMT settings. rg0 <- RGData$read("PMT-RGData.dat", path=aroma$dataPath); setLayout(rg0, Layout(4,4,17,17)); # Not really necessary! setSlideName(rg0, c("500V","600V","700V","800V")); keepSlides(rg0, slide="600V") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # "Add" some outliers # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - outliers <- 1:50 rg0$G[outliers,] <- runif(n=length(outliers), min=40000, max=55000) Alim <- c(-2,15) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Raw data # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - png("../figures/NormalizeAffine-raw.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rg0), xlim=Alim) dev.off() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Non-weighted affine normalization # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgNW <- clone(rg0) normalizeAffine(rgNW) png("../figures/NormalizeAffine-nonweighted.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rgNW), xlim=Alim) dev.off() # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Weighted affine normalization # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rgW <- clone(rg0); # Down-weight outliers weights <- rep(1, nbrOfSpots(rg0)) weights[outliers] <- 1/200 setProbeWeights(rgW, weights=weights) normalizeAffine(rgW) png("../figures/NormalizeAffine-weighted.png", width=210, height=210) opar <- par(cex=0.6) plot(as.MAData(rgW), xlim=Alim) dev.off() aroma.light/inst/rsp/README0000644000175000017500000000032314136047216015254 0ustar nileshnileshMethods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. aroma.light/inst/rsp/bgcorrection.html.rsp0000644000175000017500000000520114136047216020545 0ustar nileshnilesh

Additive background correction

By background correction we mean subtraction of additive background signals from the foreground signals on the intensity (non-log) scale. The foreground signal is defined to be the mean pixel intensity of the interior of the feature. However, what we mean by background signal is not very well defined, only that it is assumed to be added to the foreground signal.

The most common definition originates from the spotted arrays where the background is taken to be the mean pixel intensity in a region near the spot. This signal estimated using various image analysis methods. For Affymetrix arrays the background signal is instead defined to be the signal of a mismatch probe, which contains probe sequences identical to perfect-match probe except for the middle 13:th nucleotide. In both cases, the background signal is assumed to contaminate the foreground signal in some way, which typically, although not necessarily correct, means that the background signal ac,i is assumed to be added on top of the true signal such that:

yc,i = xc,i + ac,i + ec,i.

Background correction, also known as background subtraction, is then:

y*c,i = yc,i - ac,i.

Author's comments

It is important to understand that still has not been settled if the mean intensity of the pixels in the proximity of a spot is also added to the pixel intensities within the spot. Even if this can be shown, it is still not clear how to optimally estimate such background signal, cf. Bengtsson & Bengtsson (2004). Similarily, it still to be established that the signal of the mismatch probe on an Affymetrix array is added to the signal of the corresponding perfect-match probe.

I believe that the relationship between foreground and background signals as estimated by most image analysis methods is more complicated than just being additive. For instance, it is not rare to see that the spot itself is darker than the surrounding regions. Same for Affymetrix probe signals. If the mismatch probes measures the amount of cross-hybridization as intended, the relationship is rather multiplicative than additive. For this reason, I try to avoid background subtraction in the classical way. Instead I typically work with foreground signals, or perfect-match signals only, and try to estimate the background ac,i by other means.

aroma.light/inst/rsp/NormalizationWithWeights/0000755000175000017500000000000014136047216021413 5ustar nileshnilesharoma.light/inst/rsp/NormalizationWithWeights/NormalizationWithWeights.html.rsp0000644000175000017500000002132414136047216030123 0ustar nileshnilesh

Normalization with weights

Introduction

In this section we explain how to do weighted normalization. This will make it possible to specify how much each data point will affect the estimate of the normalization function. We will cover:

  1. Introduction to robustness
  2. Weights
  3. Weighted affine normalization
  4. Weighted curve-fit normalization

Introduction to robustness

Normalization of data takes place in two steps. First, a normalization function is estimated, and, second, data is normalized (transformed) using this estimated function.

Robust normalization is when the normalization function is estimated such that the estimation is affects as little as possible or not at all by outliers. Very simplied, this can be done by using a distance measure that penalizes outliers less, that is, outliers have less to say about the estimated function. For instance, in least-square regressing fitting, the distance measure is in L2, that is, (xi-m)2. When fitting in L1, that is, |xi-m|, the estimate is made more robust. Other measures, or weight functions, are also available. For details, see Bengtsson & Hssjer (2006). The default in aroma is Tukey's biweight, which gives data points close to the fitted line a high weight and data points beyond a few standard deviations zero weight.

Weights

Closely related to robustness is weighted estimation of the normalization function. This may for instance be the case when a quality measure has been assigned to each of the data points, for instance, by the amount of saturated pixels in a spot, by the shape of the spot, by visual inspection of images and so. The higher quality of a signal, the more it should have a say, that is, the more weight it should have in the estimation of the normalization function, and vice versa.

In aroma, all weights must be between zero and one, i.e. in [0,1]. Not-a-number (NA) values are not allowed. The most common form of weights is data-point weights, which define the weight of a data point. A data point can be a single value or a vector of values depending on context. For instance, in curve-fit normalization, a data point i is the log-ratio log-intensity pair (Ai,Mi). In within-array affine normalization of two-channel data, the red-green pair (Gi,Ri) is a data point, and in affine normalization with J channels, the vector (Xi,1,...,Xi,j) constitutes a data point.

Probe weights

Typically, it is much easier to assign a weight to each probe, that is spot, than to, say, a the set of identical spots across several arrays. A probe weights is a weight assigned to a specific probe. For instance, in two-channel microarrays, the red-green pair signals share the same probe weight.

Given probe weights, the data point weight is the square root of the average squared probe weight. Formally, given spots (i,j) for gene i on arrays j=1,...,J and corresponding probe weights wprobei,j, the data point weight for gene i is wi=sqrt(sumj(wprobei,j2)/J). For example, for the data point (Ai,Mi), the data point weight is simply the same as the probe weight. Similar for the pair (Gi,Ri).

Signal weights

Signals weights are similar to probe weights, but can be assigned to each channel seperately. For instance, for red-green microarrays, the red signals can be assigned a set of weights and the green signals another set of weights. This is useful when for instance the signals in one of the channels are more or less saturated. Saturation may bias data already at signal levels of around 50000 out of 65535, because a subset of pixels may be saturated (Lyng et al, 2004).

Analogously to the above, red and green signals weights can be united into probe weights by the square root of the average squared signal weight.

Signal weights are also used when doing a multiscan calibration. The resulting average scan for each channel gets a new set of signal weights calculated from the ones used to calibrate the scans.

Assign weights in aroma

In aroma, probe weights w are assigned to an RGData object rg using the setProbeWeights(rg, weights=w) method. Signal weights w are assigned to channel ch using setSignalWeights(rg, channel=ch, weights=w).

The methods will assure that the weights are valid. Data-point weights are automatically calculated out of signal or probe weights, if they exist.

Weighted normalization

In aroma, when normalizing, data point weights are automatically calculated from probe weights, if assigned. In this section we illustrate the effect of weighted normalization for both affine and curve-fit normalization.

Weighted affine normalization

Affine normalization (of a single array) with and without weights is illustrated in the below figure. To a real data set, we have "added" 50 outliers in the green channel. In the (A,M) scatter plot, these outliers can be seen at A > 10 and M < -5. As seen by the middle plot, the outliers affect the normalization although it is using Tukey's biweight. By downweighting the outliers thousand times, the result is a much better estimate and normalization.

(A,M) plot (A,M) plot (A,M) plot
Figure: Non-weighted and weighted affine normalization with a distinct set of 50 outliers. Left: (A,M) plot before normalization. The outliers can be seen at negative log-ratios and high intensities. Mid: (A,M) plot after non-weighted normalization. Although the estimate is robustified by Tukey's biweight function, it is affected quite a bit by the outliers. Right: (A,M) plot after weighted normalization where the outliers o have been downweighted (woutliers=1/1000).

Weighted curve-fit normalization

By default, curve-fit normalization in aroma is utilizing (extra robustified) loess. Loess (as robustified in aroma) is a bit more robust against outliers than affine normalization. [We hope to implement a similar level of robustness to the affine model.] For this reason, to illustrate the effect of weighted normalization, we "add" 500 (instead of 50) outliers in the green channel. See figure below. The outliers will affect the normalization as seen in the middle plot. By downweighting the outliers thousand times, the result is a much better estimate and normalization.

(A,M) plot (A,M) plot (A,M) plot
Figure: Non-weighted and weighted loess normalization with a distinct set of 500 outliers. Left: (A,M) plot before normalization. The outliers can be seen at negative log-ratios and high intensities. Mid: (A,M) plot after non-weighted normalization. Although the estimate is robustified by Tukey's biweight function, it is affected quite a bit by the outliers. Right: (A,M) plot after weighted normalization where the outliers o have been downweighted (woutliers=1/1000). aroma.light/inst/rsp/NormalizationWithWeights/TypesOfWeights.html0000644000175000017500000002060714136047216025232 0ustar nileshnilesh aroma - Types of Weights

Types of Weights

Author: Henrik Bengtsson
Created: 2005-02-01
Last updated: 2005-02-05
Phase: very alpha
Comments: Feedback is appreciated.

Back...

Introduction

For several normalization and calibration methods the estimation of the normalization (or calibration) function can be done with weights. Commonly, weights are proportional to a quality measure, that is, the less quality we assign to a signal, the less influence (weight) it should have on the estimation of calibration and normalization functions.

General about weights

The definition of a weight is a single value in [0,1]. Weights outside this range and missing values (NA) are not allowed.

Below, we will define different entities such as signals, probes/spots, probesets, channels, arrays, and data-points. To any of these entities weights may be assigned.

Signals and signal weights

A signal is a single value. A signal weight is a weight assigned to a signal. Thus, it is for entities within an array and never across/between arrays.

Example: In two-color microarray data, there are two signals for each spot, i.e. the red or the green signals, and each of them can be assigned a different signal weight. Typically, such signal weights are represented by an Nx2 matrix, where N is the number of probes/spots on the array.

Example: In Affymetrix microarray data, which is single-channel data, there is one signal per probe (in turn part of a probe set). Each such probe can be assigned a signal weight. Typically, such signal weights are represented by an Nx1 matrix, where N is the number of probes/spots on the array.

Probes and probe weights

A probe is the smallest entity (not considering image pixels) on the array that measures the amount of hybridized samples in one or several channels.

Example: For two-color microarrays, a probe is a spot.

Example: For Affymetrix arrays, a probe can be either a perfect match probe (PM) or a mismatch probe (MM).

A probe weight is a weight assigned to a probe/spot (not a probe set).

Example: For two-color data, the signals in the two channels for a given spot share the same probe weight.

Example: For four-color data, the signals in the four channels for a given spot share the same probe weight.

Example: For single-channel data such as Affymetrix data, the probe weight is identical to a signal weight.

Typically, above signal weights are represented by an Nx1 matrix, where N is the number of probes/spots on the array.

The probe weight of probe i must be equal to the mean of its signal weights.

Probesets and probeset weights

A probeset consists of a set of probes.

Example: For two-color microarrays, probesets are not defined.

Example: For Affymetrix arrays, a probeset is the set of perfect match (PM) and mismatch (MM) probes corresponding to the same gene.

A probeset weight is a weight assigned to a probeset.

Since Affymetrix is single-channel arrays, typically the above probeset weights are represented by an Nx1 matrix, where N is the number of probesets.

The probeset weight for probeset j must be equal to the mean of its probe weights (which in this case are identical the signal weights) by averaging the probe weights for each probeset.

Data points and data-point weights

The definition of a data point depends on the context. It may be assigned to entities within an array, but also across/between arrays.

Example: (Paired data-point weight). In paired-channel normalization, such as curve-fit normalization (a.k.a. lowess intensity normalization), two and only two channels are normalized together at the same time, e.g. red and the green channels in two-color data, or two two single-channel data set obtained from two different Affymetrix arrays. Here a data point is constituted by two signals, e.g. X[i] = (G[i],R[i]). A data-point weight is assigned to the pair of signals corresponding to the same spot or gene, e.g. for lowess normalization a data-point weight is assigned to a log-ratio and a log-intensity.

Example: (Multi-channel data point weight). In multi-channel normalization, such as affine normalization or quantile normalization (within a singel array and/or across multiple arrays), each data point is consituted by multiple signals, e.g. for J two-color arrays it is X[i] = (R[i,1],G[i,1],...,R[i,J],G[i,J]). To this data point, a data-point weight can be assigned.

Typically, above signal weights are represented by an Nx1 matrix, where N is the number of data points.

Data-points weights can be generated from signal or probe weights, by averaging them for each data point.

If not stated elsewise, arguments named weights are assumed to take data-point weights.

Arrays and array weights

An array weight is a weight assigned to an array, that is, to the complete set of signals in all channels constituting an array.

By definition, a channel weight can never apply across/between arrays.

Constraints: The array weight should be equal to the average of the channel (and signal/probe/probeset) weights. Hence, for single-channel arrays, the array weight should be identical to the channel weight.

Channels and channel weights

A channel weight is a weight assigned to a channel, that is, to the set of signals constituting a channel.

By definition, a channel weight can never apply across/between arrays.

Example: In two-color data, two channel weights can exist.

Example: In Affymetrix (single-channel) data, only one channel weights can exists and is therefore identical to an array weight.

Constraints: The channel weight should be equal to the average of all signal/probe/probeset weights in the channel.

Combining signal weights into spot weights

Spot weights can be generated from signal weights.

For a given spot, the spot weight is calculated as the arithmetical mean of the signal weights.

Combining signal weights into data-point weights

Data-point weights can be generated from signal weights.

For a given data point, the data point weight is calculated as the arithmetical mean of the signal weights.

Combining spot weights into data-point weights

Data-point weights can be generated from spot weights.

For a given data point, the data point weight is calculated as the arithmetical mean of the spot weights.

Restrictions

Note, currently weights are only supported by the RGData class. [The plan is to make this class the "main" class.]

Currently, it is only methods that explicitly say they support weights which use weights. For all other methods, weights are ignored.

[Package aroma Index]
aroma.light/inst/rsp/AffineNormalization/0000755000175000017500000000000014136047216020335 5ustar nileshnilesharoma.light/inst/rsp/AffineNormalization/AffineNormalization.html.rsp0000644000175000017500000002302214136047216025764 0ustar nileshnilesh

Affine normalization

Introduction

Affine normalization applies to all types of microarray platforms, both single-channel arrays such as Affymetrix, two-channel spotted microarrays, as well as multi-channel microarrays. It normalize both within and between-arrays at the same time.

In Bengtsson & Hssjer (2006) we carry out a very detailed study on how biases in each channel introduce so called intensity-dependent log-ratios among other systematic artifacts. This type of bias must be corrected for before any other normalization methods should be applied. Thus, do always apply affine normalization.

(A,M) plot (A,M) plot
Figure: Affine normalization. Left: (A,M) plot before normalization. Right: (A,M) plot after normalization. Due to the lower signal-to-noise ratios for log-ratios at lower intensities, we do expect to get a strong fan-out effect at lower intensities. The "inbalance" between postive and negative log-ratios at low intensities is due to fact that the sensitivity was higher in the green compared to the red channel (although both were scanned with PMT 600V).

Model

Data with (additive) bias in each channel is said to be affinely transformed. Data without such bias, is said to be linearly (proportionally) transformed. Ideally, observed signals (data) is a linear (proportional) function of true gene expression levels, but this is rarely the case. The affine model is

yc,i = ac + bc*xc,i + ec,i

where yc,i is the observed expression level in channel c for gene i, xc,i is the true expression level, and ec,i is noise. Scale effects, such as PMT settings and labelling efficiency, has a multiplicative effect (bc) on the signal, and scanner background has an additive effect (ac). Here we assume the same scale and offset for all signals across the array.

Assumptions

We do not assume proportional observations. The scanner bias is real evidence that assuming linearity is not correct. Affine normalization corrects for affine transformation in data. Without control spots it is not possible to estimate the bias in each of the channels but only the relative bias such that after normalization the effective bias are the same in all channels. This is why we call it normalization and not calibration. For instance, for two-color data, the normalization will assure that aR = bR/bG*aG will be true afterward. For details, see Bengtsson & Hssjer (2006).

But if the channel biases are small, what is the problem?

What we mean by small is a matter of definition and because of the logarithmic transform when calculating log-ratios, a small bias has a large impact at low intensities. Consider all non-differentially expressed genes in a two-color experiment, i.e. xR,i == xG,i == xi where i is any non-differentially expressed gene. Assuming zero-noise, the log-ratio for these genes are then Mi = log_2(aR+bR*xi)/(aG+bG*xi). To start, set bR=bG=1 and calculate Mi for genes at low and high intensities for, say, (aG,aR)=(0,0), (aG,aR)=(10,20), and (aG,aR)=(200,20). Then try the same, but with different values on bR and bG.

In <%=pkg%>

In its simplest form, affine normalization is done by: 

Xn <- normalizeAffine(X)
<%topic <- "normalizeAffine.matrix"%> [help] [example]

Symmetric log-ratios afterward

Equal effective bias in all channels is much better. First of all, any intensity-dependent bias in the log-ratios is removed for all non-differentially expressed genes. There is still an intensity-dependent bias in the log-ratios for differentially expressed genes, but this is now symmetric around log-ratio zero.

Normalization of multiple channels?

The affine normalization can be applied to multiple single-channel arrays, as well as single or multiple two- or multi-colour arrays. We recommend that all channels and all arrays are normalized together.

Why is between-array scale normalization not needed?

Affine normalization will (by default and recommended) normalize all arrays together and at once. This will guarantee that all arrays are "on the same scale". Thus, it not recommended to apply a classical between-array scale normalization afterward. Moreover, the average log-ratio will be zero after an affine normalization.

Why a fan-out effect?

Note that for a perfect affine normalization you should expect much higher noise levels in the log-ratios at lower intensities than at higher. It should also be approximately symmetric around zero log-ratio. In other words, a strong fan-out effect is a good sign.

Why asymmetric tails at low intensities?

Due to different noise levels in red and green channels, different PMT settings in different channels, plus the fact that the minimum signal is zero, "odd shapes" may be seen in the log-ratio vs log-intensity graphs at lower intensities. Typically, these show themselves as non-symmetric in positive and negative log-ratios. Note that you should not see this at higher intensities.

What about curvature at high intensities?

Note that an affine normalization will only remove curvature in the log-ratios at lower intensities. If a strong intensity-dependent bias at high intensities remains, this is most likely due to saturation effects, such as too high PMT settings or quenching. If there is a strong intensity-dependent effect left after the affine normalization, we recommend, for now, that a subsequent curve-fit or quantile normalization is done. Which one, we do not know.

Why negative signals?

By default, 5% of the normalized signals will have a non-positive signal in one or both channels. This is on purpose, although the exact number 5% is chosen by experience. The reason for introducing negative signals is that they are indeed expected. For instance, when measure a zero gene expression level, there is a chance that the observed value is (should be) negative due to measurement noise. (For this reason it is possible that the scanner manufacturers have introduced scanner bias on purpose to avoid negative signals, which then all would be truncated to zero.) To adjust the ratio (or number) of negative signals allowed, use for example normalizeAffine(X, constraint=0.01) for 1% negative signals. If set to zero (or "max") only as much bias is removed such that no negative signals exist afterward. Note that this is also true if there were negative signals on beforehand. Do not apply curve-fit normalization to "correct" for this.

Alternatives to affine normalization

Curve-fit normalization

Curve-fit normalization methods such as lowess normalization are basically designed based on linearity assumptions and will for this reason not correct for channel biases. Curve-fit normalization methods can by definition only be applied to one pair of channels at the time and do therefore require a subsequent between-array scale normalization. We believe that between-array scale normalization is rather ad hoc. Moreover, curve-fit normalization is not capable of adjusting for channel biases with the effect that if there are channel biases log-ratios will be compressed at low intensities. Example: 

Xn <- normalizeCurveFit(X)
<%topic <- "normalizeCurveFit.matrix"%> [help] [example]

Compare this with the result from normalizeAffine(X). Recall that the fan-out effect is expected.

Quantile normalization

Affine normalization can be though of a special case of quantile normalization that is more robust than the latter. See Bengtsson & Hssjer (2006) for details. Quantile normalization is better than curve-fit normalization methods, but less robust than affine normalization, especially at extreme (low and high) intensities. For this reason, we do recommend to use affine normalization first, and if this is not satisfactory, quantile normalization may be applied.

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User's guide to <%=pkg%>

Author: Henrik Bengtsson
Last updated: 2006-07-20
Comments: Feedback is appreciated.

Abstract

The <%=pkg%> package provides methods for basic microarray analysis, especially methods to pre-process (calibrate and normalize) feature intensities. Initially the methods was for two-color data, but several apply also to single- and multi-channel data. The majority of the methods operate on basic data types such as matrices and lists of vectors, making them easy to use utilize by other packages, but also as is.

Table of Contents

  1. Introduction
  2. Multiscan calibration (scanner calibration)
  3. Affine normalization
  4. Additional documentation
  5. References

Introduction

With microarray measurement we try to test a few different biological hypothesis. For instance, in gene-expression analysis we wish to test if a gene is differentially expressed (compared to a reference). In genotyping analysis, we wish to classify the genotypes along the chromsomes to be either AA, AB, or BB. Several other questions may be asked. In order for the microarray analysis to be successful, we need to be able to estimate the test variables with both high accuracy (low bias) and high precision (small spread). Typically, this means that we need to get good estimates of the true biological signal.

<%@include file="preprocessing.html.rsp"%> <%@include file="bgcorrection.html.rsp"%>

A simple model

One of the simplest models for transformation of signals is the affine model. Formally, an affine transformation function rescales the signal and then adds an offset (background). We might assume that both the scale factor and the added offset is constant for each channel (and array), that is:

yc,i = f(xc,i) + ec,i = bc*xc,i + ac + ec,i.
Compare this with the above model for additive background. The main difference is that here we assume ac,i = ac for all features i=1,2,...,I. Several of the pre-processing methods implemented in the <%=pkg%> assume (directly or indirectly) that the relationship between the true and observed signals can be approximated by this model.

The reason for not calling it a linear model is that formally, in mathematics, do linear transforms not include an additive term. By using the term affine we wish to make this distinction explicit.

In <%=pkg%>

To load the <%=pkg%> package, call: 

library(<%=pkg%>)
<%@include file="MultiscanCalibration/MultiscanCalibration.html.rsp"%> <%@include file="AffineNormalization/AffineNormalization.html.rsp"%>

Additional documentation

Package's help index

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aroma.light/NAMESPACE0000644000175000017500000001006414136047216014035 0ustar nileshnilesh# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # IMPORTS # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - importFrom("R.oo", "throw") importFrom("R.methodsS3", "setMethodS3") importFrom("R.oo", "Package") importFrom("R.oo", "getPosition") importFrom("R.oo", "startupMessage") importFrom("R.utils", "Arguments") importFrom("R.utils", "Verbose") importFrom("R.utils", "pushState") importFrom("R.utils", "popState") importFrom("R.utils", "enter") importFrom("R.utils", "exit") importFrom("R.utils", "printf") importFrom("R.utils", "hpaste") importFrom("R.utils", "cat") ## Multi-sources: R.utils, base importFrom("R.utils", "use") importFrom("matrixStats", "anyMissing") importFrom("matrixStats", "colMedians") importFrom("matrixStats", "colMins") importFrom("matrixStats", "colQuantiles") importFrom("matrixStats", "rowMedians") importFrom("matrixStats", "rowWeightedMeans") importFrom("matrixStats", "rowWeightedMedians") importFrom("grDevices", "xy.coords") importFrom("graphics", "abline", "axis", "box", "lines", "mtext", "par", "plot", "points", "polygon") importFrom("stats", "IQR", "approx", "cor", "density", "integrate", "loess", "loess.control", "lowess", "mad", "median", "na.omit", "predict", "sd", "smooth.spline", "weighted.mean", "weights") importFrom("utils", "getAnywhere", "packageDescription") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # EXPORTS # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Export all public methods, that is, those without a preceeding dot # in their names. exportPattern("^[^\\.]") # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # S3 methods # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # data.frame S3method("plotDensity", "data.frame") # default S3method("distanceBetweenLines", "default") S3method("normalizeFragmentLength", "default") S3method("normalizeQuantile", "default") S3method("robustSmoothSpline", "default") S3method("sampleTuples", "default") # density S3method("findPeaksAndValleys", "density") S3method("plotDensity", "density") # list S3method("averageQuantile", "list") S3method("normalizeAverage", "list") S3method("normalizeDifferencesToAverage", "list") S3method("normalizeQuantileRank", "list") S3method("normalizeQuantileSpline", "list") S3method("plotDensity", "list") # lowess S3method("predict", "lowess") # matrix S3method("averageQuantile", "matrix") S3method("backtransformAffine", "matrix") S3method("backtransformPrincipalCurve", "matrix") S3method("backtransformXYCurve", "matrix") S3method("calibrateMultiscan", "matrix") S3method("fitIWPCA", "matrix") S3method("fitPrincipalCurve", "matrix") S3method("fitXYCurve", "matrix") S3method("iwpca", "matrix") S3method("medianPolish", "matrix") S3method("normalizeAffine", "matrix") S3method("normalizeAverage", "matrix") S3method("normalizeCurveFit", "matrix") S3method("normalizeLoess", "matrix") S3method("normalizeLowess", "matrix") S3method("normalizeQuantileRank", "matrix") S3method("normalizeQuantileSpline", "matrix") S3method("normalizeRobustSpline", "matrix") S3method("normalizeSpline", "matrix") S3method("plotDensity", "matrix") S3method("plotMvsA", "matrix") S3method("plotMvsAPairs", "matrix") S3method("plotMvsMPairs", "matrix") S3method("plotXYCurve", "matrix") S3method("rowAverages", "matrix") S3method("sampleCorrelations", "matrix") S3method("wpca", "matrix") # numeric S3method("backtransformPrincipalCurve", "numeric") S3method("callNaiveGenotypes", "numeric") S3method("findPeaksAndValleys", "numeric") S3method("fitNaiveGenotypes", "numeric") S3method("normalizeQuantileRank", "numeric") S3method("normalizeQuantileSpline", "numeric") S3method("normalizeTumorBoost", "numeric") S3method("pairedAlleleSpecificCopyNumbers", "numeric") S3method("plotDensity", "numeric") S3method("plotXYCurve", "numeric") # smooth.spline S3method("likelihood", "smooth.spline") # SmoothSplineLikelihood S3method("print", "SmoothSplineLikelihood") # XYCurveFit S3method("lines", "XYCurveFit")