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;cEWzF⻥< @B8>39dEUxA5q; /I?p-:4MlEk]˳OAn&",٥b{ Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology License: GPL (>= 2) LazyData: yes LinkingTo: Rcpp Imports: Rcpp, stats, Biobase, methods Suggests: testthat Depends: R (>= 3.3) NeedsCompilation: yes git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_19 git_last_commit: d74c792 git_last_commit_date: 2024-04-30 Repository: Bioconductor 3.19 Date/Publication: 2024-04-30 Packaged: 2024-05-01 05:43:54 UTC; biocbuild ROTS/inst/0000755000175100017510000000000014614353231013340 5ustar00biocbuildbiocbuildROTS/inst/CITATION0000644000175100017510000000111714614306504014476 0ustar00biocbuildbiocbuildcitHeader("To cite package 'ROTS' in publications use:") bibentry(bibtype = "Article", author = personList(as.person("Tomi Suomi"), as.person("Fatemeh Seyednasrollah"), as.person("Maria Jaakkola"), as.person("Thomas Faux"), as.person("Laura Elo")), doi = "10.1371/journal.pcbi.1005562", journal = "PLoS computational biology", month = "May", number = "5", pages = "e1005562", pmid = "28542205", title = "ROTS: An R package for reproducibility-optimized statistical testing.", url = "http://www.ncbi.nlm.nih.gov/pubmed/28542205", volume = "13", year = "2017" ) ROTS/inst/doc/0000755000175100017510000000000014614353231014105 5ustar00biocbuildbiocbuildROTS/inst/doc/ROTS.pdf0000644000175100017510000073245514614353231015407 0ustar00biocbuildbiocbuild%PDF-1.5 % 32 0 obj << /Length 159 >> stream concordance:ROTS.tex:ROTS.Rnw:1 27 1 1 0 81 1 1 2 1 0 2 1 3 0 1 2 2 1 1 2 1 0 1 1 6 0 1 2 2 1 1 2 1 0 1 1 7 0 1 2 4 1 1 2 30 0 1 2 5 1 1 2 5 0 2 2 5 0 1 2 27 1 endstream endobj 41 0 obj << /Length 514 /Filter /FlateDecode >> stream xڍn0E .I R&]q"E [&킵l63n.8$sg '+k'SʼnpFkV#޶7RBY =l_K6).q2i394`O -uc,RI_{Qzʼ! k<>2h\n8]x2/hInn~%tƣ/Yּ1 pð(AuO%3ג0J+L'mjB[ͱX^`9JJ0.hEla$XHC?C\ $w-KHH~ۭ!1Hp F+i_')<^9ۗA.CGLUCL/N#dP8lV$H߸Rqh{ 2>tNywv55q2r73x_X0˩l^Vڔqlj|䪝M endstream endobj 53 0 obj << /Length 2757 /Filter /FlateDecode >> stream xڽɲБ i;]29xf93_^٩E 7p>?_|CDE"ϓjUY'Y^?#qHxE4÷[NY{h*<$=ęH4ϕQ1kYԧQ4x扶͢1H:|Q2Lvi^$OxA`d"aB$m:-UU4 dqXFr&Ǹ,DF]o{zпANxr։ަe' w }7=FOJ Hg50Ti^j/ g#OL˕4N#*K0RǦ^ jw& &SOW5)Xu[߲P{;`Z4Fz5;/Vj"8KVE uFJO#/RR!`)HF޲bb<=_^˕9lPYMx僚/o8Kpp6QZ倳b~:=تPzqR}"3\~Rtr7Jk^Sh*P(Qh/JWdS!K;$Sgqխsx/" OOrdDxcX$ e,/8SN >BEDN(8e!rWU} Wdtiq!H#|S@q`ax) ۀõ'F<͙X^".*pR!Cmj>XlppMp DB1GE,˒&$|=b D묆_yԕ*Pj!s"MJfHt_jz;d,qሜqD{ĩAIѮ緕I-O$}}oἸ;kCzY6XU`Hz'-em "XHE"rijpE@ו.@>]f7bA9[ S.i"<+d$JOлdU!gIٮl} L*^"@NTM~4@ *dlERT힨w!o@j%WPI[w3h"mSM]*VN!ߞNl·4(9\JQcksGp[kpp;l t~Qw1YRد#/-lc̉4OV-0mɚ~86˰Ϻ1 Űk**YVK W.S7E'k}ջc\DPOe9%(L%O;hL4s}޺ 4>2ĨmHx=7PTDHj[_iuuׄ] 6q'bKBIigSR$F[8[!91SzunG! 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Please note that the order of the samples in the data matrix should be exactly the same as the groups vector defined. <<>>= groups = c(rep(0,3), rep(1,3)) groups @ The ROTS function performs the final differential expression testing. The user can set the function parameters before running the analysis: <>= results = ROTS(data = input, groups = groups , B = 100 , K = 500 , seed = 1234) names(results) @ In this example, we set the number of bootstrapping (B) and the number of top-ranked features for reproducibility optimization (K) to 100 and 500 respectively, to reduce running time of the example. In real analysis it is preferred to use a higher number of bootstraps (e.g. 1000). The optimization parameters a1 and a2 should always be non-negative. The output of ROTS function includes test statistic (d), estimated \textit{p}-value (pvalue), False Discovery Rate (FDR), optimized test statistic parameters and top list size (a1, a2, k), optimized reproducibility value (R) and Z-score (Z). In general, the Z-score and reproducibility are the main indicators to decide the success of differential expression analysis. As a rule of thumb, reproducibility Z-scores below 2 indicate that the data or the statistics are not sufficient for reliable detection.\\[1em] Finally, it is possible to summarize the results based on criteria selected by the user. For instance, the following code lists the top ranked differentially expressed features with FDR below 0.05: <

>= summary(results, fdr = 0.05) @ \newpage \section{Visualization} Results can also be visualized using the standard plot command: <>= plot(results, fdr = 0.05, type = "volcano") @ <>= plot(results, fdr = 0.05, type = "heatmap") @ \section{References} Elo, L.L. et al., \emph{Reproducibility-optimized test statistic for ranking genes in microarray studies}. IEEE/ACM transactions on computational biology and bioinformatics, 5, 423-431, 2008. \\[1em] Elo, L.L. et al. \emph{Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets}. Briefings in bioinformatics, 10, 547-555, 2009. \\[1em] Seyednasrollah et al. \emph{ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer}. Nucleic Acids Research, 2015. \\[1em] Pursiheimo et al. \emph{Optimization of Statistical Methods Impact on Quantitative Proteomics Data}. J. Proteome Res., 2015. \end{document}ROTS/man/0000755000175100017510000000000014614306504013137 5ustar00biocbuildbiocbuildROTS/man/affySpikeIn.Rd0000644000175100017510000000237414614306504015644 0ustar00biocbuildbiocbuild\name{affySpikeIn} \alias{affySpikeIn} \alias{affySpikeIn.gnames} \alias{affySpikeIn.L} \alias{spikedgene} \docType{data} \title{Gene expression data from the Affymetrix spike-in experiment} \description{ The Affymetrix HG-U95Av2 spike-in data contains two groups of 12 samples. In this carefully controlled experiment, it is known that 14 genes are differentially expressed between the two sample groups, while the rest of the genes are equally expressed. In order to reduce the running time of the vignette example, this package uses a subset of 1000 genes of the original dataset including all the spiked genes with 5 samples from each group. The preprocessed RMA-normalized data were obtained from the Bioconductor 2.2 package \code{DEDS}. For more details about the spike-in experiment, see http://www.affymetrix.com. } \usage{affySpikeIn} \format{ \describe{ \item{\code{affySpikeIn}}{a numeric matrix of gene expression levels with 12626 rows (genes) and 24 columns (samples).} \item{\code{affySpikeIn.L}}{a vector indicating the sample groups.} \item{\code{affySpikeIn.gnames}}{a character vector containing the names of the genes.} \item{\code{spikedgene}}{a numeric vector indicating the locations of the 14 spiked genes.} } } \keyword{datasets} ROTS/man/plot.ROTS.Rd0000644000175100017510000000233314614306504015173 0ustar00biocbuildbiocbuild\name{plot.ROTS} \alias{plot.ROTS} \title{ Plotting of a ROTS object } \description{ Plots the ROTS objects created with the ROTS package. } \usage{ \method{plot}{ROTS}(x, fdr=0.05, type=NULL, labels=FALSE, ...) } \arguments{ \item{x}{A \code{ROTS} object created from differential expression testing run by ROTS.} \item{fdr}{Selected cutoff for FDR value.} \item{type}{Type of plot to be created. Options are: 'volcano', 'heatmap', 'ma', 'reproducibility', 'pvalue', 'pca'.} \item{labels}{Option to print labels for differentially expressed features.} \item{...}{Other arguments passed to the plot function.} } \details{ This function plots the results from a ROTS object using given false discovery rate threshold. } \value{ Plots the results from ROTS object. } \author{ Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo\cr Maintainer: Tomi Suomi } \seealso{ \code{\link{affySpikeIn}} } \examples{ ## ROTS-statistic for the Affymetrix spike-in data. rots.out <- ROTS(data = affySpikeIn, groups = c(rep(0,5), rep(1,5)), B = 100, K = 500 , seed = 1234) ## Plotting of the ROTS results. plot(rots.out, type="volcano") } \keyword{ math } ROTS/man/ROTS.Rd0000644000175100017510000001337514614306504014226 0ustar00biocbuildbiocbuild\name{ROTS} \alias{ROTS} \alias{ROTS-package} \alias{print.ROTS} \title{Reproducibility-Optimized Test Statistic (ROTS)} \description{ Calculates the reproducibility-optimized test statistic (ROTS) for ranking genes in order of evidence for differential expression in two-group or multi-group comparisons. } \usage{ ROTS(data, groups, B = 1000, K = NULL, paired = FALSE, seed = NULL, a1 = NULL, a2 = NULL, log = TRUE, progress = FALSE, verbose = TRUE, time = NULL, event = NULL) } \arguments{ \item{data}{A numeric data matrix or an ExpressionSet instance, in which rows correspond to genes and columns correspond to samples.} \item{groups}{A vector indicating the sample groups.} \item{B}{An integer specifying the number of bootstrap and permutation resamplings (default 1000).} \item{K}{An integer indicating the largest top list size considered. If no value is given, 1/4 of the features are used.} \item{paired}{A logical indicating whether a paired test is performed. The samples are expected to be in the same order in both groups.} \item{seed}{An integer seed for the random number generator.} \item{a1, a2}{Non-negative parameters. See details section for further information.} \item{log}{A logical (deafult TRUE) indicating whether input data is log2 scaled. This information is only used to calculate log fold change.} \item{progress}{A logical indicating if additional progress bars are shown.} \item{verbose}{A logical indicating if messages are shown.} \item{time}{Time to event (for survival data).} \item{event}{Censoring information (for survival data): 1=event, 0=censored.} } \details{ The reproducibility-optimization procedure ROTS enables the selection of a suitable gene ranking statistic directly from the given dataset. In two-group comparisons, the statistic is optimized among a family of t-type statistics d = m/(a1+a2*s), where m is the absolute difference between the group averages, s is the pooled standard error, and a1 and a2 are the non-negative parameters to be optimized. Two special cases of this family are the ordinary t-statistic (a1=0, a2=1) and the signal log-ratio (a1=1, a2=0). The optimality is defined in terms of maximal overlap of top-ranked genes in group-preserving bootstrap datasets. Importantly, besides the group labels, no a priori information about the properties of the data is required and no fixed cutoff for the gene rankings needs to be specified. For more details about the reproducibility-optimization procedure, see Elo et al. (2008). The user is given the option to adjust the largest top list size considered in the reproducibility calculations, since lowering this size can markedly reduce the computation time. In large data matrices with thousands of rows, we generally recommend using a size of several thousands. In smaller data matrices, and especially if there are many rows with only a few non-missing entries, the size of K should be decreased accordingly. ROTS tolerates a moderate number of missing values in the data matrix by effectively ignoring their contribution during the operation of the procedure. However, each row of the data matrix must contain at least two values in both groups. The rows containing only a few non-missing values should be removed; or alternatively, the missing data entries can be imputed using, e.g., the K-nearest neighbors imputation, which is implemented in the Bioconductor package \code{impute}. ROTS assumes the input data matrix is log2 transformed (the default for log parameter is set to TRUE). Although, this only affects fold change values, we recommend setting log parameter to FALSE if the input matrix is not log transformed to avoid downstream confusions. If the parameter values a1 and a2 are set by the user, then no optimization is performed but the statistic and FDR-values are calculated for the given parameters. The false discovery rate (FDR) for the optimized test statistic is calculated by permuting the sample labels. The results for all the genes can be obtained by setting the FDR cutoff to 1. } \value{ \code{ROTS} returns an object of class \code{ROTS}, which is a list containing the following components \item{data}{the expression data matrix.} \item{B}{the number of bootstrap and permutation resamplings.} \item{d}{the value of the optimized ROTS-statistic for each gene.} \item{pvalue}{the corresponding pvalues.} \item{FDR}{the corresponding FDR-values.} \item{a1}{the optimized parameter a1.} \item{a2}{the optimized parameter a2.} \item{k}{the optimized top list size.} \item{R}{the optimized reproducibility value.} \item{Z}{the optimized reproducibility Z-score.} \code{print} prints the optimized parameters a1 and a2, the optimized top list size and the corresponding reproducibility values. \code{summary} summarizes the results of a ROTS analysis. If \code{fdr} and \code{num.genes} are not specified, then the optimized parameters a1 and a2, the optimized top list size and the corresponding reproducibility values are shown. If \code{fdr} or \code{num.genes} is specified, then also the gene-specific information is shown for the genes at the specified FDR-level or top list size, respectively. } \references{ Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL.\cr ROTS: An R package for reproducibility-optimized statistical testing.\cr PLoS Comput Biol 2017; 13: e1005562. } \author{ Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo\cr Maintainer: Tomi Suomi } \seealso{ \code{\link{affySpikeIn}} } \examples{ ## ROTS-statistic for the Affymetrix spike-in data. rots.out <- ROTS(data = affySpikeIn, groups = c(rep(0,5), rep(1,5)), B = 100, K = 500 , seed = 1234) ## Summary of the ROTS results. rots.summary <- summary(rots.out, fdr = 0.05) } \keyword{ math }ROTS/man/summary.ROTS.Rd0000644000175100017510000000314414614306504015713 0ustar00biocbuildbiocbuild\name{summary.ROTS} \alias{summary.ROTS} \title{ Summary of a ROTS object } \description{ Summarizes the differential testing results from ROTS package. } \usage{ \method{summary}{ROTS}(object, fdr=NULL, num.genes=NULL, verbose=TRUE, ...) } \arguments{ \item{object}{a \code{ROTS} object created from differential expression testing run by ROTS.} \item{fdr}{ selected cutoff for FDR value.} \item{num.genes}{ selected cutoff number for number of differentially detected features. } \item{verbose}{ If TRUE (default), summary function will print out 10 first detections which fulfill the cutoff criteria. } \item{...}{ other arguments passed to the summary function. } } \details{ This function returns the summary information (including row number, test-statistic, pvalue and FDR value) for the selected features. } \value{ Returns a matrix where the rows are the selected features and columns are the Row number, ROTS-statistic, pvalue and FDR. } \references{ Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL.\cr ROTS: An R package for reproducibility-optimized statistical testing.\cr PLoS Comput Biol 2017; 13: e1005562. } \author{ Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo\cr Maintainer: Tomi Suomi } \seealso{ \code{\link{affySpikeIn}} } \examples{ ## ROTS-statistic for the Affymetrix spike-in data. rots.out <- ROTS(data = affySpikeIn, groups = c(rep(0,5), rep(1,5)), B = 100, K = 500 , seed = 1234) ## Summary of the ROTS results. rots.summary <- summary(rots.out, fdr = 0.05) } \keyword{ math } ROTS/man/upsSpikeIn.Rd0000644000175100017510000000111714614306504015520 0ustar00biocbuildbiocbuild\name{upsSpikeIn} \alias{upsSpikeIn} \docType{data} \title{Protein expression data from the CPTAC Technology Assessment (Study 6)} \description{ The data contains preprocessed expression values from CPTAC Technology Assessment, where equimolar concentrations of 48 human proteins (Sigma UPS 1) are spiked in five different concentrations into yeast proteome background and then processed. The example data contains only two groups from the study 6. } \usage{upsSpikeIn} \format{ \describe{ \item{\code{upsSpikeIn}}{A numeric matrix of protein expressions.} } } \keyword{datasets} ROTS/NAMESPACE0000644000175100017510000000071314614306504013604 0ustar00biocbuildbiocbuild## Export functions from the ROTS-package export(ROTS) S3method(print, ROTS) S3method(summary, ROTS) S3method(plot, ROTS) ## Load shared library ROTS.so/dll useDynLib(ROTS) importFrom(Rcpp, evalCpp) importFrom(stats, median, na.omit, heatmap, loess.smooth, prcomp) importFrom(utils, setTxtProgressBar, txtProgressBar) importFrom(graphics, plot, points, abline, legend, lines, text, hist, par) importFrom(grDevices, colorRampPalette) importFrom(methods, is) ROTS/NEWS0000644000175100017510000000053214614306504013063 0ustar00biocbuildbiocbuildCHANGES IN ROTS 1.27.1 - Added support for survival analysis CHANGES IN ROTS 1.6.0 - Added support for testing multiple groups - Bug fixes CHANGES IN ROTS 1.0.0 - Added log fold change in output. - Added default plotting for ROTS class objects - Added support for paired tests - Updated vignette - Bug fixes - Modified for Bioconductor release ROTS/R/0000755000175100017510000000000014614306504012565 5ustar00biocbuildbiocbuildROTS/R/biggerN.R0000644000175100017510000000114214614306504014263 0ustar00biocbuildbiocbuild`biggerN` <- function(x, y) { x <- sort(x, decreasing=TRUE, na.last=TRUE) # sort x in decreasing order y <- sort(y, decreasing=TRUE, na.last=TRUE) # sort y in decreasing order a <- match(x, x) # vector of the positions of (first) matches of the first argument in the second b <- x %in% y # a logical vector indicating if there is a match or not for its left operand z <- sort(c(x, y), decreasing=TRUE, na.last=TRUE) # sort c(x,y) in decreasing order d <- match(x, z) # vector of the positions of (first) matches of the first argument in the second return(d - a + b) } ROTS/R/bootstrapSamples.R0000644000175100017510000000075414614306504016260 0ustar00biocbuildbiocbuild`bootstrapSamples` <- function(data, B, labels, paired){ samples <- matrix(nrow=B, ncol=length(labels)) for(i in 1:B){ for (label in unique(labels)) { pos <- which(labels==label) samples[i,pos] <- sample(pos, length(pos), replace=TRUE) } } if (paired) { for(i in 1:B){ for (label in unique(labels)[-1]) { pos <- which(labels==label) samples[i,pos] <- samples[i,which(labels==1)]+pos[1]-1 } } } return(samples) } ROTS/R/calculateFDR.R0000644000175100017510000000134614614306504015205 0ustar00biocbuildbiocbuild`calculateFDR` <- function(observed, permuted, progress) { observed <- abs(observed) permuted <- abs(permuted) ord <- order(observed, decreasing=TRUE, na.last=TRUE) a <- observed[ord] A <- matrix(NA, nrow=length(a), ncol=ncol(permuted)) if (progress) pb <- txtProgressBar(min=0, max=ncol(A), style=3) for(i in seq_len(ncol(A))) { a.rand <- sort(permuted[,i], decreasing=TRUE, na.last=TRUE) n.bigger <- biggerN(a, a.rand) A[ord,i] <- n.bigger/seq_along(a) if (progress) setTxtProgressBar(pb, i) } if (progress) close(pb) FDR <- apply(A, 1, median) FDR[FDR>1] <- 1 FDR[ord] <- rev(sapply(length(FDR):1, function(x) return(min(FDR[ord][x:length(FDR)])))) return(FDR) } ROTS/R/calculateOverlaps1.R0000644000175100017510000000042414614306504016442 0ustar00biocbuildbiocbuild`calculateOverlaps1` <- function(D, S, pD, pS, nrow, N, N_len, ssq, B, overlaps, overlaps.P) { # Calculates overlaps in C: overlap <- NeedForSpeed1(D, S, pD, pS, nrow, N, N_len, ssq, B, overlaps, overlaps.P) overlap } ROTS/R/calculateOverlaps2.R0000644000175100017510000000037314614306504016446 0ustar00biocbuildbiocbuild`calculateOverlaps2` <- function(D, pD, nrow, N, N_len, B, overlaps, overlaps.P) { # Calculates overlaps in C: overlap <- NeedForSpeed2(D, pD, nrow, N, N_len, B, overlaps, overlaps.P) overlap } ROTS/R/calculateP.R0000644000175100017510000000110014614306504014755 0ustar00biocbuildbiocbuild`calculateP` <- function(observed, permuted) { # Store order for later use observed_order <- order(abs(observed), decreasing=TRUE) # Sort observed and permuted values observed <- sort(abs(observed), decreasing=TRUE) permuted <- sort(abs(as.vector(permuted)), decreasing=TRUE) # Get p-values from C++ code # (expects ordered vectors) p <- pvalue(observed, permuted) # Revert to original ordering results <- vector(mode="numeric", length=length(p)) for(i in seq_along(results)) { results[observed_order[i]] <- p[i] } return(results) } ROTS/R/permutatedSamples.R0000644000175100017510000000027314614306504016411 0ustar00biocbuildbiocbuild`permutatedSamples` <- function(data, B, cl) { samples <- matrix(nrow=B, ncol=ncol(data)) for(i in seq_len(B)){ samples[i,] <- sample(seq_len(ncol(data))) } return(samples) } ROTS/R/plot.ROTS.R0000644000175100017510000000552114614306504014457 0ustar00biocbuildbiocbuildplot.ROTS <- function(x, fdr=0.05, type=NULL, labels=FALSE, ...) { # Check for plot type if (!is.null(type)) { if(!(type %in% c("volcano","heatmap","ma","reproducibility","pvalue","pca"))) { stop("Plot type not available. The options are: 'volcano', 'heatmap', 'ma', 'reproducibility', 'pvalue', 'pca'") } } else { stop("Plot type not selected. The options are: 'volcano', 'heatmap', 'ma', 'reproducibility', 'pvalue', 'pca'") } # Differentially expressed features de <- which(x$FDR0) { points(x$logfc[de], -log10(x$pvalue)[de], pch=21, col="red") if (labels==TRUE) { text(x$logfc[de], -log10(x$pvalue)[de], labels=row.names(x$data)[de], pos=3, cex=0.5) } } } # Heatmap if(type=="heatmap" & length(de)>0) { try({ heatmap(as.matrix(x$data[de,]), scale="row", col=colorRampPalette(c("blue","white","red"))(512), ...) },silent=TRUE) } # MA plot if(type=="ma") { if(length(unique(x$cl))!=2) { stop(paste("MA plot not supported for for", length(unique(x$cl)), "classes.")) } M <- x$logfc A <- 0.5*(rowMeans(x$data[,x$cl==1])+rowMeans(x$data[,x$cl==2])) par(xpd=TRUE) plot(A, M, xlab="A", ylab="M", pch=20, cex=0.5, panel.first=abline(h=0, col="grey", lty=2), bty="l") lines(loess.smooth(A, M), col="blue", lty=2) if(length(de)>0) { points(A[de], M[de], pch=21, col="red") if (labels==TRUE) { text(A[de], M[de], labels=row.names(x$data)[de], pos=3, cex=0.5) } } } # Reproducibility if(type=="reproducibility") { z <- c(0,x$ztable[row.names(x$ztable)==x$a1,]) k <- c(0,as.numeric(colnames(x$ztable))) plot(k, z, pch=20, xlab="Top list size", ylab="Reproducibility Z-score", cex=0.5, panel.first=lines(k, z, col="grey"), bty="l") points(k[which(z==max(z))], z[which(z==max(z))], pch=21, col="red") text(k[which(z==max(z))], z[which(z==max(z))], labels=round(max(z),digits=3), pos=4) legend("topright",c(paste("a1 =",x$a1),paste("a2 =",x$a2)), bty="n") } # Histogram of p-values if(type=="pvalue") { hist(x$pvalue, breaks=20, col="grey", main="", xlab="p-value", ylab="Frequency") } # PCA if(type=="pca") { if(length(de)>0) { pca <- prcomp(t(x$data[de,]), center=TRUE, scale.=TRUE) par(xpd=TRUE) plot(pca$x[,1], pca$x[,2], xlab="Principal component 1", ylab="Principal component 2", pch=20, cex=2, col=x$cl, bty="l") if (labels==TRUE) { text(pca$x[,1], pca$x[,2], labels=colnames(x$data), pos=3, cex=1) } } } } ROTS/R/print.ROTS.R0000644000175100017510000000010414614306504014625 0ustar00biocbuildbiocbuild`print.ROTS` <- function( x, ... ){ print( unlist( x[7:11] ) ) } ROTS/R/RcppExports.R0000644000175100017510000000106714614306504015205 0ustar00biocbuildbiocbuild# This file was generated by Rcpp::compileAttributes # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 NeedForSpeed1 <- function(D, S, pD, pS, nrow, N, N_len, ssq, B, overlaps, overlaps_P) { .Call('ROTS_NeedForSpeed1', PACKAGE = 'ROTS', D, S, pD, pS, nrow, N, N_len, ssq, B, overlaps, overlaps_P) } NeedForSpeed2 <- function(D, pD, nrow, N, N_len, B, overlaps, overlaps_P) { .Call('ROTS_NeedForSpeed2', PACKAGE = 'ROTS', D, pD, nrow, N, N_len, B, overlaps, overlaps_P) } pvalue <- function(a, b) { .Call('ROTS_pvalue', PACKAGE = 'ROTS', a, b) } ROTS/R/ROTS.R0000644000175100017510000002777314614306504013517 0ustar00biocbuildbiocbuild`ROTS` <- function(data, groups, B=1000, K=NULL, paired=FALSE, seed=NULL, a1=NULL, a2=NULL, log=TRUE, progress=FALSE, verbose=TRUE, time=NULL, event=NULL) { if (is(data, "ExpressionSet")) data <- Biobase::exprs(data) ## Set random number generator seed for reproducibility if(!is.null(seed)) set.seed(seed, kind="default") if (!is.null(time)) { groups <- time if(length(time)!=length(event)) { stop(paste("Number of survival times and events do not match.")) } } ## Check groups if(length(groups)!=ncol(data)) { stop(paste("Number of samples in the data does not match the groups.")) } ## If rownames(data) == NULL, use integers as rownames. Summary function requires that ## the data matrix has rownames. if(is.null(rownames(data))) rownames(data) <- 1:nrow(data) ## The reproducibility values in a dense lattice ssq <- c( (0:20) / 100, (11:50) / 50, (6:25) / 5) N <- c( (1:20) * 5, (11:50) * 10, (21:40) * 25, (11:1000) * 100) ## Check for top list size if (is.null(K)) { K <- floor(nrow(data)/4) if (verbose) message(paste("No top list size K given, using",K)) } ## The top list size cannot be larger than the total number of genes K <- min(K,nrow(data)) N <- N[N < K] # handle the situation if the groups is character if(inherits(groups, "character")){ groups <- factor(groups) groups.levels <- levels(groups) groups <- as.numeric(groups) }else{ groups.levels <- NULL } ## Reorder the data according to the group labels and check for NA rows. data <- data[,order(groups)] if (!is.null(time)) { event <- event[order(groups)] } groups <- sort(groups) if (is.null(time)) { for (i in unique(groups)) { if(any(rowSums(is.na(data[,which(groups==i)])) >= length(which(groups==i))-1)) { if(is.null(groups.levels)){ target <- i } else { target <- groups.levels[i] } stop(paste("The data matrix of group",target,"contains rows with less than two non-missing values, please remove these rows.")) } } } cl <- groups+(1-min(groups)) ## Check number of samples for paired test if (paired) { for (i in unique(cl)[-1]) { if(length(which(cl==1))!=length(which(cl==i))) stop("Uneven number of samples for paired test.") } } ## Calculate fold change if (length(unique(cl))==2 && is.null(time)) { if(log) { logfc <- rowMeans(data[,which(cl==1)], na.rm=TRUE) - rowMeans(data[,which(cl==2)], na.rm=TRUE) } else { logfc <- rowMeans(log2(data[,which(cl==1)]+1), na.rm=TRUE) - rowMeans(log2(data[,which(cl==2)]+1), na.rm=TRUE) } } else { logfc <- rep(NA,nrow(data)) } ## --------------------------------------------------------------------------- ## Bootstrap samples if (verbose) message("Bootstrapping samples") samples <- bootstrapSamples(data, 2*B, cl, paired) ## Permutated samples pSamples <- permutatedSamples(data, nrow(samples), cl) ## Test statistics in the bootstrap (permutate) datasets. ## For each bootstrap (permutated) dataset, determine the signal log-ratio ## (D) and the standard error (S). D <- matrix(nrow = nrow(as.matrix(data)), ncol = nrow(samples)) S <- matrix(nrow = nrow(as.matrix(data)), ncol = nrow(samples)) pD <- matrix(nrow = nrow(as.matrix(data)), ncol = nrow(samples)) pS <- matrix(nrow = nrow(as.matrix(data)), ncol = nrow(samples)) if (progress) pb <- txtProgressBar(min=0, max=nrow(samples), style=3) for (i in seq_len(nrow(samples))) { samples.R <- split(samples[i,], cl) pSamples.R <- split(pSamples[i,], cl) ## If a1 and a2 parameters are given, we don't need the bootstrap ## dataset if( is.null(a1) | is.null(a2) ){ if (!is.null(time)) { fit <- testStatistic.surv(lapply(samples.R, function(x) data[,x]), cl, event) } else { fit <- testStatistic(paired, lapply(samples.R, function(x) data[,x])) } D[,i] <- fit$d S[,i] <- fit$s } if (!is.null(time)) { pFit <- testStatistic.surv(lapply(pSamples.R, function(x) data[,x]), cl, event) } else { pFit <- testStatistic(paired, lapply(pSamples.R, function(x) data[,x])) } pD[,i] <- pFit$d pS[,i] <- pFit$s if (progress) setTxtProgressBar(pb, i) } if (progress) close(pb) ## Free up memory rm(samples, pSamples) gc() ## --------------------------------------------------------------------------- if( is.null(a1) | is.null(a2) ){ ## Optimize the parameters if (verbose) message("Optimizing parameters") ## Calculate the reproducibility values for all the given a1-values and top ## list sizes in both bootstrap and permuted data and their standard ## deviations in the bootstrap case ## Reproducibility matrix (bootstrap data): ## the rows correspond to the a1-values given in ssq (+ 1 for signal ## log-ratio only: a1=1, a2=0), the columns correspond to the different top ## list sizes reprotable <- matrix(nrow=length(ssq) + 1, ncol=length(N)) colnames(reprotable) <- N row.names(reprotable) <- c(ssq, "slr") ## Reproducibility matrix (permuted data): ## the rows correspond to the a1-values given in ssq (+ 1 for signal ## log-ratio only: a1=1, a2=0), the columns correspond to the different top ## list sizes reprotable.P <- matrix(nrow=length(ssq) + 1, ncol=length(N)) colnames(reprotable.P) <- N row.names(reprotable.P) <- c(ssq, "slr") ## Standard deviation matrix for the reproducibility values (bootstrap ## data): the rows correspond to the a1-values given in ssq (+ 1 for signal ## log-ratio only: a1=1, a2=0), the columns correspond to the different ## top list sizes reprotable.sd <- matrix(nrow=length(ssq) + 1, ncol=length(N)) colnames(reprotable.sd) <- N row.names(reprotable.sd) <- c(ssq, "slr") if (progress) pb <- txtProgressBar(min=0, max=length(ssq), style=3) for(i in 1 : length(ssq)){ ## The overlaps between bootstrap samples. Rows correspond to different ## bootrsrap samples and columns corrospond to different top list size. ## Repeat for each parameter combination a1 and a2. overlaps <- matrix(0, nrow=B, ncol=length(N)) overlaps.P <- matrix(0, nrow=B, ncol=length(N)) ## Call the custom c++-loop 1. cResults = calculateOverlaps1 (D, S, pD, pS, nrow(D), as.integer(N), length(N), ssq[i], as.integer(B), overlaps, overlaps.P) ## Colmeans & rowMeans are a lot faster than apply # reprotable[i, ] <- colMeans(overlaps) # reprotable.P[i, ] <- colMeans(overlaps.P) reprotable[i, ] <- colMeans(cResults[["overlaps"]]) reprotable.P[i, ] <- colMeans(cResults[["overlaps_P"]]) ## Standard deviation for each column ## same as reprotable.sd[i, ] <- apply(overlaps, 2, sd) ## or just sd(overlaps), but a lot faster. # reprotable.sd[i,] <- sqrt(rowSums((t(overlaps) - reprotable[i,])^2) / # (nrow(overlaps) - 1)) reprotable.sd[i,] <- sqrt(rowSums((t(cResults[["overlaps"]]) - reprotable[i,])^2) / (nrow(cResults[["overlaps"]]) - 1)) if (progress) setTxtProgressBar(pb, i) } if (progress) close(pb) i <- length(ssq) + 1 overlaps <- matrix(0, nrow=B, ncol=length(N)) overlaps.P <- matrix(0, nrow=B, ncol=length(N)) ## Call the custom c++-loop 2. cResults = calculateOverlaps2 (D, pD, nrow(D), as.integer(N), length(N), as.integer(B), overlaps, overlaps.P) ## Free up memory rm(D, S) gc() reprotable[i, ] <- colMeans(cResults[["overlaps"]]) reprotable.P[i, ] <- colMeans(cResults[["overlaps_P"]]) ## Standard deviation for each column reprotable.sd[i,] <- sqrt(rowSums((t(cResults[["overlaps"]]) - reprotable[i,])^2) / (nrow(cResults[["overlaps"]]) - 1)) ## Free up memory rm(overlaps, overlaps.P , cResults) gc() ## ------------------------------------------------------------------------- ## Calculate the Z-statistic and find the top list size and the ## (a1,a2)-combination giving the maximal Z-value ztable <- (reprotable - reprotable.P) / reprotable.sd ## Rownames of ztable are c(ssq, "slr") and colnames are N ## Free up memory rm(reprotable.P, reprotable.sd) gc() sel <- which(ztable == max(ztable[is.finite(ztable)]), arr.ind=TRUE) ## Sel is a matrix containing the location(s) of the largest value (row, ## col). If the location of the largest value is not unique then nrow(sel) ## > 2 (length(sel) > 2) if(length(sel)>2) sel <- sel[1,] if(sel[1] < nrow(reprotable)) { a1 <- as.numeric(row.names(reprotable)[sel[1]]) a2 <- 1 } if(sel[1] == nrow(reprotable)) { a1 <- 1 a2 <- 0 } k <- as.numeric(colnames(reprotable)[sel[2]]) R <- reprotable[sel[1],sel[2]] Z <- ztable[sel[1],sel[2]] ## Free up memory rm(reprotable) gc() ## Calculate the reproducibility-optimized test statistic based on the ## reproducibility-maximizing a1, a2 and k values and the corresponding FDR if (!is.null(time)) { fit <- testStatistic.surv(lapply(split(1:length(cl), cl), function(x) data[,x]), cl, event) } else { fit <- testStatistic(paired, lapply(split(1:length(cl), cl), function(x) data[,x])) } d <- fit$d / (a1 + a2 * fit$s) pD <- pD/(a1 + a2 * pS) ## Free up memory rm(pS) gc() if (verbose) message("Calculating p-values") p <- calculateP(d, pD) if (verbose) message("Calculating FDR") FDR <- calculateFDR(d, pD, progress) ## Free up memory rm(pD) gc() ROTS.output <- list( data = data, # Original data B = B, # Number of resamplings d = d, # ROTS-statistic logfc = logfc, # Fold change pvalue = p, # p-value FDR = FDR, # FDR a1 = a1, # a1 a2 = a2, # a2 k = k, # top list size R = R, # reproducibility value Z = Z, # z-score ztable = ztable, # z-score table cl = cl) # classes } else{ # !is.null(a1 & !is.null(a2) ## Calculate statistic based on the given parameter values ## and the corresponding FDR if (!is.null(time)) { fit <- testStatistic.surv(lapply(split(1:length(cl), cl), function(x) data[,x]), cl, event) } else { fit <- testStatistic(paired, lapply(split(1:length(cl), cl), function(x) data[,x])) } d <- fit$d / (a1 + a2 * fit$s) if (verbose) message("Calculating p-values") p <- calculateP(d, pD/(a1 + a2 * pS)) if (verbose) message("Calculating FDR") FDR <- calculateFDR(d, pD/(a1 + a2 * pS), progress) ROTS.output <- list( data = data, # Original data B = B, # Number of resamplings d = d, # ROTS-statistic logfc = logfc, # Fold change pvalue = p, # p-value FDR = FDR, # FDR a1 = a1, # a1 a2 = a2, # a2 k = NULL, # top list size R = NULL, # reproducibility value Z = NULL, # z-score cl = cl) # classes } ## Define the class class(ROTS.output) <- "ROTS" return(ROTS.output) } ROTS/R/summary.ROTS.R0000644000175100017510000000356014614306504015177 0ustar00biocbuildbiocbuild`summary.ROTS` <- function(object, fdr=NULL, num.genes=NULL, verbose=TRUE, ...){ if( !is.null(fdr) || !is.null(num.genes)){ ## Sort ROTS-statistic values (abs) sorted.rots <- abs(object$d) names(sorted.rots) <- rownames(object$data) sorted.rots <- sort(sorted.rots, decreasing=TRUE) ## Add feature-names to fdr and ROTS values names(object$FDR) <- rownames(object$data) names(object$d) <- rownames(object$data) names(object$pvalue) <- rownames(object$data) ## Result matrix, columns are: "Row", "ROTS-statistic", "pvalue" and "FDR" result <- numeric(0) result <- cbind(match(names(sorted.rots), rownames(object$data)), object$d[names(sorted.rots)], object$pvalue[names(sorted.rots)], object$FDR[names(sorted.rots)]) colnames( result ) <- c("Row", "ROTS-statistic", "pvalue", "FDR") ## Show only num.gene top rows or rows whose false discovery rate <= fdr if(!is.null(fdr)) result <- result[ result[,4] <= fdr, ,drop=FALSE] else result <- result[ 1 : min(num.genes, nrow(result)), ,drop=FALSE] if( verbose ){ cat("ROTS results:", "\n\n") cat("Number of resamplings: ", object$B, "\n\n") cat("a1: ", object$a1,"\n") cat("a2: ", object$a2,"\n") cat("Top list size: ", object$k, "\n") cat("Reproducibility value: ", object$R, "\n") cat("Z-score: ", object$Z, "\n\n") cat(nrow(result), "rows satisfy the condition.") ## Print oly 10 first rows if(nrow(result) > 10){ cat(" Only ten first rows are" ,"\n", "displayed, see the return value for the whole output.\n") print(result[1:10, ]) cat("...", "\n") } else{ cat("\n") print(result) } } return(invisible(result)) } } ROTS/R/testStatistic.R0000644000175100017510000000663014614306504015564 0ustar00biocbuildbiocbuild`testStatistic` <- function(paired, samples){ # Two groups if (length(samples)==2) { X <- samples[[1]] Y <- samples[[2]] ## Calculates the test statistic for each row. ## X and Y are the data matrices of the two groups. ## Each row of these two matrices must contain at least TWO not NA values. ## Thus the "variance" always exists. ## Row means mX <- rowMeans(X, na.rm=TRUE) mY <- rowMeans(Y, na.rm=TRUE) ## Pooled standard deviations for each row sX <- rowSums((X - mX)^2, na.rm=TRUE) sY <- rowSums((Y - mY)^2, na.rm=TRUE) if(!paired) { ## Number of not NA values in each row nX <- rowSums(!is.na(X)) nY <- rowSums(!is.na(Y)) ## d == difference between the group means for each row (==gene) ## s == pooled standard deviation for each row (==gene) d <- mY - mX s <- sqrt(((sX + sY) / (nX + nY - 2)) * (1 / nX + 1 / nY)) ## Cases with less than two non-missing values. ## Set d = 0, s = 1 ind <- which( nY < 2 | nX < 2 ) d[ind] <- 0 s[ind] <- 1 } if(paired) { ## Add for paired sXY <- rowSums((X - mX)*(Y - mY), na.rm=TRUE) ## Number of not NA values in each row n <- rowSums(!is.na(X*Y)) ## d == difference between the group means for each row (==gene) ## s == pooled standard deviation for each row (==gene) d <- mY - mX s <- sqrt(((sX + sY) / (n + n - 2)) * (2 / n) - 2/(n*n-n)*sXY) ## Cases with less than two non-missing values. ## Set d = 0, s = 1 ind <- which( n < 2 ) d[ind] <- 0 s[ind] <- 1 } return(list(d=d, s=s)) } # Multiple groups if (length(samples)>2) { samples.all <- do.call("cbind",samples) if(!paired) { f <- sum(sapply(samples, ncol)) / prod(sapply(samples, ncol)) r <- vector(mode="numeric", length=nrow(samples.all)) for(k in 1:length(samples)) { r <- r + (rowMeans(samples[[k]], na.rm=TRUE)-rowMeans(samples.all, na.rm=TRUE))^2 } d <- (f*r)^0.5 f <- 1/sum(sapply(samples, ncol)-1) * sum(1/sapply(samples, ncol)) s <- vector(mode="numeric", length=nrow(samples.all)) for(k in 1:length(samples)) { s <- s + colSums(apply(samples[[k]], 1, function(x) (x-mean(x,na.rm=TRUE))^2), na.rm=TRUE) } s <- (f*s)^0.5 } if(paired) { stop("Multiple paired groups not supported!") } return(list(d=d, s=s)) } } `testStatistic.surv` <- function(samples, time, event){ samples.all <- do.call("cbind",samples) t <- unique(time[event==1]) r <- vector(mode="numeric", length=nrow(samples.all)) for(k in t) { i <- which(time>=k) z <- which(time==k) d <- z[which(event[which(time==k)]==1)] if (length(i)>1) { r <- r + (rowSums(as.data.frame(samples.all[,d]), na.rm=TRUE)-length(d)*rowMeans(samples.all[,i], na.rm=TRUE)) } } s <- vector(mode="numeric", length=nrow(samples.all)) for(k in t) { i <- which(time>=k) z <- which(time==k) d <- z[which(event[which(time==k)]==1)] if (length(i)>1) { s <- s + ((length(d)/length(i)) * rowSums((samples.all[,i]-rowMeans(samples.all[,i], na.rm=TRUE))^2, na.rm=TRUE)) } } s <- s^0.5 return(list(d=r, s=s)) } ROTS/src/0000755000175100017510000000000014614353231013152 5ustar00biocbuildbiocbuildROTS/src/NeedForSpeed1.cpp0000644000175100017510000000663614614306504016256 0ustar00biocbuildbiocbuild#include #include #include #include #include using namespace Rcpp; using namespace std; // Sort array b based on the array a (decreasingly) void sort2_1( double* a, double* b, int n ) { // Create vector of pairs. static vector< pair > pairs; pairs.reserve( n ); for( int i = 0; i < n; ++i) pairs.push_back( pair(a[i], b[i]) ); // Sort the pairs (inc). By default pairs are sorted by the first value and // in the case of a tie, the second values are used. sort( pairs.begin(), pairs.end()); // Split the pairs back into the original vectors (dec). for( int i = 0; i < n; ++i ) { a[ n-1-i ] = pairs[i].first; b[ n-1-i ] = pairs[i].second; } // Empty the vector pairs.clear(); } // Array printing routine for debugging. template void printArray( T *a, int a_len ){ for( int i = 0; i < a_len; ++i ) cout << a[i] << " "; cout << endl; } // Calculate the overlap void calculateOverlap_1( double *r1, double *r2, int r_len, IntegerVector N_ovlp, int N_len_ovlp, int b, int B_ovlp, NumericVector &result ){ // Copy r2 and sort the copy. double *r3 = new double[ r_len ]; for( int i = 0; i < r_len; ++i ) r3[i] = r2[i]; sort(r3, r3 + r_len ); reverse(r3, r3 + r_len ); // Sort r2 by r1 sort2_1( r1, r2, r_len ); // Calculate the overlap for( int i = 0; i < N_len_ovlp; ++i ){ static double sum = 0; for( int j = 0; j <= ( N_ovlp[i] - 1 ); ++j ) sum += ( r2[j] >= r3[ N_ovlp[i] - 1 ] ); result[ (b-1) + i*B_ovlp ] = sum / N_ovlp[i]; sum = 0; } delete[] r3; } // Loop1 // [[Rcpp::export]] List NeedForSpeed1( SEXP D, SEXP S, SEXP pD, SEXP pS, SEXP nrow, SEXP N, SEXP N_len, SEXP ssq, SEXP B, SEXP overlaps, SEXP overlaps_P){ NumericVector D_ovlp(D); NumericVector S_ovlp(S); NumericVector pD_ovlp(pD); NumericVector pS_ovlp(pS); //IntegerVector nrow_ovlp(nrow); int nrow_ovlp = Rcpp::as(nrow); IntegerVector N_ovlp(N); int N_len_ovlp = Rcpp::as(N_len); double ssq_ovlp = Rcpp::as(ssq); int B_ovlp = Rcpp::as(B); NumericVector overlaps_ovlp(overlaps); NumericVector overlaps_P_ovlp(overlaps_P); double *res1 = new double[ nrow_ovlp ]; double *res2 = new double[ nrow_ovlp ]; double *pres1 = new double[ nrow_ovlp ]; double *pres2 = new double[ nrow_ovlp ]; for( int b = 1; b <= B_ovlp; ++b ){ for( int i = 0; i < nrow_ovlp; ++i ){ res1[i] = fabs( D_ovlp[ ( b - 1 ) * (nrow_ovlp) + i ] / ( S_ovlp[(b - 1) * (nrow_ovlp) + i] + (ssq_ovlp) ) ); res2[i] = fabs( D_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] / ( S_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] + (ssq_ovlp) ) ); pres1[i] = fabs( pD_ovlp[ ( b - 1 ) * (nrow_ovlp) + i ] / ( pS_ovlp[(b - 1) * (nrow_ovlp) + i] + (ssq_ovlp) ) ); pres2[i] = fabs( pD_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] / ( pS_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] + (ssq_ovlp) ) ); } calculateOverlap_1( res1, res2, nrow_ovlp, N_ovlp, N_len_ovlp, b, B_ovlp, overlaps_ovlp ); calculateOverlap_1( pres1, pres2, nrow_ovlp, N_ovlp, N_len_ovlp, b, B_ovlp, overlaps_P_ovlp ); } delete[] res1; delete[] res2; delete[] pres1; delete[] pres2; return Rcpp::List::create( Rcpp::Named("overlaps") = overlaps_ovlp , Rcpp::Named("overlaps_P") = overlaps_P_ovlp ) ; } ROTS/src/NeedForSpeed2.cpp0000644000175100017510000000572414614306504016254 0ustar00biocbuildbiocbuild#include #include #include #include #include using namespace Rcpp; using namespace std; // Sort array b based on the array a (decreasingly) void sort2_2( double* a, double* b, int n ) { // Create vector of pairs. static vector< pair > pairs; pairs.reserve( n ); for( int i = 0; i < n; ++i) pairs.push_back( pair(a[i], b[i]) ); // Sort the pairs (inc). By default pairs are sorted by the first value and // in the case of a tie, the second values are used. sort( pairs.begin(), pairs.end()); // Split the pairs back into the original vectors (dec). for( int i = 0; i < n; ++i ) { a[ n-1-i ] = pairs[i].first; b[ n-1-i ] = pairs[i].second; } // Empty the vector pairs.clear(); } // Array printing routine for debugging. template void printArray( T *a, int a_len ){ for( int i = 0; i < a_len; ++i ) cout << a[i] << " "; cout << endl; } // Calculate the overlap void calculateOverlap_2( double *r1, double *r2, int r_len, IntegerVector N_ovlp, int N_len_ovlp, int b, int B_ovlp, NumericVector &result ){ // Copy r2 and sort the copy. double *r3 = new double[ r_len ]; for( int i = 0; i < r_len; ++i ) r3[i] = r2[i]; sort(r3, r3 + r_len ); reverse(r3, r3 + r_len ); // Sort r2 by r1 sort2_2( r1, r2, r_len ); // Calculate the overlap for( int i = 0; i < N_len_ovlp; ++i ){ static double sum = 0; for( int j = 0; j <= ( N_ovlp[i] - 1 ); ++j ) sum += ( r2[j] >= r3[ N_ovlp[i] - 1 ] ); result[ (b-1) + i*B_ovlp ] = sum / N_ovlp[i]; sum = 0; } delete[] r3; } //Loop2 // [[Rcpp::export]] List NeedForSpeed2( SEXP D, SEXP pD, SEXP nrow, SEXP N, SEXP N_len, SEXP B, SEXP overlaps, SEXP overlaps_P){ NumericVector D_ovlp(D); NumericVector pD_ovlp(pD); int nrow_ovlp = Rcpp::as(nrow); IntegerVector N_ovlp(N); int N_len_ovlp = Rcpp::as(N_len); int B_ovlp = Rcpp::as(B); NumericVector overlaps_ovlp(overlaps); NumericVector overlaps_P_ovlp(overlaps_P); double *res1 = new double[ nrow_ovlp ]; double *res2 = new double[ nrow_ovlp ]; double *pres1 = new double[ nrow_ovlp ]; double *pres2 = new double[ nrow_ovlp ]; for( int b = 1; b <= B_ovlp; ++b ){ for( int i = 0; i < nrow_ovlp; ++i ){ res1[i] = fabs( D_ovlp[ ( b - 1 ) * (nrow_ovlp) + i ] ); res2[i] = fabs( D_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] ); pres1[i] = fabs( pD_ovlp[ ( b - 1 ) * (nrow_ovlp) + i ] ); pres2[i] = fabs( pD_ovlp[ ( b + (B_ovlp) - 1 ) * (nrow_ovlp) + i ] ); } calculateOverlap_2( res1, res2, nrow_ovlp, N_ovlp, N_len_ovlp, b, B_ovlp, overlaps_ovlp ); calculateOverlap_2( pres1, pres2, nrow_ovlp, N_ovlp, N_len_ovlp, b, B_ovlp, overlaps_P_ovlp ); } delete[] res1; delete[] res2; delete[] pres1; delete[] pres2; return Rcpp::List::create( Rcpp::Named("overlaps") = overlaps_ovlp , Rcpp::Named("overlaps_P") = overlaps_P_ovlp ) ; } ROTS/src/pvalue.cpp0000644000175100017510000000067214614306504015160 0ustar00biocbuildbiocbuild#include using namespace Rcpp; using namespace std; // [[Rcpp::export]] NumericVector pvalue(SEXP a, SEXP b) { NumericVector observed(a); NumericVector permuted(b); NumericVector pvalues(observed.length()); int j = 0; for (int i=0; i=observed[i] && j using namespace Rcpp; // NeedForSpeed1 List NeedForSpeed1(SEXP D, SEXP S, SEXP pD, SEXP pS, SEXP nrow, SEXP N, SEXP N_len, SEXP ssq, SEXP B, SEXP overlaps, SEXP overlaps_P); RcppExport SEXP ROTS_NeedForSpeed1(SEXP DSEXP, SEXP SSEXP, SEXP pDSEXP, SEXP pSSEXP, SEXP nrowSEXP, SEXP NSEXP, SEXP N_lenSEXP, SEXP ssqSEXP, SEXP BSEXP, SEXP overlapsSEXP, SEXP overlaps_PSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< SEXP >::type D(DSEXP); Rcpp::traits::input_parameter< SEXP >::type S(SSEXP); Rcpp::traits::input_parameter< SEXP >::type pD(pDSEXP); Rcpp::traits::input_parameter< SEXP >::type pS(pSSEXP); Rcpp::traits::input_parameter< SEXP >::type nrow(nrowSEXP); Rcpp::traits::input_parameter< SEXP >::type N(NSEXP); Rcpp::traits::input_parameter< SEXP >::type N_len(N_lenSEXP); Rcpp::traits::input_parameter< SEXP >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< SEXP >::type B(BSEXP); Rcpp::traits::input_parameter< SEXP >::type overlaps(overlapsSEXP); Rcpp::traits::input_parameter< SEXP >::type overlaps_P(overlaps_PSEXP); __result = Rcpp::wrap(NeedForSpeed1(D, S, pD, pS, nrow, N, N_len, ssq, B, overlaps, overlaps_P)); return __result; END_RCPP } // NeedForSpeed2 List NeedForSpeed2(SEXP D, SEXP pD, SEXP nrow, SEXP N, SEXP N_len, SEXP B, SEXP overlaps, SEXP overlaps_P); RcppExport SEXP ROTS_NeedForSpeed2(SEXP DSEXP, SEXP pDSEXP, SEXP nrowSEXP, SEXP NSEXP, SEXP N_lenSEXP, SEXP BSEXP, SEXP overlapsSEXP, SEXP overlaps_PSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< SEXP >::type D(DSEXP); Rcpp::traits::input_parameter< SEXP >::type pD(pDSEXP); Rcpp::traits::input_parameter< SEXP >::type nrow(nrowSEXP); Rcpp::traits::input_parameter< SEXP >::type N(NSEXP); Rcpp::traits::input_parameter< SEXP >::type N_len(N_lenSEXP); Rcpp::traits::input_parameter< SEXP >::type B(BSEXP); Rcpp::traits::input_parameter< SEXP >::type overlaps(overlapsSEXP); Rcpp::traits::input_parameter< SEXP >::type overlaps_P(overlaps_PSEXP); __result = Rcpp::wrap(NeedForSpeed2(D, pD, nrow, N, N_len, B, overlaps, overlaps_P)); return __result; END_RCPP } // pvalue NumericVector pvalue(SEXP a, SEXP b); RcppExport SEXP ROTS_pvalue(SEXP aSEXP, SEXP bSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< SEXP >::type a(aSEXP); Rcpp::traits::input_parameter< SEXP >::type b(bSEXP); __result = Rcpp::wrap(pvalue(a, b)); return __result; END_RCPP } ROTS/tests/0000755000175100017510000000000014614306504013526 5ustar00biocbuildbiocbuildROTS/tests/test-all.R0000644000175100017510000000004414614306504015374 0ustar00biocbuildbiocbuildlibrary(testthat) test_check("ROTS")ROTS/tests/testthat/0000755000175100017510000000000014614306504015366 5ustar00biocbuildbiocbuildROTS/tests/testthat/test_biggerN.R0000644000175100017510000000161714614306504020132 0ustar00biocbuildbiocbuildcontext("Bigger N") test_that("Single values", { expect_that(biggerN(0,1:100), equals(100)) expect_that(biggerN(1,1:100), equals(100)) expect_that(biggerN(99,1:100), equals(2)) expect_that(biggerN(100,1:100), equals(1)) expect_that(biggerN(101,1:100), equals(0)) expect_that(biggerN(0,100:1), equals(100)) expect_that(biggerN(1,100:1), equals(100)) expect_that(biggerN(99,100:1), equals(2)) expect_that(biggerN(100,100:1), equals(1)) expect_that(biggerN(101,100:1), equals(0)) }) test_that("Multiple values", { expect_that(biggerN(c(1,2,3),1:100), equals(c(98,99,100))) expect_that(biggerN(c(3,2,1),1:100), equals(c(98,99,100))) expect_that(biggerN(c(1,1,1),1:100), equals(c(100,100,100))) }) test_that("Special values", { expect_that(biggerN(NA,1:100), equals(100)) expect_that(biggerN(Inf,1:100), equals(0)) expect_that(biggerN(c(NA,NA,200),1:100), equals(c(0,100,100))) }) ROTS/tests/testthat/test_bootstrap.R0000644000175100017510000000105514614306504020566 0ustar00biocbuildbiocbuildcontext("Bootstrapping") test_that("Basic tests", { expect_that(nrow(bootstrapSamples(data=NULL, B=1000, labels=c(1,1,1,2,2,2), paired=FALSE)), equals(1000)) expect_that(ncol(bootstrapSamples(data=NULL, B=10, labels=c(1,1,1,1,2,2,2,2), paired=FALSE)), equals(8)) expect_that(all(apply(bootstrapSamples(data=NULL, B=1000, labels=c(1,1,1,2,2,2), paired=TRUE)[,1:3],1, function(x) all(x<4))), equals(TRUE)) expect_that(all(apply(bootstrapSamples(data=NULL, B=1000, labels=c(1,1,1,2,2,2), paired=TRUE)[,4:6],1, function(x) all(x>3))), equals(TRUE)) }) ROTS/tests/testthat/test_calculateFDR.R0000644000175100017510000000041414614306504021040 0ustar00biocbuildbiocbuildcontext("FDR") test_that("Basic tests", { expect_that(calculateFDR(0.9, matrix(rep(1,100),10), FALSE), equals(1)) expect_that(calculateFDR(1.0, matrix(rep(1,100),10), FALSE), equals(1)) expect_that(calculateFDR(1.1, matrix(rep(1,100),10), FALSE), equals(0)) }) ROTS/tests/testthat/test_calculateP.R0000644000175100017510000000072514614306504020631 0ustar00biocbuildbiocbuildcontext("p-values") test_that("Single values", { expect_that(calculateP(0, matrix(1:100,10)), equals(1)) expect_that(calculateP(1, matrix(1:100,10)), equals(1)) expect_that(calculateP(51, matrix(1:100,10)), equals(0.5)) expect_that(calculateP(100, matrix(1:100,10)), equals(0.01)) expect_that(calculateP(101, matrix(1:100,10)), equals(0)) }) test_that("Multiple values", { expect_that(calculateP(c(0,101,0,51), matrix(1:100,10)), equals(c(1,0,1,0.5))) }) ROTS/tests/testthat/test_permutation.R0000644000175100017510000000034214614306504021116 0ustar00biocbuildbiocbuildcontext("Permutation") test_that("Basic tests", { expect_that(rowSums(permutatedSamples(data=matrix(1:12,2), B=1000)), equals(rep(21,1000))) expect_that(ncol(permutatedSamples(data=matrix(1:12,2), B=1000)), equals(6)) }) ROTS/tests/testthat/test_ROTS.R0000644000175100017510000000316714614306504017346 0ustar00biocbuildbiocbuildcontext("ROTS function") data(upsSpikeIn) test_that("Basic tests", { rots.out <- ROTS(data=upsSpikeIn, groups=c(0,0,0,1,1,1), B=100, K=500, seed=1) expect_that(rots.out, is_a("ROTS")) expect_that(length(grep("ups",names(rots.out$d))), equals(36)) expect_that(all((rots.out$FDR >= 0) & (rots.out$FDR <= 1)), equals(TRUE)) expect_that(all((rots.out$p >= 0) & (rots.out$p <= 1)), equals(TRUE)) }) test_that("Special cases", { case1 <- c(10,10,10,10,10,10) case2 <- c(0,0,0,0,0,0) case3 <- c(NA,10,10,NA,10,10) case4 <- c(NA,0,0,NA,0,0) case5 <- c(1,1,1,10,10,10) case6 <- c(1,NA,1,10,NA,10) rots.out <- ROTS(data=rbind(case1,case2,case3,case4,case5,case6,upsSpikeIn), groups=c(0,0,0,1,1,1), B=100 ,K=500 ,seed=1) expect_that(all(rots.out$FDR[1:4] > 0.99), equals(TRUE)) expect_that(all(rots.out$p[1:4] > 0.99), equals(TRUE)) expect_that(all(rots.out$logfc[1:4] == 0), equals(TRUE)) expect_that(all(rots.out$FDR[5:6] < 0.01), equals(TRUE)) expect_that(all(rots.out$p[5:6] < 0.01), equals(TRUE)) expect_that(all(rots.out$logfc[5:6] != 0), equals(TRUE)) }) test_that("Comparison to t-test", { rots.out <- ROTS(data=upsSpikeIn, groups=c(0,0,0,1,1,1), a1=0, a2=1, paired=FALSE, B=100, K=500, seed=1)$d ttest.out <- apply(as.matrix(upsSpikeIn), 1, function(x) t.test(x[4:6],x[1:3],paired=FALSE)$statistic) expect_that(cor(rots.out,ttest.out), equals(1)) rots.out <- ROTS(data=upsSpikeIn, groups=c(0,0,0,1,1,1), a1=0, a2=1, paired=TRUE, B=100, K=500, seed=1)$d ttest.out <- apply(as.matrix(upsSpikeIn), 1, function(x) t.test(x[4:6],x[1:3],paired=TRUE)$statistic) expect_that(cor(rots.out,ttest.out), equals(1)) }) ROTS/tests/testthat/test_testStatistic.R0000644000175100017510000000140014614306504021412 0ustar00biocbuildbiocbuildcontext("Test statistic") data(upsSpikeIn) upsSpikeIn[1,1:3] <- c(0,0,0) upsSpikeIn[1,4:6] <- c(0,0,0) upsSpikeIn[2,1:3] <- c(0,0,0) upsSpikeIn[2,4:6] <- c(4,4,4) test_that("Unpaired", { stats <- testStatistic(list(upsSpikeIn[,1:3],upsSpikeIn[,4:6]), paired=FALSE) expect_that(as.numeric(stats$d[1]), equals(0)) expect_that(as.numeric(stats$d[2]), equals(4)) expect_that(as.numeric(stats$s[1]), equals(0)) expect_that(as.numeric(stats$s[2]), equals(0)) }) test_that("Paired", { stats <- testStatistic(list(upsSpikeIn[,1:3],upsSpikeIn[,4:6]), paired=TRUE) expect_that(as.numeric(stats$d[1]), equals(0)) expect_that(as.numeric(stats$d[2]), equals(4)) expect_that(as.numeric(stats$s[1]), equals(0)) expect_that(as.numeric(stats$s[2]), equals(0)) }) ROTS/vignettes/0000755000175100017510000000000014614353231014373 5ustar00biocbuildbiocbuildROTS/vignettes/ROTS.Rnw0000644000175100017510000002455614614306504015667 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{ROTS: Reproducibility Optimized Test Statistic} %\VignetteDepends{ROTS} %\VignetteKeywords{Preprocessing, statistics} \documentclass{article} \usepackage{cite, hyperref} \usepackage{graphicx} \hypersetup{ colorlinks = true, %Colours links instead of ugly boxes urlcolor = blue, %[rgb]{0,0.125,0.376}, %Colour for external hyperlinks linkcolor = blue, %[rgb]{0,0.125,0.376}, %Colour of internal links citecolor = red %Colour of citations } \title{ \begin{center} ROTS: Reproducibility Optimized Test Statistic \end{center} } \author{Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo} \date{March 3, 2016} \setlength\parindent{0pt} \begin{document} \SweaveOpts{concordance=TRUE} \setkeys{Gin}{width=0.6\textwidth} \maketitle \textnormal{\normalfont} \tableofcontents \newpage \section{Introduction} Differential expression testing is perhaps the most common approach among current omics analysies. Reproducibility optimized test statistic (ROTS) aims to rank genomic features of interest (such as genes, proteins and transcripts) in order of evidence for differential expression in two-group comparisons. Initially, ROTS was developed to test differential expression in microarray studies (Elo 2008). However, the general design of the algorithm supports the utility of the method in proteomics and count-based technologies like RNA-seq and single cell datasets (Seyednasrollah et al. 2015, Pursiheimo et al. 2015). ROTS is a data adaptive method which can optimize its parameters based on intrinsic features of input data. Also, the method aims to solve the common problem of small sample size through a resampling procedure.\\[1em] The ROTS statistic is optimized among a family of \textit{t-type} statistics $d = r/(\alpha_1 + \alpha_2 \times s)$, where $r$ is the absolute difference between the group averages $\left|\bar{x_1} - \bar{x_2}\right|$, $s$ is the pooled standard error, and $\alpha_1$ and $\alpha_2$ are the non-negative parameters to be optimized. Two special cases of this family are the ordinary \textit{t-statistic} $(\alpha_1 = 0, \alpha_2 = 1)$ and the signal log-ratio $(\alpha_1 = 1, \alpha_2 = 0)$.\\[1em] The optimality is defined in terms of maximal overlap of top-ranked features in group-preserving bootstrap datasets. Importantly, besides the group labels, no a priori information about the properties of the data is required and no fixed cutoff for the gene rankings needs to be specified. The user is given the option to adjust the largest top list size ($K$) considered in the reproducibility calculations, since lowering this size can markedly reduce the computation time. In large data matrices with thousands of rows, we generally recommend using a size of several thousands.\\[1em] ROTS tolerates a moderate number of missing values in the data matrix by effectively ignoring their contribution during the operation of the procedure. However, each row of the data matrix must contain at least two values in both groups. The rows containing only a few non-missing values should be removed; or alternatively, the missing data entries can be imputed using, e.g., the K-nearest neighbour imputation, which is implemented in the Bioconductor package impute. If the parameter values $\alpha_1$ and $\alpha_2$ are set by the user, then no optimization is performed but the statistic and FDR-values are calculated for the given parameters. The false discovery rate (FDR) for the optimized test statistic is calculated by permuting the sample labels. The results for all the genes can be obtained by setting the FDR cutoff to 1.\\[1em] \section{Algorithm overview} ROTS optimizes the reproducibility among a family of modified statistics: \begin{equation} d_\alpha = \frac{r}{ \alpha_1 + \alpha_2 \times s} \end{equation} where $r$ is a score, $\alpha_1$ and $\alpha_2$ are non-negative parameters to be optimized, and $s$ is standard deviation.\\[1em] The optimal statistic is determined by maximizing the reproducibility Z-score: \begin{equation} Z_k\left(d_\alpha\right) = \frac{R_k\left(d_\alpha\right) - R_k^0\left(d_\alpha\right)}{s_k\left(d_\alpha\right)} \end{equation} over a dence lattice $\alpha_1 \in [0, 0.01, . . . , 5]$ , $\alpha_2 \in \left\{0, 1\right\}$, $k \in \left\{1, 2, . . . , G\right\}$. Here, $R_k\left(d_\alpha\right)$ is the observed reproducibility at top list size $k$, $R_k^0\left(d_\alpha\right)$ is the corresponding reproducibility in randomized datasets (permuted over samples), $s_k\left(d_\alpha\right)$ is the standard deviation of the bootstrap distribution, and $G$ is the total number of genes/proteins in the data. Reproducibility is defined as the average overlap of $k$ top-ranked features over pairs of bootstrapped datasets. \\[1em] In two-group comparisons, ROTS optimizes the reproducibility of top-ranked features in group-preserving bootstrap datasets among a family of modified t-statistics, where the score $r$ is the absolute difference between the group averages and $s$ is the pooled standard error: \begin{equation} r = \left|\bar{x_1} - \bar{x_2}\right| \end{equation} \begin{equation} s = \left[ \frac{ \sum_{i \in C_1}^{} (x_{i}-\bar{x}_{1})^2 + \sum_{i \in C_2}^{} (x_{i}-\bar{x}_{2})^2}{n_1 + n_2 - 2} (1/n_1 + 1/n_2)\right]^{1/2} \end{equation} where $i$ has the indices of observations in classes $C_1$ and $C_2$, and $n_1$ and $n_2$ are the number samples in classes 1 and 2, respectively.\\[1em] In multi-group comparisons, ROTS optimizes the reproducibility of top-ranked features in group-preserving bootstrap datasets among a family of modified f-statistics: \begin{equation} r = \left[ \left\{ \sum n_c / \prod n_c \right\} \sum_{c=1}^{C}n_c \left( \bar{x}_{c} - \bar{x} \right)^2 \right] ^{1/2} \end{equation} \begin{equation} s = \left[ \frac{1}{\sum \left ( n_c - 1 \right )} \left ( \sum \frac{1}{n_c} \right ) \sum_{c=1}^{C} \sum_{i \in C_c}^{ } \left( x_{i} - \bar{x}_{c} \right)^2 \right] ^ {1/2} \end{equation} where $c$ is the different classes $\{ 1,2,...,C \}$, $n_c$ is the number samples in class $c$, and $i$ has the indices of observations in class $C_c$.\\[1em] In survival analysis, ROTS optimizes the reproducibility of top-ranked features among Cox scores: \begin{equation} r = \sum_{t=1_1}^{T}\left[ x_{D_t}-d_t\bar{x}_t \right] \end{equation} \begin{equation} s = \left[ \sum_{t=t_1}^{T} (d_t/k_t) \sum_{i \in R_t}^{} (x_{i}-\bar{x}_{t})^2 \right]^{1/2} \end{equation} where $D_t$ is indices of observations at the different death times $\{ t_1,t_2,...,T \}$, $R_t$ indices of the observations at risk at these times, and $d_t$ and $k_t$ the number of deaths and individuals at risk, at the time, respectively.\\[1em] For more detailed information about the ROTS algorithm, see Elo et al. (2008) and Seyednasrollah et al. (2015). \section{Input data} ROTS expects the input data to be in form of a matrix with genomic features as rows and samples as columns. It is recommended to use normalized data as the input for ROTS. The matrix can be either of integer numbers, e.g. for RNA-seq and single cells, or float numbers, e.g. microarray intensities. \section{Preprocessing} For count-based data, we recommend the widely used preprocessing techniques like TMM (Trimmed Mean of M-values) normalization available in edgeR Bioconductor package or TMM normalization plus Voom transformation available in Limma Bioconductor package. For microarray and proteomics data, standard normalization techniques are recommended. \section{Differential expression testing} We use here a proteomics dataset as an example for differential expression testing. The overall approach is the same for other omics data along with recommended preprocessing strategies. \\[1em] The analysis starts by loading the ROTS package and the example dataset, which contains two sample groups each having three replicates: <>= library(ROTS) data(upsSpikeIn) input = upsSpikeIn @ In the next step we determine the experimental design for differential expression analysis. Please note that the order of the samples in the data matrix should be exactly the same as the groups vector defined. <<>>= groups = c(rep(0,3), rep(1,3)) groups @ The ROTS function performs the final differential expression testing. The user can set the function parameters before running the analysis: <>= results = ROTS(data = input, groups = groups , B = 100 , K = 500 , seed = 1234) names(results) @ In this example, we set the number of bootstrapping (B) and the number of top-ranked features for reproducibility optimization (K) to 100 and 500 respectively, to reduce running time of the example. In real analysis it is preferred to use a higher number of bootstraps (e.g. 1000). The optimization parameters a1 and a2 should always be non-negative. The output of ROTS function includes test statistic (d), estimated \textit{p}-value (pvalue), False Discovery Rate (FDR), optimized test statistic parameters and top list size (a1, a2, k), optimized reproducibility value (R) and Z-score (Z). In general, the Z-score and reproducibility are the main indicators to decide the success of differential expression analysis. As a rule of thumb, reproducibility Z-scores below 2 indicate that the data or the statistics are not sufficient for reliable detection.\\[1em] Finally, it is possible to summarize the results based on criteria selected by the user. For instance, the following code lists the top ranked differentially expressed features with FDR below 0.05: <>= summary(results, fdr = 0.05) @ \newpage \section{Visualization} Results can also be visualized using the standard plot command: <>= plot(results, fdr = 0.05, type = "volcano") @ <>= plot(results, fdr = 0.05, type = "heatmap") @ \section{References} Elo, L.L. et al., \emph{Reproducibility-optimized test statistic for ranking genes in microarray studies}. IEEE/ACM transactions on computational biology and bioinformatics, 5, 423-431, 2008. \\[1em] Elo, L.L. et al. \emph{Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets}. Briefings in bioinformatics, 10, 547-555, 2009. \\[1em] Seyednasrollah et al. \emph{ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer}. Nucleic Acids Research, 2015. \\[1em] Pursiheimo et al. \emph{Optimization of Statistical Methods Impact on Quantitative Proteomics Data}. J. Proteome Res., 2015. \end{document}