susieR/0000755000176200001440000000000014143515472011532 5ustar liggesuserssusieR/NAMESPACE0000644000176200001440000000432314143476342012755 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(coef,susie) S3method(predict,susie) S3method(print,summary.susie) S3method(summary,susie) export(coef.susie) export(compute_ss) export(compute_suff_stat) export(estimate_s_rss) export(get_cs_correlation) export(kriging_rss) export(predict.susie) export(print.summary.susie) export(summary.susie) export(susie) export(susie_auto) export(susie_get_cs) export(susie_get_lfsr) export(susie_get_niter) export(susie_get_objective) export(susie_get_pip) export(susie_get_posterior_mean) export(susie_get_posterior_samples) export(susie_get_posterior_sd) export(susie_get_prior_variance) export(susie_get_residual_variance) export(susie_init_coef) export(susie_plot) export(susie_plot_changepoint) export(susie_plot_iteration) export(susie_rss) export(susie_suff_stat) export(susie_trendfilter) export(univariate_regression) importFrom(Matrix,colMeans) importFrom(Matrix,colSums) importFrom(Matrix,crossprod) importFrom(Matrix,diag) importFrom(Matrix,rowMeans) importFrom(Matrix,rowSums) importFrom(Matrix,sparseMatrix) importFrom(Matrix,t) importFrom(Matrix,tcrossprod) importFrom(crayon,magenta) importFrom(crayon,red) importFrom(ggplot2,aes) importFrom(ggplot2,aes_string) importFrom(ggplot2,annotate) importFrom(ggplot2,geom_abline) importFrom(ggplot2,geom_col) importFrom(ggplot2,geom_line) importFrom(ggplot2,geom_point) importFrom(ggplot2,ggplot) importFrom(ggplot2,ggtitle) importFrom(ggplot2,labs) importFrom(ggplot2,theme_bw) importFrom(ggplot2,theme_classic) importFrom(grDevices,dev.off) importFrom(grDevices,pdf) importFrom(graphics,legend) importFrom(graphics,par) importFrom(graphics,plot) importFrom(graphics,points) importFrom(graphics,segments) importFrom(matrixStats,colSds) importFrom(methods,as) importFrom(mixsqp,mixsqp) importFrom(reshape,melt) importFrom(stats,.lm.fit) importFrom(stats,coef) importFrom(stats,cor) importFrom(stats,cov2cor) importFrom(stats,dnorm) importFrom(stats,lm) importFrom(stats,median) importFrom(stats,optim) importFrom(stats,optimize) importFrom(stats,pnorm) importFrom(stats,rmultinom) importFrom(stats,rnorm) importFrom(stats,sd) importFrom(stats,summary.lm) importFrom(stats,uniroot) importFrom(stats,var) importFrom(utils,head) importFrom(utils,modifyList) susieR/LICENSE0000644000176200001440000000015213703336624012536 0ustar liggesusersYEAR: 2017-2020 COPYRIGHT HOLDER: Gao Wang, Peter Carbonetto, Yuxin Zou, Kaiqian Zhang, Matthew Stephens. susieR/README.md0000644000176200001440000001047214142574204013012 0ustar liggesusers# susieR [![CRAN status badge](https://www.r-pkg.org/badges/version/susieR)](https://cran.r-project.org/package=susieR) [![Travis Build Status](https://travis-ci.org/stephenslab/susieR.svg?branch=master)](https://travis-ci.org/stephenslab/susieR) [![Appveyor Build status](https://ci.appveyor.com/api/projects/status/tcgeqxd8q8krija6?svg=true)](https://ci.appveyor.com/project/pcarbo/susier) [![CircleCI](https://circleci.com/gh/stephenslab/susieR/tree/master.svg?style=svg)](https://app.circleci.com/pipelines/github/stephenslab/susieR?branch=master) [![codecov](https://codecov.io/gh/stephenslab/susieR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/stephenslab/susieR) The `susieR` package implements a simple new way to perform variable selection in multiple regression ($y=Xb+e$). The methods implemented here are particularly well-suited to settings where some of the X variables are highly correlated, and the true effects are highly sparse (e.g. <20 non-zero effects in the vector $b$). One example of this is genetic fine-mapping applications, and this application was a major motivation for developing these methods. However, the methods should also be useful more generally. The methods are based on a new model for sparse multiple regression, which we call the "Sum of Single Effects" (SuSiE) model. This model, which will be described in a manuscript in preparation (Wang et al), lends itself to a particularly simple and intuitive fitting procedure -- effectively a Bayesian modification of simple forward selection, which we call "Iterative Bayesian Step-wise Selection". The output of the fitting procedure is a number of "Credible Sets" (CSs), which are each designed to have high probability to contain a variable with non-zero effect, while at the same time being as small as possible. You can think of the CSs as being a set of "highly correlated" variables that are each associated with the response: you can be confident that one of the variables has a non-zero coefficient, but they are too correlated to be sure which one. The package is developed by Gao Wang, Peter Carbonetto, Yuxin Zou, Kaiqian Zhang, and Matthew Stephens from the [Stephens Lab](https://stephenslab.uchicago.edu) at the University of Chicago. Please [post issues](https://github.com/stephenslab/susieR/issues) to ask questions, get our support or provide us feedback; please [send pull requests](https://github.com/stephenslab/susieR/pulls) if you have helped fixing bugs or making improvements to the source code. ## Quick Start Install susieR from [CRAN](https://cran.r-project.org/package=susieR): ```R install.packages("susieR") ``` Alternatively, install the latest development version of `susieR` from GitHub: ```R # install.packages("remotes") remotes::install_github("stephenslab/susieR") ``` See [here](https://stephenslab.github.io/susieR/articles/mwe.html) for a brief illustration of `susieR`. For more documentation and examples please visit https://stephenslab.github.io/susieR ## Citing this work If you find the `susieR` package or any of the source code in this repository useful for your work, please cite: > Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. (2020). A > simple new approach to variable selection in regression, with > application to genetic fine mapping. *Journal of the Royal > Statistical Society, Series B* **82**, 1273--1300. > https://doi.org/10.1111/rssb.12388 If you use any of the summary data methods---`susie_suff_stat` or `susie_rss`---please also cite: > Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. (2021). > Fine-mapping from summary data with the "Sum of Single Effects" > model. *bioRxiv* https://doi.org/10.1101/2021.11.03.467167 ## Developer notes + When any changes are made to `roxygen2` markup, run `devtools::document()` to update package `NAMESPACE` and documentation files. + Run `pkgdown::build_site()` to build the website. Getting `pkgdown` to work properly can be frustrating due to numerous & fragile dependencies. If `pkgdown` does not work for you out of the box you can use this `docker` command to run all vignettes and build the site: ```bash docker run --rm --security-opt label:disable -t -P -w $PWD -v $PWD:$PWD \ -u $UID:${GROUPS[0]} -e HOME=/home/$USER -e USER=$USER gaow/susie \ R --slave -e "pkgdown::build_site(lazy=TRUE, examples=FALSE)" ``` [susie-preprint]: https://doi.org/10.1101/501114 susieR/data/0000755000176200001440000000000014113174714012440 5ustar liggesuserssusieR/data/N3finemapping.RData0000644000176200001440000042716014113173235016062 0ustar liggesusers7zXZi"6!X'o])TW"nRʟKMd[_;zkս/Q_$67LI2şyRUQUU 3"B{G+g@ҕX%8 Q9N@5VCNZ1C;v 'UW/t7N.P̎T DL'?^UEHO޿KpA'+8.|7w.(8t96d8DDST8C;! )3 8„W/F,_wM)GY.I6ؽ/-D׍{ٝ\L 7c/1͛J”ѦT@G'ai_.q#Ƈ|1P&aVP6D ',sЀ,U6QWʙS\zyOhOl_k m`l&9GliS ^ \+!>U[پ;[x{#5 C*j~^Oh 4Qn Da]O^S\Ħ@G +9̼hK}ك$]Du)cx' Ly} VMfþA`'֬ #ʾz)ĂA'9R7_|bR\O !<(sڠa9i;?Z$c]T&d|o@QMr~i/j RfkA.J}sTz{E ?}c_=,LD00NZԛa?)p]?Ikm}_kxHj`ЗIu5^6oOkqp [4M{nL)L.ǧcvpqlF/Bg arն@>#! 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(Ǫ9,êIaW!N.s slAGc#RU/<*ِq՗Esb½/v ܅%3X! s?Ϋ7ײ?ݹH?";kPi}-b;)1 gڹjfA_5"v~!8)ksq*YQ>=6U)0K,@O}rI,&K̙1sW*C2 lJ6g!*/ZݛRJ:d3d^9G~g_F_nqc>eyC=T) Š^MFi{Y.Y!;1JtX5AJcf=|} i[/8oI\4 ~}w|P:`ŐW-sa &ꆳc1t5i^Iح] 33\1EW̡xgɨtP3l֟3Jqpi;WJ<(yYA[8WFB;Oz~ٕcW-ztYH΋q!!9x#xգ+t'2- BܵĠU8e,ʮNv]gt豋IX (.uQǼPP*dq)HO'KZ(YW|lQmȜhp0AX[&NBl:LA?E1tKQKU[uxȘ ]; q.~j$0 YZsusieR/man/0000755000176200001440000000000014143375605012307 5ustar liggesuserssusieR/man/predict.susie.Rd0000644000176200001440000000212314051346274015353 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.susie.R \name{predict.susie} \alias{predict.susie} \title{Predict outcomes or extract coefficients from susie fit.} \usage{ \method{predict}{susie}(object, newx = NULL, type = c("response", "coefficients"), ...) } \arguments{ \item{object}{A susie fit.} \item{newx}{A new value for X at which to do predictions.} \item{type}{The type of output. For \code{type = "response"}, predicted or fitted outcomes are returned; for \code{type = "coefficients"}, the estimated coefficients are returned.} \item{\dots}{Other arguments used by generic predict function. These extra arguments are not used here.} } \value{ For \code{type = "response"}, predicted or fitted outcomes are returned; for \code{type = "coefficients"}, the estimated coefficients are returned. If the susie fit has intercept = \code{NA} (which is common when using \code{susie_suff_stat}) then predictions are computed using an intercept of 0, and a warning is emitted. } \description{ Predict outcomes or extract coefficients from susie fit. } susieR/man/coef.susie.Rd0000644000176200001440000000104313755042336014637 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.susie.R \name{coef.susie} \alias{coef.susie} \title{Extract regression coefficients from susie fit} \usage{ \method{coef}{susie}(object, ...) } \arguments{ \item{object}{A susie fit.} \item{\dots}{Additional arguments passed to the generic \code{coef} method.} } \value{ A p+1 vector, the first element being an intercept, and the remaining p elements being estimated regression coefficients. } \description{ Extract regression coefficients from susie fit } susieR/man/N3finemapping.Rd0000644000176200001440000000324314142574422015273 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{N3finemapping} \alias{N3finemapping} \title{Simulated Fine-mapping Data with Three Effect Variables.} \format{ \code{N3finemapping} is a list with the following elements: \describe{ \item{X}{N by P variable matrix of centered and scaled genotype data.} \item{chrom}{Chromomsome of the original data, in hg38 coordinate.} \item{pos}{Chromomosomal positoin of the original data, in hg38 coordinate. The information can be used to compare impact of using other genotype references of the same variables in susie_rss application.} \item{true_coef}{The simulated effect sizes.} \item{residual_variance}{The simulated residual covariance matrix.} \item{Y}{The simulated response variables.} \item{allele_freq}{Allele frequency of the original genotype data.} \item{V}{Prior covariance matrix for effect size of the three non-zero effect variables.} } } \description{ The data-set contains a matrix of 574 individuals and 1,001 variables. These variables are real-world genotypes centered and scaled, and therefore retains the correlation structure of variables in the original genotype data. 3 out of the variables have non-zero effects. The response data is generated under a multivariate linear regression model. See Wang \emph{et al} (2020) for more details. } \examples{ data(N3finemapping) } \references{ G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple new approach to variable selection in regression, with application to genetic fine-mapping. \emph{Journal of the Royal Statistical Society, Series B} \doi{10.1101/501114}. } \keyword{data} susieR/man/susie_plots.Rd0000644000176200001440000000626614055430035015150 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_plots.R \name{susie_plot} \alias{susie_plot} \alias{susie_plot_iteration} \title{SuSiE Plots.} \usage{ susie_plot( model, y, add_bar = FALSE, pos = NULL, b = NULL, max_cs = 400, add_legend = NULL, ... ) susie_plot_iteration(model, L, file_prefix, pos = NULL) } \arguments{ \item{model}{A SuSiE fit, typically an output from \code{\link{susie}} or one of its variants. For \code{suse_plot}, the susie fit must have \code{model$z}, \code{model$PIP}, and may include \code{model$sets}. \code{model} may also be a vector of z-scores or PIPs.} \item{y}{A string indicating what to plot: either \code{"z"} for z-scores, \code{"PIP"} for posterior inclusion probabilities, \code{"log10PIP"} for posterior inclusion probabiliities on the (base-10) log-scale. For any other setting, the data are plotted as is.} \item{add_bar}{If \code{add_bar = TRUE}, add horizontal bar to signals in credible interval.} \item{pos}{Indices of variables to plot. If \code{pos = NULL} all variables are plotted.} \item{b}{For simulated data, set \code{b = TRUE} to highlight "true" effects (highlights in red).} \item{max_cs}{The largest credible set to display, either based on purity (set \code{max_cs} between 0 and 1), or based on size (set \code{max_cs > 1}).} \item{add_legend}{If \code{add_legend = TRUE}, add a legend to annotate the size and purity of each CS discovered. It can also be specified as location where legends should be added, e.g., \code{add_legend = "bottomright"} (default location is \code{"topright"}).} \item{\dots}{Additional arguments passed to \code{\link[graphics]{plot}}.} \item{L}{An integer specifying the number of credible sets to plot.} \item{file_prefix}{Prefix to path of output plot file. If not specified, the plot, or plots, will be saved to a temporary directory generated using \code{\link{tempdir}}.} } \value{ Invisibly returns \code{NULL}. } \description{ \code{susie_plot} produces a per-variable summary of the SuSiE credible sets. \code{susie_plot_iteration} produces a diagnostic plot for the susie model fitting. For \code{susie_plot_iteration}, several plots will be created if \code{track_fit = TRUE} when calling \code{susie}. } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[sample(1:1000,4)] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) res = susie(X,y,L = 10) susie_plot(res,"PIP") susie_plot(res,"PIP",add_bar = TRUE) susie_plot(res,"PIP",add_legend = TRUE) susie_plot(res,"PIP", pos=1:500, add_legend = TRUE) # Plot selected regions with adjusted x-axis position label res$genomic_position = 1000 + (1:length(res$pip)) susie_plot(res,"PIP",add_legend = TRUE, pos = list(attr = "genomic_position",start = 1000,end = 1500)) # True effects are shown in red. susie_plot(res,"PIP",b = beta,add_legend = TRUE) set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[sample(1:1000,4)] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) res = susie(X,y,L = 10) susie_plot_iteration(res, L=10) } \seealso{ \code{\link{susie_plot_changepoint}} } susieR/man/FinemappingConvergence.Rd0000644000176200001440000000234314113174734017211 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{FinemappingConvergence} \alias{FinemappingConvergence} \title{Simulated Fine-mapping Data with Convergence Problem.} \format{ \code{FinemappingConvergence} is a list with the following elements: \describe{ \item{XtX}{Summary statistics computed using the centered and scaled genotype matrix.} \item{Xty}{Summary statistics computed using the centered and scaled genotype data, and the centered simulated response.} \item{yty}{yty is computed using the centered simulated response.} \item{n}{The sample size (50,000).} \item{true_coef}{The coefficients used to simulate the responses.} \item{z}{z-scores from a simple (single-SNP) linear regression.}} } \description{ Data simulated using real genotypes from 50,000 individuals and 200 SNPs. Two of the SNPs have non-zero effects on the multivariate response. The response data are generated under a linear regression model. The simulated response and the columns of the genotype matrix are centered. } \examples{ data(FinemappingConvergence) } \seealso{ A similar data set with more SNPs is used in the \dQuote{Refine SuSiE model} vignette. } \keyword{data} susieR/man/univariate_regression.Rd0000644000176200001440000000322414051464002017172 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/univariate_regression.R \name{univariate_regression} \alias{univariate_regression} \title{Perform Univariate Linear Regression Separately for Columns of X} \usage{ univariate_regression( X, y, Z = NULL, center = TRUE, scale = FALSE, return_residuals = FALSE ) } \arguments{ \item{X}{n by p matrix of regressors.} \item{y}{n-vector of response variables.} \item{Z}{Optional n by k matrix of covariates to be included in all regresions. If Z is not \code{NULL}, the linear effects of covariates are removed from y first, and the resulting residuals are used in place of y.} \item{center}{If \code{center = TRUE}, center X, y and Z.} \item{scale}{If \code{scale = TRUE}, scale X, y and Z.} \item{return_residuals}{Whether or not to output the residuals if Z is not \code{NULL}.} } \value{ A list with two vectors containing the least-squares estimates of the coefficients (\code{betahat}) and their standard errors (\code{sebetahat}). Optionally, and only when a matrix of covariates \code{Z} is provided, a third vector \code{residuals} containing the residuals is returned. } \description{ This function performs the univariate linear regression y ~ x separately for each column x of X. Each regression is implemented using \code{.lm.fit()}. The estimated effect size and stardard error for each variable are outputted. } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[1:4] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) res = univariate_regression(X,y) plot(res$betahat/res$sebetahat) } susieR/man/SummaryConsistency.Rd0000644000176200001440000000213414113174734016452 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{SummaryConsistency} \alias{SummaryConsistency} \title{Simulated Fine-mapping Data with LD matrix From Reference Panel.} \format{ \code{SummaryConsistency} is a list with the following elements: \describe{ \item{z}{z-scores computed by fitting univariate simple regression variable-by-variable.} \item{ldref}{LD matrix estimated from the reference panel.} \item{flip_id}{The index of the SNP with the flipped allele.} \item{signal_id}{The index of the SNP with the non-zero effect.}} } \description{ Data simulated using real genotypes from 10,000 individuals and 200 SNPs. One SNP have non-zero effect on the multivariate response. The response data are generated under a linear regression model. There is also one SNP with flipped allele between summary statistics and the reference panel. } \examples{ data(SummaryConsistency) } \seealso{ A similar data set with more samples is used in the \dQuote{Diagnostic for fine-mapping with summary statistics} vignette. } \keyword{data} susieR/man/estimate_s_rss.Rd0000644000176200001440000000336614055245030015620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_utils.R \name{estimate_s_rss} \alias{estimate_s_rss} \title{Estimate s in \code{susie_rss} Model Using Regularized LD} \usage{ estimate_s_rss(z, R, r_tol = 1e-08, method = "null-mle") } \arguments{ \item{z}{A p-vector of z scores.} \item{R}{A p by p symmetric, positive semidefinite correlation matrix.} \item{r_tol}{Tolerance level for eigenvalue check of positive semidefinite matrix of R.} \item{method}{a string specifies the method to estimate \eqn{s}.} } \value{ A number between 0 and 1. } \description{ The estimated s gives information about the consistency between the z scores and LD matrix. A larger \eqn{s} means there is a strong inconsistency between z scores and LD matrix. The \dQuote{null-mle} method obtains mle of \eqn{s} under \eqn{z | R ~ N(0,(1-s)R + s I)}, \eqn{0 < s < 1}. The \dQuote{null-partialmle} method obtains mle of \eqn{s} under \eqn{U^T z | R ~ N(0,s I)}, in which \eqn{U} is a matrix containing the of eigenvectors that span the null space of R; that is, the eigenvectors corresponding to zero eigenvalues of R. The estimated \eqn{s} from \dQuote{null-partialmle} could be greater than 1. The \dQuote{null-pseudomle} method obtains mle of \eqn{s} under pseudolikelihood \eqn{L(s) = \prod_{j=1}^{p} p(z_j | z_{-j}, s, R)}, \eqn{0 < s < 1}. } \examples{ set.seed(1) n = 500 p = 1000 beta = rep(0,p) beta[1:4] = 0.01 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) input_ss = compute_suff_stat(X,y,standardize = TRUE) ss = univariate_regression(X,y) R = cor(X) attr(R,"eigen") = eigen(R, symmetric = TRUE) zhat = with(ss,betahat/sebetahat) s = estimate_s_rss(zhat, R) } susieR/man/single_effect_regression.Rd0000644000176200001440000000657314051464466017650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/single_effect_regression.R, % R/single_effect_regression_rss.R, R/single_effect_regression_ss.R \name{single_effect_regression} \alias{single_effect_regression} \alias{single_effect_regression_rss} \alias{single_effect_regression_ss} \title{Bayesian single-effect linear regression} \usage{ single_effect_regression( y, X, V, residual_variance = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0 ) single_effect_regression_rss( z, Sigma, V = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0 ) single_effect_regression_ss( Xty, dXtX, V = 1, residual_variance = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0 ) } \arguments{ \item{y}{An n-vector.} \item{X}{An n by p matrix of covariates.} \item{V}{A scalar giving the (initial) prior variance} \item{residual_variance}{The residual variance.} \item{prior_weights}{A p-vector of prior weights.} \item{optimize_V}{The optimization method to use for fitting the prior variance.} \item{check_null_threshold}{Scalar specifying threshold on the log-scale to compare likelihood between current estimate and zero the null.} \item{z}{A p-vector of z scores.} \item{Sigma}{\code{residual_var*R + lambda*I}} \item{Xty}{A p-vector.} \item{dXtX}{A p-vector containing the diagonal elements of \code{crossprod(X)}.} } \value{ A list with the following elements: \item{alpha}{Vector of posterior inclusion probabilities; \code{alpha[i]} is posterior probability that the ith coefficient is non-zero.} \item{mu}{Vector of posterior means (conditional on inclusion).} \item{mu2}{Vector of posterior second moments (conditional on inclusion).} \item{lbf}{Vector of log-Bayes factors for each variable.} \item{lbf_model}{Log-Bayes factor for the single effect regression.} \code{single_effect_regression} and \code{single_effect_regression_ss} additionally output: \item{V}{Prior variance (after optimization if \code{optimize_V != "none"}).} \item{loglik}{The log-likelihood, \eqn{\log p(y | X, V)}.} } \description{ These methods fit the regression model \eqn{y = Xb + e}, where elements of e are \emph{i.i.d.} \eqn{N(0,s^2)}, and b is a p-vector of effects to be estimated. The assumption is that b has exactly one non-zero element, with all elements equally likely to be non-zero. The prior on the coefficient of the non-zero element is \eqn{N(0,V)}. } \details{ \code{single_effect_regression_ss} performs single-effect linear regression with summary data, in which only the statistcs \eqn{X^Ty} and diagonal elements of \eqn{X^TX} are provided to the method. \code{single_effect_regression_rss} performs single-effect linear regression with z scores. That is, this function fits the regression model \eqn{z = R*b + e}, where e is \eqn{N(0,Sigma)}, \eqn{Sigma = residual_var*R + lambda*I}, and the b is a p-vector of effects to be estimated. The assumption is that b has exactly one non-zero element, with all elements equally likely to be non-zero. The prior on the non-zero element is \eqn{N(0,V)}. The required summary data are the p-vector \code{z} and the p by p matrix \code{Sigma}. The summary statistics should come from the same individuals. } \keyword{internal} susieR/man/kriging_rss.Rd0000644000176200001440000000420414102301653015102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_utils.R \name{kriging_rss} \alias{kriging_rss} \title{Compute Distribution of z-scores of Variant j Given Other z-scores, and Detect Possible Allele Switch Issue} \usage{ kriging_rss( z, R, r_tol = 1e-08, s = estimate_s_rss(z, R, r_tol, method = "null-mle") ) } \arguments{ \item{z}{A p-vector of z scores.} \item{R}{A p by p symmetric, positive semidefinite correlation matrix.} \item{r_tol}{Tolerance level for eigenvalue check of positive semidefinite matrix of R.} \item{s}{an estimated s from \code{estimate_s_rss}} } \value{ a list containing a ggplot2 plot object and a table. The plot compares observed z score vs the expected value. The possible allele switched variants are labeled as red points (log LR > 2 and abs(z) > 2). The table summarizes the conditional distribution for each variant and the likelihood ratio test. The table has the following columns: the observed z scores, the conditional expectation, the conditional variance, the standardized differences between the observed z score and expected value, the log likelihood ratio statistics. } \description{ Under the null, the rss model with regularized LD matrix is \eqn{z|R,s ~ N(0, (1-s)R + s I))}. We use a mixture of normals to model the conditional distribution of z_j given other z scores, \eqn{z_j | z_{-j}, R, s ~ \sum_{k=1}^{K} \pi_k N(-\Omega_{j,-j} z_{-j}/\Omega_{jj}, \sigma_{k}^2/\Omega_{jj})}, \eqn{\Omega = ((1-s)R + sI)^{-1}}, \eqn{\sigma_1, ..., \sigma_k} is a grid of fixed positive numbers. We estimate the mixture weights \eqn{\pi} We detect the possible allele switch issue using likelihood ratio for each variant. } \examples{ # See also the vignette, "Diagnostic for fine-mapping with summary # statistics." set.seed(1) n = 500 p = 1000 beta = rep(0,p) beta[1:4] = 0.01 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) ss = univariate_regression(X,y) R = cor(X) attr(R,"eigen") = eigen(R, symmetric = TRUE) zhat = with(ss,betahat/sebetahat) cond_dist = kriging_rss(zhat, R) cond_dist$plot } susieR/man/get_cs_correlation.Rd0000644000176200001440000000252014061022471016427 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_utils.R \name{get_cs_correlation} \alias{get_cs_correlation} \title{Get Correlations Between CSs, using Variable with Maximum PIP From Each CS} \usage{ get_cs_correlation(model, X = NULL, Xcorr = NULL, max = FALSE) } \arguments{ \item{model}{A SuSiE fit, typically an output from \code{\link{susie}} or one of its variants.} \item{X}{n by p matrix of values of the p variables (covariates) in n samples. When provided, correlation between variables will be computed and used to remove CSs whose minimum correlation among variables is smaller than \code{min_abs_corr}.} \item{Xcorr}{p by p matrix of correlations between variables (covariates). When provided, it will be used to remove CSs whose minimum correlation among variables is smaller than \code{min_abs_corr}.} \item{max}{When \code{max = FAFLSE}, return a matrix of CS correlations. When \code{max = TRUE}, return only the maximum absolute correlation among all pairs of correlations.} } \value{ A matrix of correlations between CSs, or the maximum absolute correlation when \code{max = TRUE}. } \description{ This function evaluates the correlation between single effect CSs. It is not part of the SuSiE inference. Rather, it is designed as a diagnostic tool to assess how correlated the reported CS are. } susieR/man/susieR-package.Rd0000644000176200001440000000274214143476342015446 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susieR-package.R \docType{package} \name{susieR-package} \alias{susieR} \alias{susieR-package} \title{susieR: Sum of Single Effects Linear Regression} \description{ Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) and Zou et al (2021) . These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables). } \seealso{ Useful links: \itemize{ \item \url{https://github.com/stephenslab/susieR} \item Report bugs at \url{https://github.com/stephenslab/susieR/issues} } } \author{ \strong{Maintainer}: Peter Carbonetto \email{peter.carbonetto@gmail.com} Authors: \itemize{ \item Gao Wang \email{wang.gao@columbia.edu} \item Yuxin Zou \item Kaiqian Zhang \item Matthew Stephens } } \keyword{internal} susieR/man/susie_get_methods.Rd0000644000176200001440000001330714143476342016314 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_utils.R \name{susie_get_objective} \alias{susie_get_objective} \alias{susie_get_posterior_mean} \alias{susie_get_posterior_sd} \alias{susie_get_niter} \alias{susie_get_prior_variance} \alias{susie_get_residual_variance} \alias{susie_get_lfsr} \alias{susie_get_posterior_samples} \alias{susie_get_cs} \alias{susie_get_pip} \title{Inferences From Fitted SuSiE Model} \usage{ susie_get_objective(res, last_only = TRUE, warning_tol = 1e-06) susie_get_posterior_mean(res, prior_tol = 1e-09) susie_get_posterior_sd(res, prior_tol = 1e-09) susie_get_niter(res) susie_get_prior_variance(res) susie_get_residual_variance(res) susie_get_lfsr(res) susie_get_posterior_samples(susie_fit, num_samples) susie_get_cs( res, X = NULL, Xcorr = NULL, coverage = 0.95, min_abs_corr = 0.5, dedup = TRUE, squared = FALSE, check_symmetric = TRUE, n_purity = 100, use_rfast ) susie_get_pip(res, prune_by_cs = FALSE, prior_tol = 1e-09) } \arguments{ \item{res}{A susie fit, typically an output from \code{\link{susie}} or one of its variants. For \code{susie_get_pip} and \code{susie_get_cs}, this may instead be the posterior inclusion probability matrix, \code{alpha}.} \item{last_only}{If \code{last_only = FALSE}, return the ELBO from all iterations; otherwise return the ELBO from the last iteration only.} \item{warning_tol}{Warn if ELBO is decreasing by this tolerance level.} \item{prior_tol}{Filter out effects having estimated prior variance smaller than this threshold.} \item{susie_fit}{A susie fit, an output from \code{\link{susie}}.} \item{num_samples}{The number of draws from the posterior distribution.} \item{X}{n by p matrix of values of the p variables (covariates) in n samples. When provided, correlation between variables will be computed and used to remove CSs whose minimum correlation among variables is smaller than \code{min_abs_corr}.} \item{Xcorr}{p by p matrix of correlations between variables (covariates). When provided, it will be used to remove CSs whose minimum correlation among variables is smaller than \code{min_abs_corr}.} \item{coverage}{A number between 0 and 1 specifying desired coverage of each CS.} \item{min_abs_corr}{A "purity" threshold for the CS. Any CS that contains a pair of variables with correlation less than this threshold will be filtered out and not reported.} \item{dedup}{If \code{dedup = TRUE}, remove duplicate CSs.} \item{squared}{If \code{squared = TRUE}, report min, mean and median of squared correlation instead of the absolute correlation.} \item{check_symmetric}{If \code{check_symmetric = TRUE}, perform a check for symmetry of matrix \code{Xcorr} when \code{Xcorr} is provided (not \code{NULL}).} \item{n_purity}{The maximum number of credible set (CS) variables used in calculating the correlation (\dQuote{purity}) statistics. When the number of variables included in the CS is greater than this number, the CS variables are randomly subsampled.} \item{use_rfast}{Use the Rfast package for the purity calculations. By default \code{use_rfast = TRUE} if the Rfast package is installed.} \item{prune_by_cs}{Whether or not to ignore single effects not in a reported CS when calculating PIP.} } \value{ \code{susie_get_objective} returns the evidence lower bound (ELBO) achieved by the fitted susie model and, optionally, at each iteration of the IBSS fitting procedure. \code{susie_get_residual_variance} returns the (estimated or fixed) residual variance parameter. \code{susie_get_prior_variance} returns the (estimated or fixed) prior variance parameters. \code{susie_get_posterior_mean} returns the posterior mean for the regression coefficients of the fitted susie model. \code{susie_get_posterior_sd} returns the posterior standard deviation for coefficients of the fitted susie model. \code{susie_get_niter} returns the number of model fitting iterations performed. \code{susie_get_pip} returns a vector containing the posterior inclusion probabilities (PIPs) for all variables. \code{susie_get_lfsr} returns a vector containing the average lfsr across variables for each single-effect, weighted by the posterior inclusion probability (alpha). \code{susie_get_posterior_samples} returns a list containing the effect sizes samples and causal status with two components: \code{b}, an \code{num_variables} x \code{num_samples} matrix of effect sizes; \code{gamma}, an \code{num_variables} x \code{num_samples} matrix of causal status random draws. \code{susie_get_cs} returns credible sets (CSs) from a susie fit, as well as summaries of correlation among the variables included in each CS. If desired, one can filter out CSs that do not meet a specified \dQuote{purity} threshold; to do this, either \code{X} or \code{Xcorr} must be supplied. It returns a list with the following elements: \item{cs}{A list in which each list element is a vector containing the indices of the variables in the CS.} \item{coverage}{The nominal coverage specified for each CS.} \item{purity}{If \code{X} or \code{Xcorr} iis provided), the purity of each CS.} \item{cs_index}{If \code{X} or \code{Xcorr} is provided) the index (number between 1 and L) of each reported CS in the supplied susie fit.} } \description{ These functions access basic properties or draw inferences from a fitted susie model. } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[1:4] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) s = susie(X,y,L = 10) susie_get_objective(s) susie_get_objective(s, last_only=FALSE) susie_get_residual_variance(s) susie_get_prior_variance(s) susie_get_posterior_mean(s) susie_get_posterior_sd(s) susie_get_niter(s) susie_get_pip(s) susie_get_lfsr(s) } susieR/man/susie_rss.Rd0000644000176200001440000001635714142574570014632 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_rss.R \name{susie_rss} \alias{susie_rss} \title{Sum of Single Effects (SuSiE) Regression using summary statistics} \usage{ susie_rss( z, R, z_ld_weight = 0, prior_variance = 50, estimate_prior_variance = TRUE, check_prior = TRUE, ... ) } \arguments{ \item{z}{A p-vector of z scores.} \item{R}{A p by p correlation matrix.} \item{z_ld_weight}{This parameter is included for backwards compatibility with previous versions of the function, but it is no longer recommended to use a non-zero value. If \code{z_ld_weight > 0}, the matrix R used in the model is adjusted to be \code{cov2cor((1-w)*R + w*tcrossprod(z))}, where \code{w = z_ld_weight}.} \item{prior_variance}{The prior variance(s) for the non-zero element of \eqn{b_l}. It is either a scalar, or a vector of length L. When \code{estimate_prior_variance = TRUE} (highly recommended) this simply provides an initial value for the prior variance, and the default value of 50 is simply intended to be a large initial value.} \item{estimate_prior_variance}{If \code{estimate_prior_variance = TRUE}, which is highly recommended, the prior variance is estimated (this is a separate parameter for each of the L effects). If provided, \code{prior_variance} is then used as an initial value for the optimization. When \code{estimate_prior_variance = FALSE} (not recommended) the prior variance for each of the L effects is determined by the value supplied to \code{prior_variance}.} \item{check_prior}{When \code{check_prior = TRUE}, it checks if the estimated prior variance becomes unreasonably large (comparing with 10 * max(abs(z))^2).} \item{\dots}{Other parameters to be passed to \code{\link{susie_suff_stat}}.} } \value{ A \code{"susie"} object with the following elements: \item{alpha}{An L by p matrix of posterior inclusion probabilites.} \item{mu}{An L by p matrix of posterior means, conditional on inclusion.} \item{mu2}{An L by p matrix of posterior second moments, conditional on inclusion.} \item{lbf}{log-Bayes Factor for each single effect.} \item{lbf_variable}{log-Bayes Factor for each variable and single effect.} \item{V}{Prior variance of the non-zero elements of b.} \item{elbo}{The value of the variational lower bound, or \dQuote{ELBO} (objective function to be maximized), achieved at each iteration of the IBSS fitting procedure.} \item{Rr}{A vector of length p, equal to \code{R \%*\% colSums(alpha*mu)}.} \item{sets}{Credible sets estimated from model fit; see \code{\link{susie_get_cs}} for details.} \item{pip}{A vector of length p giving the (marginal) posterior inclusion probabilities for all p covariates.} \item{niter}{Number of IBSS iterations that were performed.} \item{converged}{\code{TRUE} or \code{FALSE} indicating whether the IBSS converged to a solution within the chosen tolerance level.} } \description{ \code{susie_rss} performs variable selection under a sparse Bayesian multiple linear regression of \eqn{Y} on \eqn{X} using only the z-scores from standard univariate regression of \eqn{Y} on each column of \eqn{X}, and an estimate \eqn{R} of the correlation matrix between columns of \eqn{X}. It does this by combining the "RSS likelihood" from Zhu and Stephens (2017) with the Sum of Single Effects" model from Wang et al (2020). } \details{ In some applications, particularly genetic applications, it is desired to fit a regression model (\eqn{Y = X\tilde{b} + E} say, which we refer to as "the original regression model" or ORM) without access to the actual values of \eqn{Y} and \eqn{X}, but given only some summary statistics. \code{susie_rss} assumes the availability of \eqn{z} scores from standard univariate regression of \eqn{Y} on each column of \eqn{X}, and an estimate \eqn{R} of the correlation matrix between columns of \eqn{X} (\eqn{R} is sometimes called the LD matrix in genetic applications). See Zhu and Stephens (2017), and references therein, for further background. The \code{susie_rss} function is based on the model (2.10) from Zhu and Stephens, \eqn{z | R, b ~ N(Rb,R)} where \eqn{b} is a vector of length p representing the effects to be estimated. The effect \eqn{b_j} is simply a multiple of the coefficient \eqn{\tilde{b}_j} in the ORM, and so \eqn{b_j} is non-zero if and only if \eqn{\tilde{b}_j} is non-zero. In this sense the variable selection problem in this model is the same as the variable selection problem in the ORM, and so the credible sets and PIPs computed by \code{susie_rss} can be interpreted as credible sets and PIPs for the ORM. However, converting posterior estimates of \eqn{b_j} to estimates of \eqn{\tilde{b}_j} would require computation of the scaling factor (not done here). More precisely, \code{susie_rss} assumes the log-likelihood for \eqn{b} is \eqn{l(b; z,R) = -0.5(b'Rb - 2z'b)}, which is equivalent to model (2.10) from Zhu and Stephens if \eqn{R} is invertible, but does not require \eqn{R} to be invertible. It combines this likelihood with the \dQuote{susie prior} which assumes that \eqn{b = \sum_{l=1}^L b_l} where each \eqn{b_l} is a vector of length p with exactly one non-zero element; see \code{\link{susie}} and Wang et al (2020) for details. In practice, this is accomplished by calling \code{susie_suff_stat} with \code{XtX = R} and \code{Xty = z}, and fixing \code{residual_variance = 1}. (Values for \code{n} and \code{yty} are also required by \code{susie_suff_stat}. They do not affect inference when the residual variance is fixed, but they do affect the interpretation of \code{scaled_prior_variance}; we set \code{n=2, yty=1} so that \eqn{var(y) = yty/(n-1) = 1}.) Additional arguments to be passed to \code{\link{susie_suff_stat}} can be provided via \code{...}. } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[1:4] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) input_ss = compute_suff_stat(X,y,standardize = TRUE) ss = univariate_regression(X,y) R = with(input_ss,cov2cor(XtX)) zhat = with(ss,betahat/sebetahat) res = susie_rss(zhat,R) # Toy example illustrating behaviour susie_rss when the z-scores # are mostly consistent with a non-invertible correlation matrix. # Here the CS should contain both variables, and two PIPs should # be nearly the same. z = c(6,6.01) R = matrix(1,2,2) fit = susie_rss(z,R) print(fit$sets$cs) print(fit$pip) # In this second toy example, the only difference is that one # z-score is much larger than the other. Here we expect that the # second PIP will be much larger than the first. z = c(6,7) R = matrix(1,2,2) fit = susie_rss(z,R) print(fit$sets$cs) print(fit$pip) } \references{ G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple new approach to variable selection in regression, with application to genetic fine-mapping. \emph{Journal of the Royal Statistical Society, Series B} \bold{82}, 1273-1300 \doi{10.1101/501114}. Y. Zou, P. Carbonetto, G. Wang and M. Stephens (2021). Fine-mapping from summary data with the \dQuote{Sum of Single Effects} model. \emph{bioRxiv} \doi{10.1101/2021.11.03.467167}. X. Zhu and M. Stephens (2017). Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. \emph{Annals of Applied Statistics} \bold{11}, 1561-1592. } susieR/man/susie.Rd0000644000176200001440000004107314142574570013734 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie.R, R/susie_ss.R \name{susie} \alias{susie} \alias{susie_suff_stat} \title{Sum of Single Effects (SuSiE) Regression} \usage{ susie( X, y, L = min(10, ncol(X)), scaled_prior_variance = 0.2, residual_variance = NULL, prior_weights = NULL, null_weight = NULL, standardize = TRUE, intercept = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim", "EM", "simple"), check_null_threshold = 0, prior_tol = 1e-09, residual_variance_upperbound = Inf, s_init = NULL, coverage = 0.95, min_abs_corr = 0.5, compute_univariate_zscore = FALSE, na.rm = FALSE, max_iter = 100, tol = 0.001, verbose = FALSE, track_fit = FALSE, residual_variance_lowerbound = var(drop(y))/10000, refine = FALSE, n_purity = 100 ) susie_suff_stat( bhat, shat, R, n, var_y, XtX, Xty, yty, X_colmeans = NA, y_mean = NA, maf = NULL, maf_thresh = 0, L = 10, scaled_prior_variance = 0.2, residual_variance = NULL, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim", "EM", "simple"), check_null_threshold = 0, prior_tol = 1e-09, r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, standardize = TRUE, max_iter = 100, s_init = NULL, coverage = 0.95, min_abs_corr = 0.5, tol = 0.001, verbose = FALSE, track_fit = FALSE, check_input = FALSE, refine = FALSE, check_prior = FALSE, n_purity = 100 ) } \arguments{ \item{X}{An n by p matrix of covariates.} \item{y}{The observed responses, a vector of length n.} \item{L}{Maximum number of non-zero effects in the susie regression model. If L is larger than the number of covariates, p, L is set to p.} \item{scaled_prior_variance}{The prior variance, divided by \code{var(y)} (or by \code{(1/(n-1))yty} for \code{susie_suff_stat}); that is, the prior variance of each non-zero element of b is \code{var(y) * scaled_prior_variance}. The value provided should be either a scalar or a vector of length \code{L}. If \code{estimate_prior_variance = TRUE}, this provides initial estimates of the prior variances.} \item{residual_variance}{Variance of the residual. If \code{estimate_residual_variance = TRUE}, this value provides the initial estimate of the residual variance. By default, it is set to \code{var(y)} in \code{susie} and \code{(1/(n-1))yty} in \code{susie_suff_stat}.} \item{prior_weights}{A vector of length p, in which each entry gives the prior probability that corresponding column of X has a nonzero effect on the outcome, y.} \item{null_weight}{Prior probability of no effect (a number between 0 and 1, and cannot be exactly 1).} \item{standardize}{If \code{standardize = TRUE}, standardize the columns of X to unit variance prior to fitting (or equivalently standardize XtX and Xty to have the same effect). Note that \code{scaled_prior_variance} specifies the prior on the coefficients of X \emph{after} standardization (if it is performed). If you do not standardize, you may need to think more carefully about specifying \code{scaled_prior_variance}. Whatever your choice, the coefficients returned by \code{coef} are given for \code{X} on the original input scale. Any column of \code{X} that has zero variance is not standardized.} \item{intercept}{If \code{intercept = TRUE}, the intercept is fitted; it \code{intercept = FALSE}, the intercept is set to zero. Setting \code{intercept = FALSE} is generally not recommended.} \item{estimate_residual_variance}{If \code{estimate_residual_variance = TRUE}, the residual variance is estimated, using \code{residual_variance} as an initial value. If \code{estimate_residual_variance = FALSE}, the residual variance is fixed to the value supplied by \code{residual_variance}.} \item{estimate_prior_variance}{If \code{estimate_prior_variance = TRUE}, the prior variance is estimated (this is a separate parameter for each of the L effects). If provided, \code{scaled_prior_variance} is then used as an initial value for the optimization. When \code{estimate_prior_variance = FALSE}, the prior variance for each of the L effects is determined by the value supplied to \code{scaled_prior_variance}.} \item{estimate_prior_method}{The method used for estimating prior variance. When \code{estimate_prior_method = "simple"} is used, the likelihood at the specified prior variance is compared to the likelihood at a variance of zero, and the setting with the larger likelihood is retained.} \item{check_null_threshold}{When the prior variance is estimated, compare the estimate with the null, and set the prior variance to zero unless the log-likelihood using the estimate is larger by this threshold amount. For example, if you set \code{check_null_threshold = 0.1}, this will "nudge" the estimate towards zero when the difference in log-likelihoods is small. A note of caution that setting this to a value greater than zero may lead the IBSS fitting procedure to occasionally decrease the ELBO.} \item{prior_tol}{When the prior variance is estimated, compare the estimated value to \code{prior_tol} at the end of the computation, and exclude a single effect from PIP computation if the estimated prior variance is smaller than this tolerance value.} \item{residual_variance_upperbound}{Upper limit on the estimated residual variance. It is only relevant when \code{estimate_residual_variance = TRUE}.} \item{s_init}{A previous susie fit with which to initialize.} \item{coverage}{A number between 0 and 1 specifying the \dQuote{coverage} of the estimated confidence sets.} \item{min_abs_corr}{Minimum absolute correlation allowed in a credible set. The default, 0.5, corresponds to a squared correlation of 0.25, which is a commonly used threshold for genotype data in genetic studies.} \item{compute_univariate_zscore}{If \code{compute_univariate_zscore = TRUE}, the univariate regression z-scores are outputted for each variable.} \item{na.rm}{Drop any missing values in y from both X and y.} \item{max_iter}{Maximum number of IBSS iterations to perform.} \item{tol}{A small, non-negative number specifying the convergence tolerance for the IBSS fitting procedure. The fitting procedure will halt when the difference in the variational lower bound, or \dQuote{ELBO} (the objective function to be maximized), is less than \code{tol}.} \item{verbose}{If \code{verbose = TRUE}, the algorithm's progress, and a summary of the optimization settings, are printed to the console.} \item{track_fit}{If \code{track_fit = TRUE}, \code{trace} is also returned containing detailed information about the estimates at each iteration of the IBSS fitting procedure.} \item{residual_variance_lowerbound}{Lower limit on the estimated residual variance. It is only relevant when \code{estimate_residual_variance = TRUE}.} \item{refine}{If \code{refine = TRUE}, then an additional iterative refinement procedure is used, after the IBSS algorithm, to check and escape from local optima (see details).} \item{n_purity}{Passed as argument \code{n_purity} to \code{\link{susie_get_cs}}.} \item{bhat}{A p-vector of estimated effects.} \item{shat}{A p-vector of standard errors.} \item{R}{A p by p correlation matrix. It should be estimated from the same samples used to compute \code{bhat} and \code{shat}. Using an out-of-sample matrix may produce unreliable results.} \item{n}{The sample size.} \item{var_y}{The sample variance of y, defined as \eqn{y'y/(n-1)}. When the sample variance cannot be provided, the coefficients (returned from \code{coef}) are computed on the "standardized" X, y scale.} \item{XtX}{A p by p matrix \eqn{X'X} in which the columns of X are centered to have mean zero.} \item{Xty}{A p-vector \eqn{X'y} in which y and the columns of X are centered to have mean zero.} \item{yty}{A scalar \eqn{y'y} in which y is centered to have mean zero.} \item{X_colmeans}{A p-vector of column means of \code{X}. If both \code{X_colmeans} and \code{y_mean} are provided, the intercept is estimated; otherwise, the intercept is NA.} \item{y_mean}{A scalar containing the mean of \code{y}. If both \code{X_colmeans} and \code{y_mean} are provided, the intercept is estimated; otherwise, the intercept is NA.} \item{maf}{Minor allele frequency; to be used along with \code{maf_thresh} to filter input summary statistics.} \item{maf_thresh}{Variants having a minor allele frequency smaller than this threshold are not used.} \item{r_tol}{Tolerance level for eigenvalue check of positive semidefinite matrix of R.} \item{check_input}{If \code{check_input = TRUE}, \code{susie_suff_stat} performs additional checks on \code{XtX} and \code{Xty}. The checks are: (1) check that \code{XtX} is positive semidefinite; (2) check that \code{Xty} is in the space spanned by the non-zero eigenvectors of \code{XtX}.} \item{check_prior}{If \code{check_prior = TRUE}, it checks if the estimated prior variance becomes unreasonably large (comparing with 10 * max(abs(z))^2).} } \value{ A \code{"susie"} object with some or all of the following elements: \item{alpha}{An L by p matrix of posterior inclusion probabilites.} \item{mu}{An L by p matrix of posterior means, conditional on inclusion.} \item{mu2}{An L by p matrix of posterior second moments, conditional on inclusion.} \item{Xr}{A vector of length n, equal to \code{X \%*\% colSums(alpha * mu)}.} \item{lbf}{log-Bayes Factor for each single effect.} \item{lbf_variable}{log-Bayes Factor for each variable and single effect.} \item{intercept}{Intercept (fixed or estimated).} \item{sigma2}{Residual variance (fixed or estimated).} \item{V}{Prior variance of the non-zero elements of b, equal to \code{scaled_prior_variance * var(y)}.} \item{elbo}{The value of the variational lower bound, or \dQuote{ELBO} (objective function to be maximized), achieved at each iteration of the IBSS fitting procedure.} \item{fitted}{Vector of length n containing the fitted values of the outcome.} \item{sets}{Credible sets estimated from model fit; see \code{\link{susie_get_cs}} for details.} \item{pip}{A vector of length p giving the (marginal) posterior inclusion probabilities for all p covariates.} \item{z}{A vector of univariate z-scores.} \item{niter}{Number of IBSS iterations that were performed.} \item{converged}{\code{TRUE} or \code{FALSE} indicating whether the IBSS converged to a solution within the chosen tolerance level.} \code{susie_suff_stat} returns also outputs: \item{XtXr}{A p-vector of \code{t(X)} times the fitted values, \code{X \%*\% colSums(alpha*mu)}.} } \description{ Performs a sparse Bayesian multiple linear regression of y on X, using the "Sum of Single Effects" model from Wang et al (2020). In brief, this function fits the regression model \eqn{y = \mu + X b + e}, where elements of \eqn{e} are \emph{i.i.d.} normal with zero mean and variance \code{residual_variance}, \eqn{\mu} is an intercept term and \eqn{b} is a vector of length p representing the effects to be estimated. The \dQuote{susie assumption} is that \eqn{b = \sum_{l=1}^L b_l} where each \eqn{b_l} is a vector of length p with exactly one non-zero element. The prior on the non-zero element is normal with zero mean and variance \code{var(y) * scaled_prior_variance}. The value of \code{L} is fixed, and should be chosen to provide a reasonable upper bound on the number of non-zero effects to be detected. Typically, the hyperparameters \code{residual_variance} and \code{scaled_prior_variance} will be estimated during model fitting, although they can also be fixed as specified by the user. See functions \code{\link{susie_get_cs}} and other functions of form \code{susie_get_*} to extract the most commonly-used results from a susie fit. } \details{ The function \code{susie} implements the IBSS algorithm from Wang et al (2020). The option \code{refine = TRUE} implements an additional step to help reduce problems caused by convergence of the IBSS algorithm to poor local optima (which is rare in our experience, but can provide misleading results when it occurs). The refinement step incurs additional computational expense that increases with the number of CSs found in the initial run. The function \code{susie_suff_stat} implements essentially the same algorithms, but using sufficient statistics. (The statistics are sufficient for the regression coefficients \eqn{b}, but not for the intercept \eqn{\mu}; see below for how the intercept is treated.) If the sufficient statistics are computed correctly then the results from \code{susie_suff_stat} should be the same as (or very similar to) \code{susie}, although runtimes will differ as discussed below. The simplest sufficient statistics are the sample size \code{n}, and then the p by p matrix \eqn{X'X}, the p-vector \eqn{X'y}, and the sum of squared y values \eqn{y'y}, all computed after centering the columns of \eqn{X} and the vector \eqn{y} to have mean 0; these can be computed using \code{compute_suff_stat}. Alternatively the user can provide \code{n} and \code{bhat} (the univariate OLS estimates from regressing y on each column of X), \code{shat} (the standard errrors from these OLS regressions), the p by p symmetric, positive semidefinite correlation matrix \eqn{R = cov2cor(X'X)}, and the variance of \eqn{y}, again all computed from centered \eqn{X} and \eqn{y}. Note that here \code{R} and \code{bhat} should be computed using the same matrix \eqn{X}. If you do not have access to the original \eqn{X} to compute the matrix \code{R} then use \code{\link{susie_rss}}. The handling of the intercept term in \code{susie_suff_stat} needs some additional explanation. Computing the summary data after centering \code{X} and \code{y} effectively ensures that the resulting posterior quantities for \eqn{b} allow for an intercept in the model; however, the actual value of the intercept cannot be estimated from these centered data. To estimate the intercept term the user must also provide the column means of \eqn{X} and the mean of \eqn{y} (\code{X_colmeans} and \code{y_mean}). If these are not provided, they are treated as \code{NA}, which results in the intercept being \code{NA}. If for some reason you prefer to have the intercept be 0 instead of \code{NA} then set \code{X_colmeans = 0,y_mean = 0}. For completeness, we note that if \code{susie_suff_stat} is run on \eqn{X'X, X'y, y'y} computed \emph{without} centering \eqn{X} and \eqn{y}, and with \code{X_colmeans = 0,y_mean = 0}, this is equivalent to \code{susie} applied to \eqn{X, y} with \code{intercept = FALSE} (although results may differ due to different initializations of \code{residual_variance} and \code{scaled_prior_variance}). However, this usage is not recommended for for most situations. The computational complexity of \code{susie} is \eqn{O(npL)} per iteration, whereas \code{susie_suff_stat} is \eqn{O(p^2L)} per iteration (not including the cost of computing the sufficient statistics, which is dominated by the \eqn{O(np^2)} cost of computing \eqn{X'X}). Because of the cost of computing \eqn{X'X}, \code{susie} will usually be faster. However, if \eqn{n >> p}, and/or if \eqn{X'X} is already computed, then \code{susie_suff_stat} may be faster. } \examples{ # susie example set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[1:4] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) res1 = susie(X,y,L = 10) susie_get_cs(res1) # extract credible sets from fit plot(beta,coef(res1)[-1]) abline(a = 0,b = 1,col = "skyblue",lty = "dashed") plot(y,predict(res1)) abline(a = 0,b = 1,col = "skyblue",lty = "dashed") # susie_suff_stat example input_ss = compute_suff_stat(X,y) res2 = with(input_ss, susie_suff_stat(XtX = XtX,Xty = Xty,yty = yty,n = n, X_colmeans = X_colmeans,y_mean = y_mean,L = 10)) plot(coef(res1),coef(res2)) abline(a = 0,b = 1,col = "skyblue",lty = "dashed") } \references{ G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple new approach to variable selection in regression, with application to genetic fine-mapping. \emph{Journal of the Royal Statistical Society, Series B} \bold{82}, 1273-1300 \doi{10.1101/501114}. Y. Zou, P. Carbonetto, G. Wang and M. Stephens (2021). Fine-mapping from summary data with the \dQuote{Sum of Single Effects} model. \emph{bioRxiv} \doi{10.1101/2021.11.03.467167}. } \seealso{ \code{\link{susie_get_cs}} and other \code{susie_get_*} functions for extracting results; \code{\link{susie_trendfilter}} for applying the SuSiE model to non-parametric regression, particularly changepoint problems, and \code{\link{susie_rss}} for applying the SuSiE model when one only has access to limited summary statistics related to \eqn{X} and \eqn{y} (typically in genetic applications). } susieR/man/susie_trendfilter.Rd0000644000176200001440000000622614055444252016333 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_trendfilter.R \name{susie_trendfilter} \alias{susie_trendfilter} \title{Apply susie to trend filtering (especially changepoint problems), a type of non-parametric regression.} \usage{ susie_trendfilter(y, order = 0, standardize = FALSE, use_mad = TRUE, ...) } \arguments{ \item{y}{An n-vector of observations ordered in time or space (assumed to be equally spaced).} \item{order}{An integer specifying the order of trend filtering. The default, \code{order = 0}, corresponds to "changepoint" problems (\emph{i.e.}, piecewise constant \eqn{mu}). Although \code{order > 0} is implemented, we do not recommend its use; in practice, we have found problems with convergence of the algorithm to poor local optima, producing unreliable inferences.} \item{standardize}{Logical indicating whether to standardize the X variables ("basis functions"); \code{standardize = FALSE} is recommended as these basis functions already have a natural scale.} \item{use_mad}{Logical indicating whether to use the "median absolute deviation" (MAD) method to the estimate residual variance. If \code{use_mad = TRUE}, susie is run twice, first by fixing the residual variance to the MAD value, then a second time, initialized to the first fit, but with residual variance estimated the usual way (by maximizing the ELBO). We have found this strategy typically improves reliability of the results by reducing a tendency to converge to poor local optima of the ELBO.} \item{...}{Other arguments passed to \code{\link{susie}}.} } \value{ A "susie" fit; see \code{\link{susie}} for details. } \description{ Fits the non-parametric Gaussian regression model \eqn{y = mu + e}, where the mean \eqn{mu} is modelled as \eqn{mu = Xb}, X is a matrix with columns containing an appropriate basis, and b is vector with a (sparse) SuSiE prior. In particular, when \code{order = 0}, the jth column of X is a vector with the first j elements equal to zero, and the remaining elements equal to 1, so that \eqn{b_j} corresponds to the change in the mean of y between indices j and j+1. For background on trend filtering, see Tibshirani (2014). See also the "Trend filtering" vignette, \code{vignette("trend_filtering")}. } \details{ This implementation exploits the special structure of X, which means that the matrix-vector product \eqn{X^Ty} is fast to compute; in particular, the computation time is \eqn{O(n)} rather than \eqn{O(n^2)} if \code{X} were formed explicitly. For implementation details, see the "Implementation of SuSiE trend filtering" vignette by running \code{vignette("trendfiltering_derivations")}. } \examples{ set.seed(1) mu = c(rep(0,50),rep(1,50),rep(3,50),rep(-2,50),rep(0,200)) y = mu + rnorm(400) s = susie_trendfilter(y) plot(y) lines(mu,col = 1,lwd = 3) lines(predict(s),col = 2,lwd = 2) # Calculate credible sets (indices of y that occur just before # changepoints). susie_get_cs(s) # Plot with credible sets for changepoints. susie_plot_changepoint(s,y) } \references{ R. J. Tibshirani (2014). Adaptive piecewise polynomial estimation via trend filtering. \emph{Annals of Statistics} \bold{42}, 285-323. } susieR/man/summary.susie.Rd0000644000176200001440000000120313760346546015424 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summary.susie.R \name{summary.susie} \alias{summary.susie} \alias{print.summary.susie} \title{Summarize Susie Fit.} \usage{ \method{summary}{susie}(object, ...) \method{print}{summary.susie}(x, ...) } \arguments{ \item{object}{A susie fit.} \item{\dots}{Additional arguments passed to the generic \code{summary} or \code{print.summary} method.} \item{x}{A susie summary.} } \value{ \code{summary.susie} returns a list containing a data frame of variables and a data frame of credible sets. } \description{ \code{summary} method for the \dQuote{susie} class. } susieR/man/N2finemapping.Rd0000644000176200001440000000330514051474607015274 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{N2finemapping} \alias{N2finemapping} \title{Simulated Fine-mapping Data with Two Effect Variables} \format{ \code{N2finemapping} is a list with the following elements: \describe{ \item{X}{Centered and scaled genotype data.} \item{chrom}{Chromomsome of the original data, in hg38 coordinates.} \item{pos}{Chromomosomal position of the original data, in hg38 coordinates. The information can be used to compare impact of using other genotype references of the same variables in \code{susie_rss} application.} \item{true_coef}{Simulated effect sizes.} \item{residual_variance}{Simulated residual covariance matrix.} \item{Y}{Simulated multivariate response.} \item{allele_freq}{Allele frequencies based on the original genotype data.} \item{V}{Suggested prior covariance matrix for effect sizes of the two non-zero effect variables.} } } \description{ This data set contains a genotype matrix for 574 individuals and 1,002 variables. The variables are genotypes after centering and scaling, and therefore retain the correlation structure of the original genotype data. Two of the variables have non-zero effects on the multivariate response. The response data are generated under a multivariate linear regression model. See Wang \emph{et al} (2020) for details. } \examples{ data(N2finemapping) } \references{ G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple new approach to variable selection in regression, with application to genetic fine-mapping. \emph{Journal of the Royal Statistical Society, Series B} \doi{10.1101/501114}. } \keyword{data} susieR/man/susie_init_coef.Rd0000644000176200001440000000175613755042336015756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/initialize.R \name{susie_init_coef} \alias{susie_init_coef} \title{Initialize a susie object using regression coefficients} \usage{ susie_init_coef(coef_index, coef_value, p) } \arguments{ \item{coef_index}{An L-vector containing the the indices of the nonzero coefficients.} \item{coef_value}{An L-vector containing initial coefficient estimates.} \item{p}{A scalar giving the number of variables.} } \value{ A list with elements \code{alpha}, \code{mu} and \code{mu2} to be used by \code{susie}. } \description{ Initialize a susie object using regression coefficients } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[sample(1:1000,4)] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) # Initialize susie to ground-truth coefficients. s = susie_init_coef(which(beta != 0),beta[beta != 0],length(beta)) res = susie(X,y,L = 10,s_init=s) } susieR/man/compute_ss.Rd0000644000176200001440000000132414051346274014755 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compute_ss.R \name{compute_ss} \alias{compute_ss} \title{Compute sufficient statistics for input to \code{susie_suff_stat}} \usage{ compute_ss(X, y, standardize = FALSE) } \arguments{ \item{X}{An n by p matrix of covariates.} \item{y}{An n vector.} \item{standardize}{Logical flag indicating whether to standardize columns of X to unit variance prior to computing summary data.} } \value{ A list of sufficient statistics (X'X, X'y, y'y and n) } \description{ This is a synonym for \code{compute_suff_stat} included for historical reasons (deprecated). } \examples{ data(N2finemapping) ss = compute_ss(N2finemapping$X, N2finemapping$Y[,1]) } susieR/man/susie_auto.Rd0000644000176200001440000000557014055242561014761 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_zzz_auto.R \name{susie_auto} \alias{susie_auto} \title{Attempt at Automating SuSiE for Hard Problems} \usage{ susie_auto( X, y, L_init = 1, L_max = 512, verbose = FALSE, init_tol = 1, standardize = TRUE, intercept = TRUE, max_iter = 100, tol = 0.01, ... ) } \arguments{ \item{X}{An n by p matrix of covariates.} \item{y}{The observed responses, a vector of length n.} \item{L_init}{The initial value of L.} \item{L_max}{The largest value of L to consider.} \item{verbose}{If \code{verbose = TRUE}, the algorithm's progress, and a summary of the optimization settings, are printed to the console.} \item{init_tol}{The tolerance to passed to \code{susie} during early runs (set large to shorten the initial runs).} \item{standardize}{If \code{standardize = TRUE}, standardize the columns of X to unit variance prior to fitting. Note that \code{scaled_prior_variance} specifies the prior on the coefficients of X \emph{after} standardization (if it is performed). If you do not standardize, you may need to think more carefully about specifying \code{scaled_prior_variance}. Whatever your choice, the coefficients returned by \code{coef} are given for \code{X} on the original input scale. Any column of \code{X} that has zero variance is not standardized.} \item{intercept}{If \code{intercept = TRUE}, the intercept is fitted; it \code{intercept = FALSE}, the intercept is set to zero. Setting \code{intercept = FALSE} is generally not recommended.} \item{max_iter}{Maximum number of IBSS iterations to perform.} \item{tol}{A small, non-negative number specifying the convergence tolerance for the IBSS fitting procedure. The fitting procedure will halt when the difference in the variational lower bound, or \dQuote{ELBO} (the objective function to be maximized), is less than \code{tol}.} \item{\dots}{Additional arguments passed to \code{\link{susie}}.} } \value{ See \code{\link{susie}} for a description of return values. } \description{ \code{susie_auto} is an attempt to automate reliable running of susie even on hard problems. It implements a three-stage strategy for each L: first, fit susie with very small residual error; next, estimate residual error; finally, estimate the prior variance. If the last step estimates some prior variances to be zero, stop. Otherwise, double L, and repeat. Initial runs are performed with relaxed tolerance; the final run is performed using the default susie tolerance. } \examples{ set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[1:4] = 1 X = matrix(rnorm(n*p),nrow = n,ncol = p) X = scale(X,center = TRUE,scale = TRUE) y = drop(X \%*\% beta + rnorm(n)) res = susie_auto(X,y) plot(beta,coef(res)[-1]) abline(a = 0,b = 1,col = "skyblue",lty = "dashed") plot(y,predict(res)) abline(a = 0,b = 1,col = "skyblue",lty = "dashed") } \seealso{ \code{\link{susie}} } susieR/man/compute_suff_stat.Rd0000644000176200001440000000166114051346274016332 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compute_ss.R \name{compute_suff_stat} \alias{compute_suff_stat} \title{Compute sufficient statistics for input to \code{susie_suff_stat}} \usage{ compute_suff_stat(X, y, standardize = FALSE) } \arguments{ \item{X}{An n by p matrix of covariates.} \item{y}{An n vector.} \item{standardize}{Logical flag indicating whether to standardize columns of X to unit variance prior to computing summary data} } \value{ A list of sufficient statistics (\code{XtX, Xty, yty, n}) and \code{X_colmeans}, \code{y_mean}. } \description{ Computes the sufficient statistics \eqn{X'X, X'y, y'y} and \eqn{n} after centering (and possibly standardizing) the columns of \eqn{X} and centering \eqn{y} to have mean zero. We also store the column means of \eqn{X} and mean of \eqn{y}. } \examples{ data(N2finemapping) ss = compute_suff_stat(N2finemapping$X, N2finemapping$Y[,1]) } susieR/man/susie_plot_changepoint.Rd0000644000176200001440000000241114061022471017326 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/susie_plot_changepoint.R \name{susie_plot_changepoint} \alias{susie_plot_changepoint} \title{Plot changepoint data and susie fit using ggplot2} \usage{ susie_plot_changepoint( s, y, line_col = "blue", line_size = 1.5, cs_col = "red" ) } \arguments{ \item{s}{A susie fit generated by \code{susie_trendfilter(y,order = 0)}.} \item{y}{An n-vector of observations that are ordered in time or space (assumed equally-spaced).} \item{line_col}{Color for the line showing fitted values.} \item{line_size}{Size of the lines showing fitted values} \item{cs_col}{Color of the shaded rectangles showing credible sets.} } \value{ A ggplot2 plot object. } \description{ Plots original data, y, overlaid with line showing susie fitted value and shaded rectangles showing credible sets for changepoint locations. } \examples{ set.seed(1) mu = c(rep(0,50),rep(1,50),rep(3,50),rep(-2,50),rep(0,300)) y = mu + rnorm(500) # Here we use a less sensitive tolerance so that the example takes # less time; in practice you will likely want to use a more stringent # setting such as tol = 0.001. s = susie_trendfilter(y,tol = 0.1) # Produces ggplot with credible sets for changepoints. susie_plot_changepoint(s,y) } susieR/DESCRIPTION0000644000176200001440000000402714143515472013243 0ustar liggesusersEncoding: UTF-8 Type: Package Package: susieR Title: Sum of Single Effects Linear Regression Description: Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) and Zou et al (2021) . These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables). Date: 2021-11-12 Version: 0.11.92 Authors@R: c(person("Gao","Wang",role="aut",email="wang.gao@columbia.edu"), person("Yuxin","Zou",role="aut"), person("Kaiqian","Zhang",role="aut"), person("Peter","Carbonetto",role=c("aut","cre"), email="peter.carbonetto@gmail.com"), person("Matthew","Stephens",role="aut")) URL: https://github.com/stephenslab/susieR BugReports: https://github.com/stephenslab/susieR/issues License: MIT + file LICENSE Depends: R (>= 3.0.0) Imports: methods, graphics, grDevices, stats, Matrix, matrixStats, mixsqp, reshape, crayon, ggplot2 Suggests: curl, testthat, microbenchmark, knitr, rmarkdown, L0Learn, genlasso, Rfast LazyData: yes LazyDataCompression: xz NeedsCompilation: no RoxygenNote: 7.1.2 VignetteBuilder: knitr Packaged: 2021-11-12 14:53:21 UTC; pcarbo Author: Gao Wang [aut], Yuxin Zou [aut], Kaiqian Zhang [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut] Maintainer: Peter Carbonetto Repository: CRAN Date/Publication: 2021-11-12 16:50:02 UTC susieR/build/0000755000176200001440000000000014143477740012636 5ustar liggesuserssusieR/build/vignette.rds0000644000176200001440000000077114143477740015202 0ustar liggesusersS]K0qqGw _X}{&)I7goۤaE9ۄz65ĵE9L Cl,X~esOAf>Ȏc.,xM&g1bcY먆NZŵ1qٵTe_<d<ۭ|U-\0sui u|W>\~+3p M@(O@Lsq+`V{ru.󼢉7J sɤaJS]|ц_FH[ORW۶tH=zC^7VƇef0{YbX|[%,'^susieR/build/partial.rdb0000644000176200001440000010334314143477564014773 0ustar liggesusers |Kz6<<^p08x';|o]1q{^ HI[+Yeɲ,ɒeYC|Ɏ$vINJ'NNl+b;x_׿gjxcIz PꫯL&ӕٺ+ӵmd/-OzB̝g]۷v-1{l7YcWevѩ+[ L; Uk9Ŀ=?x/~B{_''k) ~+[ yȍ݂WtHv*A˟2zN U\ ӿQ_~b_. ]kGO ոVIAZY qtGR³+ʍ`X,V+?쎾=:W-W*ޭ[9}hP {nloL,9]*s m+'jaCqҾR5Ŗ]׶ڶ`n& kܴ&x7 H@<N <YK1o+H)w d*j*x 'NV\a иNucB}kdFxm-Xuw | sW@<-Cto}n;F8te'ŐbN,Vr*Jj{N].o7eXjtHVNzzԃѥCntVv&bT.$yAل?:][*P$ ;퇃YPJl^5Y4Q?# 4f]N)v+09yeGi{1nV&NmS +WV YifH,QʁW d,;UU+˗oU;kz3iC;T'i}7C׶V<>.ݐL7= ZطBzN۰ɶqg hk,O퐱.y~ mV Q-W,?J2|Z:ƲoA״:ֹp o΅[=+4C|?^|]Y[ջ' h/ANĈ]CJFQn"F~&~RQ}RἸ ;>#ܗv(v?ohtQHq|5{/H446MKyڑpp;!t"K/u^x:M07PGPv8ŚUZЗP<`cothTY nFTO OA"@G@qIV!VCUiBT<7e{YiQE9ț0bP##M7QQVd%~O`dE 6NGs'/ZbܦlՖmOi$qmLYi"$ӽ?( N8q:)}* zN['e;;EeKOũ!dNC/RR9mvG^ھsei=֖K9@uAVbx }| ܅f@&ucy?n}3x?t fJx4ke'|UqiQݡWdiECצ1-:Q8!f&k-5QhU(~_KUۗ}sObrӫem1 \PE";zQDf]p B@~P=h'hkՖ} hs̲4yvgw}apB~|;dyk?! .%nƞxǮB_˧$  +Mu-QT?^/"E|C79sAZD'~J͛7 ~?;>:6>>~%myɃקgLy,d}іe{;_*D۪B 0 k!񛬹;soj /e756bS)nmm{b8wE߶`A%:Ւ"f+ENmUS n)9`wR5}]R\?.Fm$G̦jDZr}5wf%=,٥ i]p׹cF.x˧wSf%lZA:(%6oI(U@r[WfUvFUi{Ҭ͖!R^m(+P巷Qo/n/zmގ9i{˗̗ٓ+q{ |Xly[w痜o _;Zq7E~`zՍX@H_5?W\ ZǩvVjY6hm%w=3۶J>Dz5׫Nk?/xr²#+sU; We+FZ=_+ v̯Z}I:kEgIل^܁_I؆߲ֆ 8T %ڔ*Ҫ/""lCUhmЭZY#'hZnR15ܤzVk~cTb3շ^uu =Wꅂ|rzWX^/llN5 8!|k:ڴy:Kn:F|U¡D#cx3M*EKɪyni- a-GTdN>VޠƛR_Omek5M)6elgw.z/$heSfG` >+l!F:j% *vRԆ윯ZR_Vɤ|eIFObm߬4]koy 2|ڦ˯hAk׽`-olOꬡIآlol<{*y2W³EDW\@$mk:.W_mNs.A^jYle|GT`4;{_󓇦ק.|WkEFhςѹR9hb\qO(G 4|߻[.wœ9%u,~ex_~knxZކ,d,z8yHlm?^z?Z_}a>/>x 8" w 'grɊrhל`' wT_e~#&Zk}ύn8A`%/>{'uȯyVͨ6ڰ%Y[ M k56.hջ`J㬟9zx%G/Gnщt54%_C/?ĭ_EcD=x2;護Pp!^Hp^>7iDLT=*AC7b{k[ƣxKǐkwޯ[Nt6m^SH8XrѸlnu7ΊoSJhu73!c{o[/R7^]սn9vmUwiSN夶Gz{j v"_ӷNI070opԫ3J,U֩N5 ' CM|QSuJmToTP: !Ni6SjuJen|S+mw*/;2Ag_)SoDzģ؈;t|SOiae.ZbģGgt|SxJ$[+ә^Vo<Ɓqa/(J*gm]% %d >1!cw6nt0>߇2Jc^JDJWU.ƒj}}2:TCt>umj bG"LKmKhXc^, ɬKgߡ+ r_uX T7}$sQ|=/ 3m&e܊bfOAtxf(}fX% !8YLRywHG;Tx:MCh3;xGM:LGѡ \e&{xrGѽ7qA?pP7״I_t )F(]+3|AεKC7"3Cx:MCh3}]JP >=5Db-0}Hny3Q+;0z@"ы(&^'1nVW;OHs[vQ華Bң)S-|- >LUV_v|?< N7NɏxXƤ3}ѓѥs&C1 ^ G{  /@u*n񖻍ۍ|*4G ;"iDq#]ѱzI(h ܧB+OH&y_/ n; +Nbȶ%eNwU~j'2= GRt,`COjOY6آfA\5īKGrA~`k%s=IR@J%#3m2]P8YpOYRv`cG!]:ybģH^&dKIZu ؒ/i%92*#vЏ{ɂ˟-"r 238I9[%<LOiy <'I 6F |"T` UxA-Ah.yۯbv /t^}4ISdHw n$a&I&N*]Y}JG츅RhVs/JU9i vFiת΢Srk|+nŷW0\e_qTs[USs=; ;_d6 lȳYCѵyhIBl6K{Dpv}nd b1I^| nM."DZ9ZQ⻵UmQz>R:CC#ϳs{>\g^f Lλ v7' 9;s9dF,V C`svGiW+4ZaFaUD\kæneXƄ7s]<'+lF 4FQ1愙)zդSucn K֌KgތA[FekhZv=1?/Ԗd$I2{cR|C80+Ec\U*xmƄ? 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[%UJKVuXNsSSE UT8x,Q""4JTi9eЈpˊ׼$@#dwX{!$6W%74UPgŸ\sU jw7* p#J/Ə&I%jvClaC#mm|Q][n* e̹rLŝಁd˻45ɟx "Y.+:>4Ǟ^\1c\@jdtĈjӓl(qxˊܱ^-mDP+ޞҋRN/qLy׸܎c!Ys:6O]ɘ:6t] Q r;Zj3vhkS6T{ZuN`?W8 M\"M@ Gz ezjΪUr[S :M 5Bj !;q xꍸ͹:=i|llLϷa66|-=:ە\Eپ Z_6ID7|KԷa/Hj> C!w͹<͹ K_X񍖟oϽ @pt" k!UBϳH3/1tVw& hj`* pXkQ]G8~pjٮ.!"egÁ[* ,躈%'5JA%*%:D+x'/ 1 xZ,9=dEisK5{0W`.3^0' Qծ**ZyrrNTVyА| acў3 t!VZ#+*ߢD ͙aϘI҆ҡtj%(LVi?4+")(Cxi5Yhqx +8D:uw:YƸU&(0(x ;p3쏀P&3 &u P] ֥>ZD}rsl:ϞX3ϱl=׮VhUbFehblb,g,p6!^м=`̵Iߣ[Cfa(GyrYt8tۏUUhqFB8prHqLABZWi*缵O]%VI> p[`WppgOlH #d#vH=eB]K(ETƏ.`tݘPWITҙXJŔbOqY1~ydؘJÜsY#Wm>b1;~1:ŚS*~畂7o{ E<;> >G_Iy`ٹ7S.w7/|+LʙB6':zMoeC=VJpst4FGب7ֶ,Fɫֳb?H#2o\rm9oVM%*V7}tk|P 딪Ӎ∬D_jvhT 8vVNլb%$mxL= ݔ5W̬7Fyŷ[LOei$1M2~zmΡ^Is ֩Mu!uߗ 9AMy1jG9*Ie_lVWT=w?z&U.3A^ڍoqwboH,uoG ~w-?Q- Do_ؑP1q@M]txA1'=I?[ £h 77YJ.w"@s m(-}539{R{ಁmXS/|N7 z>5b*|@ݳR6ʾ/@!cr^RPJ_%xnvb9-"C1Ə>i_>~}5Oseb>n 1 #ocvntz+>5~Q~?k rsusieR/tests/0000755000176200001440000000000013476534107012700 5ustar liggesuserssusieR/tests/testthat/0000755000176200001440000000000014143515472014534 5ustar liggesuserssusieR/tests/testthat/test_SER_posterior_e_loglik.R0000644000176200001440000000102013437306365022317 0ustar liggesuserscontext("test_SER_posterior_e_loglik.R") test_that("SER_posterior_e_loglik agrees with version 0.3", with(simulate(sparse=T), { original.res = readRDS('SER_original_res.rds') Eb = rep(1, p) Eb2 = rep(1, p) s2 = s$sigma2 scaledX = set_X_attributes(X) scaledX.sparse = set_X_attributes(X.sparse) dense.res = SER_posterior_e_loglik(scaledX,y,s2,Eb,Eb2) sparse.res = SER_posterior_e_loglik(scaledX.sparse,y,s2,Eb,Eb2) expect_equal(dense.res, original.res) expect_equal(sparse.res, original.res) })) susieR/tests/testthat/ER2_original_res.rds0000644000176200001440000000005713607476333020403 0ustar liggesusersb```b`fef`b2aUu'susieR/tests/testthat/test_get_ER2_rss.R0000644000176200001440000000142614057454526020045 0ustar liggesuserscontext("test_get_ER2_rss.R") test_that("get_ER2_rss (lambda = 0) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('ER2_rss_lambda0_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 0 res = get_ER2_rss(s$sigma2, R, z, s) expect_equal(res, original.res, tolerance=1e-4) })) test_that("get_ER2_rss (lambda = 1) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('ER2_rss_lambda1_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 1 res = get_ER2_rss(s$sigma2, R, z, s) expect_equal(res, original.res, tolerance=1e-4) })) susieR/tests/testthat/test_init.R0000644000176200001440000000074214061022471016653 0ustar liggesuserscontext("test_init.R") test_that("susie init", with(simulate(sparse=T), { original.res = readRDS('susiefit_original_res.rds') original.res$lbf_variable = matrix(0, nrow(original.res$alpha), ncol(original.res$alpha)) expect_warning(capture_output(susie(X, y, L=2, tol=1E-2, s_init = original.res, estimate_prior_variance = FALSE,verbose=T))) expect_message(capture_output(susie(X, y, L=20, tol=1E-2, s_init = original.res, estimate_prior_variance = FALSE, verbose=T))) })) susieR/tests/testthat/test_SER_posterior_e_loglik_rss.R0000644000176200001440000000201314057454555023215 0ustar liggesuserscontext("test_SER_posterior_e_loglik_rss.R") test_that("SER_posterior_e_loglik_rss (lambda = 0) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('SER_rss_lambda0_res.rds')$s Ez = rep(1, p) Ez2 = rep(1, p) s2 = s$sigma2 ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 0 Sigma = update_Sigma(R, s2, z) res = SER_posterior_e_loglik_rss(R, Sigma, z,Ez,Ez2) expect_equal(res, original.res, tolerance=1e-4) })) test_that("SER_posterior_e_loglik_rss (lambda = 1) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('SER_rss_lambda1_res.rds')$s Ez = rep(1, p) Ez2 = rep(1, p) s2 = s$sigma2 ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 1 Sigma = update_Sigma(R, s2, z) res = SER_posterior_e_loglik_rss(R, Sigma, z,Ez,Ez2) expect_equal(res, original.res, tolerance=1e-4) })) susieR/tests/testthat/test_small_resid.R0000644000176200001440000000050314051743475020216 0ustar liggesusers# context("test_small_resid.R") # This test comes from Issue #7. # test_that("susie works when residual variance is small",{ # dat <- readRDS("full_data_1_sim_gaussian_null_1.rds")$data # fit <- susie(dat$X,dat$Y,L = 10,null_weight = 0) # expect_equal(fit$sigma2,1e-4) # expect_true(all(is.finite(fit$elbo))) # }) susieR/tests/testthat/objective_rss_lambda0_res.rds0000644000176200001440000000021313607145336022336 0ustar liggesusersb```b`fef`b1|> f ft~VVr>-30pi ԔDsSTS33T4 KTcd$ӔscTSc&$}yPØPozfI|r~n.L IsusieR/tests/testthat/test_susie.R0000644000176200001440000000336713612573541017060 0ustar liggesuserscontext("test_susie.R") test_that("susie agrees with version 0.3", with(simulate(sparse=T), { original.res = readRDS('susiefit_original_res.rds') original.res2 = readRDS('susiefit_original_res2.rds') original.res3 = readRDS('susiefit_original_res3.rds') original.res4 = readRDS('susiefit_original_res4.rds') original.res$Xr = as.vector(original.res$Xr) original.res2$Xr = as.vector(original.res2$Xr) original.res3$Xr = as.vector(original.res3$Xr) original.res4$Xr = as.vector(original.res4$Xr) dense.res = susie(X, y, tol=1E-2, estimate_prior_variance = FALSE) sparse.res = susie(X.sparse, y, tol=1E-2, estimate_prior_variance = FALSE) dense.res2 = susie(X, y, standardize=TRUE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE) sparse.res2 = susie(X.sparse, y, standardize=TRUE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE) dense.res3 = susie(X, y, standardize=FALSE, intercept = TRUE, tol=1E-2, estimate_prior_variance = FALSE) sparse.res3 = susie(X.sparse, y, standardize=FALSE, intercept = TRUE, tol=1E-2, estimate_prior_variance = FALSE) dense.res4 = susie(X, y, standardize=FALSE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE) sparse.res4 = susie(X.sparse, y, standardize=FALSE, intercept = FALSE, tol=1E-2, estimate_prior_variance = FALSE) expect_equal_susie(sparse.res, original.res) expect_equal_susie(dense.res, original.res) expect_equal_susie(sparse.res2, original.res2) expect_equal_susie(dense.res2, original.res2) expect_equal_susie(sparse.res3, original.res3) expect_equal_susie(dense.res3, original.res3) expect_equal_susie(sparse.res4, original.res4) expect_equal_susie(dense.res4, original.res4) }))susieR/tests/testthat/test_intercept_standardize.R0000644000176200001440000000110613437306365022305 0ustar liggesuserscontext("test_intercept_standardize.R") test_that("scaling and intercept works as expected", with(simulate(200,1000), { s1 = susie(X,y,intercept= TRUE, standardize=TRUE) s2 = susie(X,y,intercept = FALSE, standardize = FALSE) s3 = susie(X,y,intercept =TRUE, standardize = FALSE) s4 = susie(X,y,intercept = FALSE,standardize = TRUE) expect_equal(predict(s2),predict(s2,X)) expect_equal(predict(s4),predict(s4,X)) expect_equal(predict(s1),predict(s1,X)) expect_equal(predict(s3),predict(s3,X)) expect_equal(s2$intercept, 0) expect_equal(s4$intercept, 0) })) susieR/tests/testthat/test_Eloglik_rss.R0000644000176200001440000000144014057454413020173 0ustar liggesuserscontext("test_Eloglik_rss.R") test_that("Eloglik_rss (lambda = 0) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('Eloglik_rss_lambda0_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 0 res = Eloglik_rss(s$sigma2, R, z, s) expect_equal(res, original.res, tolerance=1e-4) })) test_that("Eloglik_rss (lambda = 1) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('Eloglik_rss_lambda1_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 1 res = Eloglik_rss(s$sigma2, R, z, s) expect_equal(res, original.res, tolerance=1e-4) })) susieR/tests/testthat/SER_original_res.rds0000644000176200001440000000005713607476333020444 0ustar liggesusersb```b`fef`b2:Mꅛ0_susieR/tests/testthat/test_susie_get_cs.R0000644000176200001440000000152514143475555020404 0ustar liggesuserscontext("test_susie_get_cs.R") test_that("susie_get_cs purity calculations are correct",{ # Simulate a data set with correlated variables. dat <- simulate(40,100) X <- dat$X y <- dat$y X <- cbind(X, X + matrix(rnorm(4000),40,100)/20, X + matrix(rnorm(4000),40,100)/20) # Fit a susie model. fit <- susie(X,y,estimate_prior_variance = FALSE) # The purity calculations should be the same whether or not the # Rfast package functions are used, and all the purity statistics # should be positive. set.seed(1) purity1 <- susie_get_cs(fit,X,min_abs_corr = 0,use_rfast = FALSE)$purity expect_gt(min(purity1),0) skip_if_not_installed("Rfast") set.seed(1) purity2 <- susie_get_cs(fit,X,min_abs_corr = 0,use_rfast = TRUE)$purity expect_equal(purity1,purity2,scale = 1,tolerance = 1e-15) }) susieR/tests/testthat/test_susie_XtX_Xty.R0000644000176200001440000000746314141046730020521 0ustar liggesuserscontext("test_susie_XtX_Xty.R") test_that(paste("compute_ss XtX calculations are correct with", "standardize = FALSE and with standardize = TRUE"),{ set.seed(1) n <- 10 p <- 5 X <- matrix(rnorm(n*p),n,p) y <- rnorm(n) Y1 <- scale(X,center = TRUE,scale = FALSE) Y2 <- scale(X,center = TRUE,scale = TRUE) out1 <- compute_suff_stat(X,y,standardize = FALSE) out2 <- compute_suff_stat(X,y,standardize = TRUE) dimnames(out1$XtX) <- NULL dimnames(out2$XtX) <- NULL expect_equal(out1$XtX,crossprod(Y1),scale = 1,tolerance = 1e-14) expect_equal(out2$XtX,crossprod(Y2),scale = 1,tolerance = 1e-14) # Run the same checks again, but with X now being a sparse matrix. X <- as(X,"dgCMatrix") out1 <- compute_suff_stat(X,y,standardize = FALSE) out2 <- compute_suff_stat(X,y,standardize = TRUE) dimnames(out1$XtX) <- NULL dimnames(out2$XtX) <- NULL expect_equal(out1$XtX,crossprod(Y1),scale = 1,tolerance = 1e-14) expect_equal(out2$XtX,crossprod(Y2),scale = 1,tolerance = 1e-14) }) test_that("Results from sufficient stat vs original data", with(simulate(200,1000), { ss = compute_ss(X, y, standardize = FALSE) expect_warning(res <- susie(X, y, intercept = TRUE, standardize = TRUE, max_iter = 2, estimate_residual_variance=FALSE, estimate_prior_variance = FALSE)) expect_warning(res2 <- susie_suff_stat(XtX = ss$XtX, Xty = ss$Xty, yty = ss$yty, n = ss$n, standardize = TRUE, max_iter = 2, estimate_prior_variance =FALSE, estimate_residual_variance = FALSE)) expect_equal(res$alpha, res2$alpha) expect_equal(res$mu, res2$mu) expect_equal(res$mu2, res2$mu2) expect_equal(res$V, res2$V) expect_equal(res$elbo, res2$elbo) expect_equal(coef(res)[-1], coef(res2)[-1]) cm = colMeans(X, na.rm = TRUE) csd = compute_colSds(X) csd[csd==0] = 1 X.cs = t( (t(X) - cm) / csd ) expect_equal(crossprod(X.cs, res$Xr), res2$XtXr) })) test_that("Results from sufficient stat vs original data: estimate residual variance", with(simulate(200,1000), { ss = compute_ss(X, y, standardize = FALSE) expect_warning(res <- susie(X, y, intercept = TRUE, standardize = TRUE, max_iter = 2, estimate_residual_variance=TRUE, estimate_prior_variance = FALSE)) expect_warning(res2 <- susie_suff_stat(XtX = ss$XtX, Xty = ss$Xty, yty = ss$yty, n = ss$n, standardize = TRUE, max_iter = 2, estimate_prior_variance = FALSE, estimate_residual_variance = TRUE)) expect_equal(res$alpha, res2$alpha) expect_equal(res$mu, res2$mu) expect_equal(res$mu2, res2$mu2) expect_equal(res$V, res2$V) expect_equal(res$elbo, res2$elbo) expect_equal(res$sigma2, res2$sigma2) expect_equal(coef(res)[-1], coef(res2)[-1]) cm = colMeans(X, na.rm = TRUE) csd = compute_colSds(X) csd[csd==0] = 1 X.cs = t( (t(X) - cm) / csd ) expect_equal(crossprod(X.cs, res$Xr), res2$XtXr) })) test_that("MAF filter works", with(simulate(200,1000), { X.maf = cbind(X, 0) ss = compute_ss(X, y, standardize = FALSE) ss.maf = compute_ss(X.maf, y, standardize = FALSE) maf = c(rep(1,1000),0.01) res1 <- susie_suff_stat(XtX = ss$XtX, Xty = ss$Xty, yty = ss$yty, n = ss$n, standardize = TRUE, estimate_prior_variance =FALSE, estimate_residual_variance = FALSE) res2 <- susie_suff_stat(XtX = ss.maf$XtX, Xty = ss.maf$Xty, yty = ss$yty, n = ss$n, maf_thresh = 0.05, maf = maf, standardize = TRUE, estimate_prior_variance =FALSE, estimate_residual_variance = FALSE) expect_equal(res1$alpha, res2$alpha) expect_equal(res1$mu, res2$mu) expect_equal(res1$mu2, res2$mu2) expect_equal(res1$V, res2$V) expect_equal(res1$elbo, res2$elbo) expect_equal(coef(res1), coef(res2)) expect_equal(res1$XtXr, res2$XtXr) })) susieR/tests/testthat/objective_rss_lambda1_res.rds0000644000176200001440000000021313607145336022337 0ustar liggesusersb```b`fef`b1|> f<\@ 5SSM-SMLS-,,SSSMLSS̍RM X%C c 2C%@*3(GOsusieR/tests/testthat/vbupdate_rss_lambda1_res.rds0000644000176200001440000003433413607145336022212 0ustar liggesusersy8m6,<2e(d.ID !i0E)*$EH)* )IЈ.R$(R2F|<۶_wvo\vyuDZG4vNv>iyB4x10;hoʸ?JE Mg ϲ꩐oa5&:c%8uџ\Opi(٧gf_>cag`x˛f oWօ]0ԄčǺrr0S3OaUX9)vvO9E8=[ *.s`o`"~̾WssKiVz%,M/ƙLOpd ͆E +/n6[[ڻ0<~4ހq/aGoqPf oH~* GX {~o}/%.U7py<KӮGY<,ᄍGGR Xx>8agZA}Q]t/ , *`x_ۊy.Þd sjބc5K1xElV xC)"9l4{Zie}yh O40m/0^&ܒcW8Rf_Gm9(<9E`u%+FZVBRҢ%h}U{v8 ie3%0W>y|cq/"\`o`1yC8OK},UE0;ԧ+`Ϗ?iG|jξ|QVy7눨ڞAoAY$8~Jtښ `eaǍ]2sOqG~t'7Ln -j$f,}٥Rɭ~,|j!ZǶG/pqzTݞ9;0z&=cgʑj"/,qS՜딽()WB$vx0:_}X؊x!0<ǯqũDj.a 'Ugbax&}& 3X%MƙYS08Z:vo7xWZ*o2w` 4*/9W[:fYc'q/ED?VTƓǷq\HK =[?UKcsT??n'{}]/ op8Eږ,|/ h,e!lˁyEe? 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M9:;khfld_W+_K!HesusieR/tests/testthat/test_get_ER2.R0000644000176200001440000000070613607476333017156 0ustar liggesuserscontext("test_get_ER2.R") test_that("get_ER2 agrees with version 0.3", with(simulate(sparse = TRUE), { original.res = readRDS('ER2_original_res.rds') scaledX = set_X_attributes(X) scaledX.sparse = set_X_attributes(X.sparse) s$Xr = colSums(compute_MXt(s$alpha*s$mu, scaledX)) dense.res = get_ER2(scaledX, y, s) sparse.res = get_ER2(scaledX.sparse, y, s) expect_equal(dense.res, original.res) expect_equal(sparse.res, original.res) })) susieR/tests/testthat/test_susie_trendfilter.R0000644000176200001440000001067313542461263021457 0ustar liggesuserscontext("test_susie_trendfilter.R") #check if the susie_trendfilter produces the same results as the original version test_that("susie for trend filtering",{ #order 0 trend filtering with(simulate_tf(0), { original.s0 = susie(X,y,estimate_prior_variance = FALSE) s0 = susie_trendfilter(y,0,estimate_prior_variance = FALSE, standardize = TRUE, use_mad=FALSE) original.s0.ns.ni = susie(X,y,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE) s0.ns.ni = susie_trendfilter(y,0,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE) original.s0.ns = susie(X,y,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE) s0.ns = susie_trendfilter(y,0,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE, use_mad=FALSE) original.s0.ni = susie(X,y,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE) s0.ni = susie_trendfilter(y,0,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE) expect_equal_susie(s0, original.s0) expect_equal_susie(s0.ns.ni, original.s0.ns.ni) expect_equal_susie(s0.ns, original.s0.ns) expect_equal_susie(s0.ni, original.s0.ni) }) #order 1 trend filtering with(simulate_tf(1), { suppressWarnings({ original.s1 <- susie(X,y,estimate_prior_variance = FALSE) s1 <- suppressWarnings(susie_trendfilter(y,1,estimate_prior_variance = FALSE, standardize = TRUE, use_mad=FALSE)) original.s1.ns.ni <- susie(X,y,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE) s1.ns.ni <- suppressWarnings(susie_trendfilter(y,1,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE)) original.s1.ns <- susie(X,y,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE) s1.ns <- suppressWarnings(susie_trendfilter(y,1,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE, use_mad=FALSE)) original.s1.ni <- susie(X,y,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE) s1.ni <- suppressWarnings(susie_trendfilter(y,1,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE)) }) expect_equal_susie(s1, original.s1) expect_equal_susie(s1.ns.ni, original.s1.ns.ni) expect_equal_susie(s1.ns, original.s1.ns) expect_equal_susie(s1.ni, original.s1.ni) }) #order 2 trend filtering with(simulate_tf(2), { original.s2 = susie(X,y,estimate_prior_variance = FALSE) s2 = suppressWarnings(susie_trendfilter(y,2,estimate_prior_variance = FALSE, standardize = TRUE, use_mad=FALSE)) original.s2.ns.ni = susie(X,y,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE) s2.ns.ni = suppressWarnings(susie_trendfilter(y,2,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE)) original.s2.ns = susie(X,y,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE) s2.ns = suppressWarnings(susie_trendfilter(y,2,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE, use_mad=FALSE)) original.s2.ni = susie(X,y,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE) s2.ni = suppressWarnings(susie_trendfilter(y,2,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE)) expect_equal_susie(s2, original.s2) expect_equal_susie(s2.ns.ni, original.s2.ns.ni) expect_equal_susie(s2.ns, original.s2.ns) expect_equal_susie(s2.ni, original.s2.ni) }) }) #check if susie_trendfilter can fit different arguments test_that("susie_trendfilter interface",{ with(simulate_tf(0), { n = length(y) expect_error(susie_trendfilter(y,0,standardize = TRUE, estimate_prior_variance=TRUE, use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize = TRUE, null_weight=1/(n+1),estimate_prior_variance = FALSE, use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize = TRUE, estimate_prior_variance=TRUE, null_weight=1/(n+1), use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize=FALSE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize=FALSE,intercept=TRUE,estimate_prior_variance = FALSE, use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize=TRUE,intercept=FALSE,estimate_prior_variance = FALSE, use_mad=FALSE), NA) expect_error(susie_trendfilter(y,0,standardize = TRUE, compute_univariate_zscore =TRUE,estimate_prior_variance = FALSE, use_mad=FALSE), NA) }) }) susieR/tests/testthat/ER2_rss_lambda0_res.rds0000644000176200001440000000021313607145336020754 0ustar liggesusersb```b`fef`b1|> ft(a{Ǿ; [g`aɩ)榖&ffiƉ橩ɦI))f ,LHXsS1A ̒\ susieR/tests/testthat/Eloglik_original_res.rds0000644000176200001440000000006013607476333021373 0ustar liggesusersb```b`fef`b2ٿwlctsusieR/tests/testthat/vbupdate_original_res.rds0000644000176200001440000000730613607476333021631 0ustar liggesusersy8_!"%E& :(%H"HQDeLB"EzsEBi@I%]ݣs;ϳ=_>k׻{^! `_ߎcPUaD*Ժv/\SsSF${hEUa*k- [h :/xEVoo'Ѻozk업0ޝգO#?4NDpO梻g~N&3RC ΔzjxgB'>xYBgNjQ0Jz@WR3ƻ;=5d N&>:GN/FƒdgZY .;xzenY}>_&0MH{0XyǵG":an%0^iRF*af^2ޒp W9NVigsx>P:*K䳬j9bJ^jq{ưlr!WNS:]e%nCgoJNjT/]ݰ>Y{<^ 3ߣ>hW`nIS&(_Ӓp qG p#ީ݃7<;/_Y{sIh\o σV j@%OT`w ko5` 1z`[mR7P'd zD9$őW8Sd wq{{ї0^QiUSp89z}y.xQ#\<>.}o1jL(~'Ūa5f_^4X Y}c6҅h<+OMQp|/7Z?t}loȇS׳Wx}"_qm0ޝ6]Py&/Ė+TM2ړ8sӷ_0-=]0>x<=TQ%oͿpdñ0z~;V40B0BྫVkpk:|M]/pcllZֈWq v|{y<:;Z(lzOȳj/[g9xX5[t#L_x郛Gߣ?4Kݮ;/uTU .qn\PiN2/Q"Jo[ؕr!TxldKAl0;%}{Ikcc2` qj?{xg7mYPDž- iH"/XX0^6AT\SWT;jw>r8<}"󢌼 Y{ IʫQYRRքx<%moV{6yӗ-ʾlX.GlNV-9/Xy?.7Rd>Rqo?('Ixg^kQ\@=ؗ~.ّ|cgfoaۓh{ߏܱoc _k"zVi݁a#eWr! U?4)npv+X&xzԉc҆oJZ3=b_X;|:^>Mk2:To¨!P^[ոŧ ƻv-~I}3?-ouWo1dS9+y<;oaTǥ.+?>4Ũ^oe^ga?nqSs Ef DFxD{;?] *J^c ;h0Y}QYaXcBw&nٺ}0^ؔQ0L! a[W X9.ww11td_kþɕ!x8J`alvX0|cϖVݺ0^L% z'x^p!;++LygInC7n,r\YYp)4-;HEnd_ o3ods<=+WɦO4o/٧Ư +F}۴:~߳# , M2U<_s`΂W/`pWˍŇ>$ZoWXΔbF [!-q’> :K<_ u[Y,nF;Ί5]] 79lz ka%἞{A5K(>`'%+C^49WrBX4W%KY{|<8zg6 ogMG^Cӭ`h$bN؞ o',g|{+y_~Xi'3˜ 䝍y=NsUV=qEiY{6 6r4ɷYXz,h +YXx;sC=\>A2U&Kw֌ak\51z`'X |. +Ju$?`s l)ƝQn&?NC{ LN?0Zc4DftP?/}|lȩA&ŇmXX/|&VxCbW`5X:U#^XLʸVqk3x>hѸ9V%ZGoQh@pL9*;TEp9.'u&uS^uf8Bc a Rۙ[/v>*>׎lַrm ώ8wGc24eN>irvyF;ot^8|\|Qmޖ{D"[[և5Kc.w;[rVWF3?R\D|†)EWjV)_a+.gݮ)VC ? ^ر M m/xg$Vә|WNggy_Xo;ݜ>8мc7HjQuӕ`>*3}?ܸ\̟dBso篘ѯ;`267303*f]ߛ}?}hvy_Cޭ7ۿXgRZsusieR/tests/testthat/est_V_debug.rds0000644000176200001440000001012113437306365017473 0ustar liggesusersi4Uo"eT*J\3qp c T24P9JW) 4!I#IQQ$=×ۻֽuõvYJ 8 \9\ B)?4VpW8`eEa0k[G8NPq4|A]@KVڱyS7ΓN~o, +5 $;-v@&63O~ΨǀU"qӰ91*\U­ `gv Аo!U}Ҏb Cxk{ Ux%OY?9'rxO*]>^4Dn/xOr=ƛ0[kqc^%kM #sAF>FZ?N-?Q3t㒭y6TAIԫ0[[hMW}rv=G}g6RW֡̐yG oX0g2 SnԿ!m̼ЮFVv_0bSܠv g˺e*^r|ee?">wsnԾW+xȡgilt|7\a-xMNf]-Z5ALG"HOtUkAҭ1S`u)%M1sOנ-y-]qӿo(Fr5=z9@ fMlE:bC=82nK{i/Cѭ;TF d8' >mpmD5=Jg õ1J{7M˖ǎ{{5n?+j*'6&Y^Z;.Y5[k\}!GPuƮm>0rU!6MhJadcHhy; tgJM֣w/U`œ@I laJugHa9'd_B۸-ת~o6l~3hfr!"q/R=e<1So .nj kƵS_a 7Ua~F!dl4 i1&2Bil60_m{vm! :+Y.C1WOB7<5ڭ F)V%H%ۗ݉Rt-(Oh=IUs^vy,,AsFHЮO?_oЮ{C;Da0P1=,7Hƒ@ɞybkwT$U 흼 ,Lj2s$Jyїm1LZm&pp)*٩n让@n:ɫ7}~_# ? % f(2p[ś8c"`+wlmL;!S;EXӫA: Bw~\5E!Oo,4bX3텇~vOю~3f/K âq+zh5vӻsm{uT$Z.ihBVH3cA6!~uwLsp>-.}ՃXnj3<(H&;Ȱ|F2UtK_T^ d ygb RK}9 Gc )ʗߋulM,S=a I@U5`f3GR!\B}NS^1ʉHv2Q6m81ՅH+E}τ^]?_Cq!Ro+:lb,T+!_\J3-Ѱ2QY ߑi)Չے!4ڂ_ǖA‹]w6;ψH'U\p/(p?`ϋAoZc支kFS^)9 Kc]x;؊aīGUNb{䁫w9M4Z L:XTZp|\_X@^Kј0>`T{txzB z>\j4ia@]AE-ʳ_|u-'Qwv֪~3R5Z=$ؒ`ICX̪u10{Q#Y#}m~'xNHXP ro<$K+F<0NmP*B -2}Yx \' -(c/a.0qV]=3ao&czyVm{*0ښ'd3nst |$` }E`H蜀 #1GVc Hk&|@Q0G) })sH1܋)kj !U不Qh˷;(,%:JƁ,6U8 MQ UܧLOEB.+Eꉰ}[fqLkZg+v=Rꙕ1Ԁ~BGf0hwaӋ9v C)<&t?޹6Lx=07>>AW|#O&G%uܕCmfwV3'!8-%V%h?9g›|[!+ze\=&ĝFnbW( |7< |=H'u̙&ˇ/8d ᾔ.*C4QNͰCXggqthI!v,ONoJ/p1a4/uD.xIl oJLo݄5{xKT}q>Ǖ;FtzQ<NʬR;⇐ nѡ|G/Mu#~iU}zM~Evĝ},b3?3@\HZ1HG?0-Jm.G $:o//U3Hq+7_>,hמi^PZR(4!z M*wh|,ӻ1·SzxݛU|jQ aPĪun$c1ߢ<}76B uQ݀-hj/!6.i̽ 65}7b_ KOCGCK?m&+^IHRM@f+z?/g{1 =u^N~[ӑ/BB ,M<%z?DHELj_R}& 툞hǟ̺R;w2!$IJ]]""ڣS*tNl'f!bZpy˚iFicc$zpǕX:Of⿱w])b rC, lWK@旮Ph5M%֧mcPOt~p7VjUm{@]B~{,O1dF4 #Q}D䆿N:S}IVv`&:-Ւs(QSouܧ 1Udܭ:{pi\KÌ15I ٨s$N'V(W_ve3b+Si'}W/^8S.xebbR!@oNc`xpaX)aiUſK).V,Q ;‡u_ł3i_Wjw6y(|KQ:㠊*aX]V_e+{Rwݻr$\JA͈\0+r?,.[ɓg )֜Ėv ݧeo?ؓ |+[V_=ae[SOvr)y|>dG/tjOݠtA~;׏BcA-ӝ~͠a"$#Qo8Lj=\1,#W7/yT"`p?V;zҍ/8X]K՗%l6} z¼u}sfTǩlE9ð|mMt)ƒi+wrݨwGܱgG S]׭Gc@LT/ g3./Ͱ;d@l^Ȉ fqxA7"#mk(ps"_ o4ʏ3N?q1 H%AL|^"\za".?FPI/8M$羫X> 2OaxI fzcϬ_ɮn}>p]SFɊ%pϭAR8#1HV. Kks Bj@Țeݭ@055"t'ZW(9a{To|J֓N"H- MI[6r 2Դo\swuMrlXWrz`=S~+Y ލWD屾)iZ 43EjqF]]jcǹ y |>t{ Q:ZyEِboF<{$ӿ*|W/9\6'6_R.3{vL8*Y6_wDh6}{r܂e,/Ъsć\֞X*]1픙'r}f~,{)*)b jnv3ޙO^Q7[%@5ptȤ:hn3`Q eX]x›9[atnb~4b bb}MKvcf(gs?p1v,ͮ*o4ǍPj5xĥh_/@`둳&?'60?'M' }b~ Z1 #opl ˍp&<'yzW-7'>;Ƴp$yָshW*&B?+38W:4:K?2+Kw[){e)'w*susieR/tests/testthat/test_est_V.R0000644000176200001440000000062513437306365017005 0ustar liggesuserscontext("test_est_V.R") test_that("est_V has V nonzero for the debug case", { debug = readRDS('est_V_debug.rds') betahat = debug$b shat2 = debug$s2 prior_weights = debug$pw V_debug = est_V_uniroot(betahat, shat2, prior_weights) betahat_V0 = rep(0, 512) shat2_V0 = rep(1, 512) V_0 = est_V_uniroot(betahat_V0, shat2_V0, prior_weights) expect_false(V_debug==0) expect_equal(V_0, 0) }) susieR/tests/testthat/Eloglik_rss_lambda1_res.rds0000644000176200001440000000021313607145336021753 0ustar liggesusersb```b`fef`b1|> f f|x O/4o8~IdZ^. }v" V27m0=Έ 5< ona50*1`v6ҷW詃 +NNRk3 ;]x}=lg!>uhuFd}j4:Gq>$8cHѼvƷYV^ ^瑓1aH5OKWu=r$261ax>|xv>u3gtoI^B>[_2ض҇s}~%zֿO Ze[_TϯRl˅_K݅pL9蓾?a=*x͗sꚰw,\o` }ګk9:ֿJTZ}+hK%KwMތ~,;^T+axmϏw3 ;վSwQ{|Nޏ{qj(<-~odpZ _+}> kD3oʧ20ˍM=wٛ R'Y1bՑL-*b-k"n<8_,mŻkLIc2aԴ/[ZS~fڛ};#j^ ΰPE>̾`{O6JאK OհRPwc;f`| 濧oJN-W9W.pdzI,>Q ygoVxC;>QsUOr *-Ɲ_".<~iW^ do8ZG|8&%zN\=lp6_8_F~}P1F!rXYw&ơkN:U{e|_`xU֋sSaxW.\Sa ߅ngEaAzS1 /F<'Vn@oډ~țz7Vw,]qf_U9h|NN3*`x͎]tJ?i3v°K VgY? wZ/̾ h`|=Zu~=XMti!k/1)V{ l<||l}9a75Ȍ/4؞ëk=w_xIOdgޓk{'3(# 9xmI,xtvN\#U~ cFI8w7kj@x(t6/,?=JK\OMCmaGzZ/ˏ +XO4#':ߐ&0Ce8}G>/ci ;5'-[7? 髄dKyCFz-{g&H^֪8_%xhJ( ޓ PxJ |*ΜFpXVEI˓׃omm! 6J9 SpQZJn9) ǗD6 ;zFʈTy0<6YY^H0vnҏXÛk7Uzp;$?fofvIڊ*>3n34V[8,LZ2G^ӻX{ZSa%cEQ- .rv` OlI Jkj)>)ø3ZZxFu.n=S^4Vl1 ٙ_ <붱l58Kݦ^^(<J?ETaqp¼$S6\x (r=Zfa0a&3q0>n"X~7iɓǴpsQM0<3_u]O*`%i4}ڲX+#elúb#^lMk)\~qr`sϼEdZM?09:r?N'f;ߋ]L۷qF8,Oli۸|0狏r p0N 6OŴq0BJW0е¾0=`mKKN|0̾C&gp͓gOc;-ZqlmwɹGk9,=<X>$m&,Hz'}{>Y ϣ[n9ow2Z3U_D̾#Y1wxV cC 18ܬgpnWyO-x饫0zSySIo`M_ ?j\yX3}ӵy#`::qFa1:?aQu 7lsR_~qp;?3̣H7L栨(/qLVoYդpgcdϞ'8qȶfYu\~A0?,:nsN\Gӱte=p% 'UZ\`z #0}L:E8}3 'h?[&4s#ͻ*`l/yC~dOC`U}hN߱B1iOdp{ڽ6̾t'r?Qa~ˠ<|]0*%iWJuۊq|YK\=ǫӯ[` jvKX=ZPYu_z;8=Ju0CʹSt'#P2,F:s(<Zo;̾mӄ:Lσ.:0_F_V)Qi%oK;%Tj݁=O4dG$sql*OOl2R&.^C̬V(t}70>]o'`y~l'oWW٦@z&GЀwtדLG"r@_t;]%`鮟g.W)N?(?O???k?쟯x |uy >%ErZAT\L[F!E|k?,]GsrI7_HyX‹[DGZM)Wz-Xzׁ*`oO`rYP[L9lcBKX"KP0ⶳtqrs0,v9շٗ`#;]%kxnS ̀zCCg-nݞH9vhw1 ]A,F7cv`ȫ{X;3ۏX‘5on̟­C.)䢙*270>\y î,e/"y`xL^92k!98>,v~&S],qDٸ̖f?>Vbi{6%M3sfe`.X >, +a)DJq&7b|pًZ3Q~K,+e/YsKZ_ .-IX9;̽3WYyI"Ӟ KQ106ާg9`,φ)s͞sJFg;NM>ѵBҴ˿ԉf 3?r.f na91#) 7`ΘP]qW^|1;:VN(~prGH ZIYUэXe`,, R`,!mۑi[9 /'/qh",@:cg_=Q-"OIEQ2顂(9/v ).*C/K$frj>R&K\YB'& )ݷc^`pʫa W%:6E}"^[v':*-dr?sgZopJ'#>1`?grsEG6V}#xaYDFq|v-a}w{hg#". $Jօɑ;cSŽ`Y u^ `t}/sKLhx Q/x(bqx&Jsdž`r,u~8>~%09%̇a,759ȝqX~/0+.߀Th4eC!us OLHOivTdޠVƉ\U8̾Yk^r ,|DMJNڣ2"vdVyyv>%,7H&IX y0<օ'`̺OiG5Ӥ {[nv~'`r,ܣ. /so1XLǯwjPx$5nW8/<|GoT|500 +/1rN<f_g|_K}\?N$8lЄ-nڮx/ oh/m1_Y\ 뷰ZRbN[gu'.?@q(8hJXa愝4l}a~|mX=[ƇcN_ [XlqSA/TX{"ʧ1k[MW"3a|V 9 x_h;޽\dHOS哼?p,V7-[뿱 q痑 ^x. xr / 8)~tGzϡV.%ΞظCxU'twũ0<~Ғ aE-憝YK'G`r,MˋOհFRdyƚ;r>O߅x|ʰ0uE'̟H|Ike wޡ^ƃf5m\|ç kom`INϢ%Ĥ_仙cvD#JRt ^*UA]{8~ Nv>`_{dNK鹇놩],9taaHP_ Ai*Kj>o9kX9@f{`x,J{ΓYjX st7,m/,%AWlݐ_ D2nd$?`0mQ֣#>H%?|Ţf |a"ZҌ@}W,G߇Wߪ@gcMqrL=K~\tBDt2׆~0 Ѽbu_U쭭GpOjšlz$nIԗUryEuӸ|:0tk- 4gXԥOuN2z,6)PAsD)JRJ2\|_J-LO9qtvG  uc}'#b{E Mqu҇?%a}εh[ X} fUiN$ oZ Yj۾H.|}0<Q9Ͱx,uz]| Zv~fwkoX<5,_%\8v(l>(֐G=fպ?M&gQ|iRr6nf~=6/-76lt]zKOpzҋw=r\ ]-ܑgw pnlP9yt G'TOd ɴgΌΗ߃ Kw,ccu2>lsX}K}ٕ_8S !3"gNq3l|gJe.}]_3&섵wlɌ,Z&rW$gKr mkJaV,Avuz'~cE[_˜ݜ_|7ztlb3,c9Z'^kk~\/~R;/&]$6vWpԈNb7f}67/{&d-c$vg=-o}͑C zOus0~":6['.?y@jj,.Sf _Oj6n O\uC>`|K8~:..>3$,ߞ9hޙ_EUڣsW&NgaX֯WOS8cl{SpA>0&{&Fq|`3N7,~ΜVOŵ6Xhnz>ד;>bs!R~6a!W/'j ?"9nn:kM PG8}t-}pad^E߹weףzW]X<%^rg~z+qo?%z">ZZ~;叕&?Iu8um8} >~HX! 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/֪⿒53/7;0<}qLεCV8ng#B 4XM#>_w܏#`78uxaxVIN\вwE+qz ou^|g+5}|g]Mvj޵{|ǢqړeWq{E;߆7q5\wMj]>uӧNܓK/ +w.n݁_A&{p]8>0{LYCN3/D`x}pV42wKJY03)tZ[<.70/-4lX|؂ƿ"uq_EWhC0< "6}90<`Qj%>WI8 rſF4 ,TV/g͋cOz1Q"#7 E7 Yxd3#i?Q"f߲ŏeJCZ9_\ado7eK38~djYQr),Jo ,Nhp^]ӧ&_e Jy֔js#r&,Tt_P4o̾q%  c0<o2Q%N`x1eO`a$ϞG\pzeɔŰ|blڰ!\![`RC/wE&\G6ǗHhePnj S6ЅIE|?!WaPPKְ8^k-~-iD<.]Ň`x|{xS~!~䤞=|d  zd4[VcAf8a`x&'`xԕ>zYk$MD|*Zv͋ g_pyM .+~nUKb>ĽmAbWYM[oxꡑ ePxĈkʮ0MxgÄq&qcN8vi8wqzf,v0<Ȩ8"3pzmb0S3(,-D,d??hW^l+?V ^((! n QN_8&0Nқ Ϫu, OtS?x/цw[wSP3*+էn$t퐢5p;-a|qGY<W(y;)mE8ǖO(_L 0S|#秄͓}T[/W:?}Ftυ9%?}'ܿϿϿϿϿϿϿϿϿϿϿϿϿϿϿI?ɵ'8 ϶}'0~ZOܡA}/\7Q7Ѝ h4z#al`@35 tc:?=S?6fsusieR/tests/testthat/test_single_effect_regression_rss.R0000644000176200001440000000243314057454724023652 0ustar liggesuserscontext("test_single_effect_regression_rss.R") test_that("single_effect_regression_rss (lambda = 0) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('singleReg_rss_lambda0_res.rds')$s # the git commit id to previous result is readRDS('singleReg_rss_res.rds')$git_commit_id ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 0 Sigma = update_Sigma(R, s$sigma2, z) s = single_effect_regression_rss(z, Sigma, optimize_V = "none") expect_equal(attr(R, 'd'), rep(1, 500), tolerance=1e-4) expect_equal_SER_suff_stat(s, original.res, tolerance=1e-4) })) test_that("single_effect_regression_rss (lambda = 1) agrees with previous version", with(simulate(200, 500), { original.res = readRDS('singleReg_rss_lambda1_res.rds')$s # the git commit id to previous result is readRDS('singleReg_rss_res.rds')$git_commit_id ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat R = set_R_attributes(R, 1e-08) attr(R, 'lambda') = 1 Sigma = update_Sigma(R, s$sigma2, z) s = single_effect_regression_rss(z, Sigma, optimize_V = "none") expect_equal(attr(R, 'd'), rep(1, 500), tolerance=1e-4) expect_equal_SER_suff_stat(s, original.res, tolerance=1e-4) })) susieR/tests/testthat/test_update_each_effect_rss.R0000644000176200001440000000177114057454504022373 0ustar liggesuserscontext("test_update_each_effect_rss.R") test_that("update_each_effect_rss (lambda = 0) agrees with previous version ", with(simulate(200, 500), { original.res = readRDS("vbupdate_rss_lambda0_res.rds")$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat s$Rz = rep(5, nrow(R)) R = set_R_attributes(R, 1e-08) attr(R, "lambda") = 0 Sigma = update_Sigma(R, s$sigma2, z) res = update_each_effect_rss(R, z, s, Sigma) expect_equal_susie_rss_update(res, original.res, tolerance = 1e-4) })) test_that("update_each_effect_rss (lambda = 1) agrees with previous version ", with(simulate(200, 500), { original.res = readRDS("vbupdate_rss_lambda1_res.rds")$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat s$Rz = rep(5, nrow(R)) R = set_R_attributes(R, 1e-08) attr(R, "lambda") = 1 Sigma = update_Sigma(R, s$sigma2, z) res = update_each_effect_rss(R, z, s, Sigma) expect_equal_susie_rss_update(res, original.res, tolerance = 1e-4) })) susieR/tests/testthat/test_susie_rss.R0000644000176200001440000000202214141046730017723 0ustar liggesuserscontext("test_susie_rss.R") test_that("susie_rss (lambda = 0) agrees with previous version", with(simulate(200, 500),{ skip("susie_rss_lambda is not currently being used") original.res = readRDS('susierssfit_lambda0_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat res = susie_rss_lambda(z, R, lambda = 0, tol=1E-2, check_z = FALSE) skip('susie_rss_lambda is updated.') expect_equal(res$converged, TRUE) expect_equal_susie_rss(res, original.res, tolerance=1e-4) })) test_that("susie_rss (lambda = 1) agrees with previous version", with(simulate(200, 500),{ skip("susie_rss_lambda is not currently being used") original.res = readRDS('susierssfit_lambda1_res.rds')$s ss = univariate_regression(X, y) R = cor(X) z = ss$betahat/ss$sebetahat res = susie_rss_lambda(z, R, lambda = 1, tol=1E-2, check_z = FALSE) skip('susie_rss_lambda is updated.') expect_equal(res$converged, TRUE) expect_equal_susie_rss(res, original.res, tolerance=1e-4) })) susieR/tests/testthat/test_single_effect_regression.R0000644000176200001440000000140113463056206022746 0ustar liggesuserscontext("test_single_effect_regression.R") test_that("single_effect_regression agrees with version 0.3", with(simulate(sparse=T), { original.res = readRDS('singleReg_original_res.rds') V = 0.2 original.res$alpha = as.vector(original.res$alpha) original.res$mu = as.vector(original.res$mu) original.res$mu2 = as.vector(original.res$mu2) original.res$Xr = as.vector(original.res$Xr) original.res$lbf = as.vector(original.res$lbf) scaledX = set_X_attributes(X) scaledX.sparse = set_X_attributes(X.sparse) dense.res = single_effect_regression(y,scaledX,V, optimize_V="none") sparse.res = single_effect_regression(y,scaledX.sparse,V, optimize_V="none") expect_equal_SER(sparse.res, original.res) expect_equal_SER(dense.res, original.res) })) susieR/tests/testthat/test_prior_weights.R0000644000176200001440000000352614057454332020612 0ustar liggesuserscontext("test_prior_weights.R") test_that("prior weights specification agrees with default", with(simulate(200,1000), { res1 = susie(X, y, estimate_prior_variance = TRUE) res2 = susie(X, y, estimate_prior_variance = TRUE, prior_weights = rep(1/ncol(X), ncol(X))) expect_equal_susie(res1,res2,tolerance = 0.1) })) test_that("Sufficient stat (Xty): prior weights specification agrees with default", with(simulate(200,1000), { ss = compute_ss(X, y) res1 = susie_suff_stat(XtX = ss$XtX, Xty = ss$Xty, yty = ss$yty, n = n, estimate_prior_variance = TRUE) res2 = susie_suff_stat(XtX = ss$XtX, Xty = ss$Xty, yty = ss$yty, n = n, estimate_prior_variance = TRUE, prior_weights = rep(1/ncol(ss$XtX), ncol(ss$XtX))) expect_equal_susie_suff_stat(res1,res2,tolerance = 1e-6) })) test_that("Sufficient stat (beta): prior weights specification agrees with default", with(simulate(200,1000), { ss = univariate_regression(X, y) R = cor(X) res1 = susie_suff_stat(bhat = ss$betahat,shat = ss$sebetahat,R = R, n = n,estimate_prior_variance = TRUE) res2 = susie_suff_stat(bhat = ss$betahat, shat = ss$sebetahat, R = R, n = n, estimate_prior_variance = TRUE, prior_weights = rep(1/ncol(R), ncol(R))) expect_equal_susie_suff_stat(res1,res2,tolerance = 1e-6) })) test_that("RSS: prior weights specification agrees with default", with(simulate(200,500), { ss = univariate_regression(X, y) R = cor(X) set.seed(1) # otherwise it might fail because the two results will be slighly different res1 = susie_rss(z = ss$betahat/ss$sebetahat, R = R, estimate_prior_variance = TRUE) res2 = susie_rss(z = ss$betahat/ss$sebetahat, R = R, estimate_prior_variance = TRUE, prior_weights = rep(1/ncol(R), ncol(R))) expect_equal_susie_suff_stat(res1,res2,tolerance = 1e-05) })) 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attr(X.c, 'scaled:center'))/attr(X.c, 'scaled:scale')) expect_equal(crossprod(X.c, orig$Xr), fit$XtXr) })) test_that("Results from sufficient stat interface vs original data: standardize", with(simulate(200,1000), { ss = univariate_regression(X, y) R = cor(X) X.s = set_X_attributes(X, center = FALSE, scale = TRUE) X.s = t((t(X.s) - attr(X.s, 'scaled:center'))/attr(X.s, 'scaled:scale')) X.cs = set_X_attributes(X, center = TRUE, scale = TRUE) X.cs = t((t(X.cs) - attr(X.cs, 'scaled:center'))/attr(X.cs, 'scaled:scale')) expect_warning(orig <- susie(X.s, y, intercept = TRUE, standardize = TRUE, max_iter = 2, estimate_residual_variance=FALSE, estimate_prior_variance = FALSE)) expect_warning(fit <- susie_suff_stat(bhat = ss$betahat, shat = ss$sebetahat, R = R, n = n, var_y = var(y), standardize = TRUE, max_iter = 2, estimate_prior_variance = FALSE, estimate_residual_variance = FALSE)) expect_equal(fit$alpha, orig$alpha) expect_equal(fit$mu, orig$mu) expect_equal(fit$mu2, orig$mu2) expect_equal(fit$V, orig$V) expect_equal(crossprod(X.cs, orig$Xr), fit$XtXr) })) test_that("Results from sufficient stat interface: t statistics", with(simulate(200,1000), { ss = univariate_regression(X, y) R = cor(X) X.s = set_X_attributes(X, center = FALSE, scale = TRUE) X.s = t((t(X.s) - attr(X.s, 'scaled:center'))/attr(X.s, 'scaled:scale')) X.cs = set_X_attributes(X, center = TRUE, scale = TRUE) X.cs = t((t(X.cs) - attr(X.cs, 'scaled:center'))/attr(X.cs, 'scaled:scale')) expect_warning(orig <- susie(X.s, y/sd(y), intercept = TRUE, standardize = TRUE, max_iter = 2, estimate_residual_variance=FALSE, estimate_prior_variance = FALSE)) expect_warning(fit <- susie_suff_stat(bhat = ss$betahat/ss$sebetahat, shat = 1, R = R, n = n, standardize = TRUE, max_iter = 2, estimate_prior_variance = FALSE, estimate_residual_variance = FALSE)) expect_equal(fit$alpha, orig$alpha) expect_equal(fit$mu, orig$mu) expect_equal(fit$mu2, orig$mu2) expect_equal(fit$V, orig$V) expect_equal(crossprod(X.cs, orig$Xr), fit$XtXr) })) susieR/tests/testthat/test_get_pip.R0000644000176200001440000000107713437306365017356 0ustar liggesusers context("test_get_pip.R") test_that("PIP computation is correct in light of null weight and estimated prior",{ res = list(alpha = matrix(c(rep(1,10),rep(2,10),rep(3,10))/10, 3, 10, byrow=T), sets=list(cs_index = c(2)), null_index = 10, V = rep(1,3)) class(res) = 'susie' expect_equal(susie_get_pip(res), rep(0.496, 9)) # expect_equal(susie_get_pip(res, prune_by_cs=T), rep(0.2, 9)) res$V = rep(0,3) expect_equal(susie_get_pip(res), rep(0,9)) res$V = rep(1,3) res$sets$cs_index=NULL expect_equal(susie_get_pip(res,prune_by_cs=T), rep(0,9)) }) susieR/tests/testthat/test_sparse_safe_colScale.R0000644000176200001440000000075013437306365022024 0ustar liggesuserscontext("test_sparse_set_X_attributes.R") test_that("sparse version set_X_attributes", with(simulate(sparse=T), { dense.res = set_X_attributes(X) sparse.res = set_X_attributes(X.sparse) expect_equal(sparse.res, X.sparse, check.attributes=FALSE) expect_equal(attr(dense.res, 'scaled:center'), attr(sparse.res, 'scaled:center')) expect_equal(attr(dense.res, 'scaled:scale'), attr(sparse.res, 'scaled:scale')) expect_equal(attr(dense.res, 'd'), attr(sparse.res, 'd')) })) susieR/tests/testthat/SER_rss_lambda0_res.rds0000644000176200001440000000021213607145336021014 0ustar liggesusersb```b`fef`b1|> fݬ5JYX tzb+zz>h6*,߻3-bPJ3XR ORJ7e}Fe*A1+<cKʛ>Wңñr}ґs}*}}.&zQYl0±=n`ySpOkvN\ >@'Ζ.iv}Rcim5  T>"<.]UTf@Uuw<1?e/&:u+*Zt|܂0dV{I1#mE _-|tY9q"r0x95ODUfqsC3X.Fl;,I@c!5J6hL;#hL{Աƻ^7NƸg(ːiƅJ4P *Y@?{WV(K..=nmmt{Ņu@yyBl6Y 1=o9~DI& ٨9o\Mt=:$ChՁ$F4#;UT;Y{Un/o*?c@ H 8Tʟ]sZ:@b=w?H,OgP}Wb|&̣yDxJH:Hf!;iaSƆ7 7 V-l2@jid޺H+3X`ܭsuH߷-VGOJ#C~!\=y)W8-ynW=n%\oJ/cwW[DP[w>+=J#99mjf.E?*e)q"IjZt57ƩXZHm )j#[ $^F K癮M tX4,&7h HZG/?-cEtSo5aCdc}šk DM͘$GDNݢ)(g|2ms_AGPĚ k7iۻ_¼}^WA$uFP~>ZArx݂A1uaPШF;*#'_|*CG2h&lyds];Ʒ)z@Z7 :Pnf,1 ۏN󡗣@Ye˜/г x?:9ʷ)oƼ"IWW6 z5{5?B҉*`qI^4UN\O,))3zu=o$bGק5} 9q}W0ɂЇD@iJ9w<(}N4؜JWH'±6~vy+vyFԺW[W76ybŷϻvu*HKm6`G X|WޢY$ް$RZ\Dz2b1Ҡ'~B`;艫H[ SʯAOԠ.=uj@xÁ"ލYU`X! 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үcczE4쒠fGwe_K >|\o7q~sx6.O+a2{1/hXЛg"70q`9>h;0ubٚ?ذe a,OA=QV_d}ͮQU=>$.TͅuOqloҞLkS 9pl|Q}7<<ĘѯbijNU2ЅcOc'"Je;c>ݝƹu: 0_i6#]ީ#o-LjiV 5o1'h).+&T?)GoG(7;!mmf/Bs*롧ul{>:yc~g*b##<0zE+{FvvK&F.ǺL2] U- s(O`#7adV#to]q ´ U:0lŠݭ vtVZzyHEtt[g5Fk!uȊƶ<乯cq)loNs\ ̸,>GX3ÌjL4Zz΄%^ Cڰ\ i$j٣)0GkbQ+fwl`42qݚ[mtSR //+PeKyR)аagmnbv6w`t\t%TY?ٮҫ^Fb3njM~,O̶~9Xe l,Ϭ~Vh王4'T3VKÄQK]:۔jKWݱ1ŋXKxLӡw;sfs#[ơ>u0{MW2`Sw/>NQPuϱ^ )ޱ߆qZfI(7(EZFc]&btFˣJ98sv.T%,xc{8dO 8 zN:' hOqޘ}Srbvc;~$Jc\Bt@[p6Dq^1DK\ N(29𞲋;w忸q۞ =  w yCFܿ>˩]7ᎈzG939<3s grwz}; iy7,\rY.e,\rY.so|ϯqO>>I|VUr%P9A _l\㰜㰜㰜㰜㰜㰜㰜㰜㰜㰜㰜㰜㰜㰜v0a/e,\rY.e,\\2Wߎ9>%ƿ$ܜ*"_qX_G[u .,}~%?OX99;!7w ;{o /'d?usrtO?vC~SNks_ q[ 9_~MG:t/;*v9f7[.(ܯ7U'N|_$+.K/<}4g䉳Wz;ع:usۉ1?g_+.2 pqٲeV-Z[9lݢerj9>8xngё&)susieR/tests/testthat/test_update_each_effect.R0000644000176200001440000000100113463056206021463 0ustar liggesuserscontext("test_update_each_effect.R") test_that("update_each_effect agrees with version 0.3", with(simulate(sparse=T), { original.res = readRDS('vbupdate_original_res.rds') original.res$Xr = as.vector(original.res$Xr) scaledX = set_X_attributes(X) scaledX.sparse = set_X_attributes(X.sparse) dense.res = update_each_effect(scaledX,y,s) sparse.res = update_each_effect(scaledX.sparse,y,s) expect_equal_susie_update(sparse.res, original.res) expect_equal_susie_update(dense.res, original.res) })) susieR/tests/testthat/helper_testthat.R0000644000176200001440000002040514141046730020051 0ustar liggesusers# @title sets three attributes for matrix X # @param X an n by p data matrix that can be either a trend filtering # matrix or a regular dense/sparse matrix # @param center boolean indicating centered by column means or not # @param scale boolean indicating scaled by column standard deviations or not # @return X with three attributes e.g. `attr(X, 'scaled:center') is a # p vector of column means of X if center=TRUE, a p vector of zeros # otherwise. 'attr(X, 'scaled:scale') is a p vector of columan standard # deviations of X if scale=TRUE, a p vector of 1s otherwise. 'attr(X, # 'd') is a p vector of column sums of X.standardized^2,' where # X.standardized is the matrix X centered by attr(X, 'scaled:center') # and scaled by attr(X, 'scaled:scale'). # #' @importFrom Matrix rowSums #' @importFrom Matrix colMeans set_X_attributes = function(X, center = TRUE, scale = TRUE) { # if X is a trend filtering matrix if (!is.null(attr(X,"matrix.type"))) { order = attr(X,"order") n = ncol(X) # Set three attributes for X. attr(X,"scaled:center") = compute_tf_cm(order,n) attr(X,"scaled:scale") = compute_tf_csd(order,n) attr(X,"d") = compute_tf_d(order,n,attr(X,"scaled:center"), attr(X,"scaled:scale"),scale,center) if (!center) attr(X,"scaled:center") = rep(0,n) if (!scale) attr(X,"scaled:scale") = rep(1,n) } else { # If X is either a dense or sparse ordinary matrix. # Get column means. cm = colMeans(X,na.rm = TRUE) # Get column standard deviations. csd = compute_colSds(X) # Set sd = 1 when the column has variance 0. csd[csd == 0] = 1 if (!center) cm = rep(0,length = length(cm)) if (!scale) csd = rep(1,length = length(cm)) X.std = (t(X) - cm)/csd # Set three attributes for X. attr(X,"d") = rowSums(X.std * X.std) attr(X,"scaled:center") = cm attr(X,"scaled:scale") = csd } return(X) } create_sparsity_mat = function(sparsity, n, p){ nonzero = round(n*p*(1-sparsity)) nonzero.idx = sample(n*p, nonzero) mat = numeric(n*p) mat[nonzero.idx] = 1 mat = matrix(mat, nrow=n, ncol=p) return(mat) } simulate = function(n=100, p=200, sparse=F) { suppressWarnings(RNGversion("3.5.0")) set.seed(1) beta = rep(0,p) beta[1:4] = 10 if (sparse) { X = create_sparsity_mat(0.99,n,p) X.sparse = as(X,'dgCMatrix') } else { X = matrix(rnorm(n*p,3,4),n,p) X.sparse = NA } y = c(X %*% beta + rnorm(n)) L = 10 residual_variance = 0.8 scaled_prior_variance = 0.2 s = list(alpha=matrix(1/p,nrow=L,ncol=p), mu=matrix(2,nrow=L,ncol=p), mu2=matrix(3,nrow=L,ncol=p), Xr=rep(5,n), KL=rep(1.2,L), sigma2=residual_variance, V=scaled_prior_variance * as.numeric(var(y)), lbf_variable = matrix(0,L,p)) return(list(X=X, X.sparse=X.sparse, s=s, y=y, n=n, p=p, b=beta)) } simulate_tf = function(order){ suppressWarnings(RNGversion("3.5.0")) set.seed(2) n = 50 D = diag(-1, n) for (i in 1:(n-1)){ D[i, i+1] = 1 } if (order==0) { beta = c(rep(0,5),rep(1,5),rep(3,5),rep(-2,5),rep(0,30)) y = beta + rnorm(n) X = solve(D) } else if (order==1) { beta = numeric(n) for (i in 1:n){ if (i <= 5){ beta[i] = 0.001*i + 2 } else if (i <= 15){ beta[i] = 5*0.001*i + 1.6 } else{ beta[i] = 6.1 - 10*0.001*i } } y = beta + rnorm(n) X = solve(D%*%D) } else if (order==2) { beta = numeric(n) for (i in 1:n){ if (i <= 5){ beta[i] = (0.001*i)^2 } else if (i <= 35){ beta[i] = -5*(0.001*i)^2 + 0.06 } else{ beta[i] = 3*(0.001*i)^2 - 3.86 } } y = beta + rnorm(n) X = solve(D%*%D%*%D) } return(list(X=X, y=y)) } expect_equal_susie_update = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal(new.res$alpha, original.res$alpha, scale = 1, tolerance = tolerance) expect_equal(new.res$mu, original.res$mu, scale = 1, tolerance = tolerance) expect_equal(new.res$mu2, original.res$mu2, scale = 1, tolerance = tolerance) expect_equal(new.res$Xr, original.res$Xr, scale = 1, tolerance = tolerance) expect_equal(new.res$KL, original.res$KL, scale = 1, tolerance = tolerance) expect_equal(new.res$sigma2, original.res$sigma2, scale = 1, tolerance = tolerance) expect_equal(new.res$V, original.res$V, scale = 1, tolerance = tolerance) } expect_equal_susie_suff_stat_update = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal(new.res$alpha, original.res$alpha, scale = 1, tolerance = tolerance) expect_equal(new.res$mu, original.res$mu, scale = 1, tolerance = tolerance) expect_equal(new.res$mu2, original.res$mu2, scale = 1, tolerance = tolerance) expect_equal(new.res$XtXr, original.res$XtXr, scale = 1, tolerance = tolerance) expect_equal(new.res$KL, original.res$KL, scale = 1, tolerance = tolerance) expect_equal(new.res$sigma2, original.res$sigma2, scale = 1, tolerance = tolerance) expect_equal(new.res$V, original.res$V, scale = 1, tolerance = tolerance) } expect_equal_susie_rss_update = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal(new.res$alpha, original.res$alpha, scale = 1, tolerance = tolerance) expect_equal(new.res$mu, original.res$mu, scale = 1, tolerance = tolerance) expect_equal(new.res$mu2, original.res$mu2, scale = 1, tolerance = tolerance) expect_equal(new.res$Rz, original.res$Rz, scale = 1, tolerance = tolerance) expect_equal(new.res$KL, original.res$KL, scale = 1, tolerance = tolerance) expect_equal(new.res$sigma2, original.res$sigma2, scale = 1, tolerance = tolerance) expect_equal(new.res$V, original.res$V, scale = 1, tolerance = tolerance) } expect_equal_SER = function(new.res, original.res){ expect_equal(new.res$alpha, original.res$alpha) expect_equal(new.res$mu, original.res$mu) expect_equal(new.res$mu2, original.res$mu2) expect_equal(new.res$lbf, original.res$lbf) expect_equal(new.res$V, original.res$V) expect_equal(new.res$loglik, original.res$loglik) } expect_equal_SER_suff_stat = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal(new.res$alpha, original.res$alpha, scale = 1, tolerance = tolerance) expect_equal(new.res$mu, original.res$mu, scale = 1, tolerance = tolerance) expect_equal(new.res$mu2, original.res$mu2, scale = 1, tolerance = tolerance) expect_equal(new.res$lbf, original.res$lbf, scale = 1, tolerance = tolerance) expect_equal(new.res$V, original.res$V, scale = 1, tolerance = tolerance) expect_equal(new.res$lbf_model, original.res$lbf_model, scale = 1, tolerance = tolerance) } expect_equal_susie = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal_susie_update(new.res, original.res, tolerance = tolerance) expect_equal(new.res$elbo, original.res$elbo, scale = 1, tolerance = tolerance) expect_equal(new.res$niter, original.res$niter, scale = 1, tolerance = tolerance) expect_equal(new.res$intercept, original.res$intercept, scale = 1, tolerance = tolerance) expect_equal(new.res$fitted, original.res$fitted, scale = 1, tolerance = tolerance) expect_equal(new.res$X_column_scale_factors, original.res$X_column_scale_factors, scale = 1, tolerance = tolerance) } expect_equal_susie_suff_stat = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal_susie_suff_stat_update(new.res, original.res, tolerance = tolerance) expect_equal(new.res$elbo, original.res$elbo, scale = 1, tolerance = tolerance) expect_equal(new.res$niter, original.res$niter, scale = 1, tolerance = tolerance) expect_equal(new.res$intercept, original.res$intercept, scale = 1, tolerance = tolerance) expect_equal(new.res$Xtfitted, original.res$Xtfitted, scale = 1, tolerance = tolerance) } expect_equal_susie_rss = function(new.res, original.res, tolerance = .Machine$double.eps^0.5){ expect_equal_susie_rss_update(new.res, original.res, scale = 1, tolerance = tolerance) expect_equal(new.res$elbo, original.res$elbo, scale = 1, tolerance = tolerance) expect_equal(new.res$niter, original.res$niter, scale = 1, tolerance = tolerance) expect_equal(new.res$intercept, original.res$intercept, scale = 1, tolerance = tolerance) expect_equal(new.res$Rz, original.res$Rz, scale = 1, tolerance = tolerance) } susieR/tests/README.md0000644000176200001440000000005613437306365014160 0ustar liggesusersTo run unit tests, ```r devtools::test() ``` susieR/tests/testthat.R0000644000176200001440000000007013313211334014640 0ustar liggesuserslibrary(testthat) library(susieR) test_check("susieR") susieR/vignettes/0000755000176200001440000000000014143477740013547 5ustar liggesuserssusieR/vignettes/finemapping.Rmd0000644000176200001440000001355514112242336016504 0ustar liggesusers--- title: "Fine-mapping example" author: "Gao Wang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Fine-mapping example} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates `susieR` in the context of genetic fine-mapping. We use simulated data of expression level of a gene ($y$) in $N \approx 600$ individuals. We want to identify with the genotype matrix $X_{N\times P}$ ($P=1001$) the genetic variables that causes changes in expression level. The simulated data set is simulated to have exactly 3 non-zero effects. ```{r} library(susieR) set.seed(1) ``` ## The data-set ```{r} data(N3finemapping) attach(N3finemapping) ``` The loaded dataset contains regression data $X$ and $y$, along with some other relevant properties in the context of genetic studies. It also contains the "true" regression coefficent the data is simulated from. Notice that we've simulated 2 sets of $Y$ as 2 simulation replicates. Here we'll focus on the first data-set. ```{r} dim(Y) ``` Here are the 3 "true" signals in the first data-set: ```{r} b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ``` ```{r} which(b != 0) ``` So the underlying causal variables are 403, 653 and 773. ## Simple regression summary statistics `univariate_regression` function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are $\hat{\beta}$ and $SE(\hat{\beta})$ from which z-scores can be derived. Again we focus only on results from the first data-set: ```{r} sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ``` ## Fine-mapping with `susieR` For starters, we assume there are at most 10 causal variables, i.e., set `L = 10`, although SuSiE is robust to the choice of `L`. The `susieR` function call is: ```{r} fitted <- susie(X, Y[,1], L = 10, verbose = TRUE) ``` ### Credible sets By default, we output 95% credible set: ```{r} print(fitted$sets) ``` The 3 causal signals have been captured by the 3 CS reported here. The 3rd CS contains many variables, including the true causal variable `403`. The minimum absolute correlation is 0.86. If we use the default 90% coverage for credible sets, we still capture the 3 signals, but "purity" of the 3rd CS is now 0.91 and size of the CS is also a bit smaller. ```{r} sets <- susie_get_cs(fitted, X = X, coverage = 0.9, min_abs_corr = 0.1) ``` ```{r} print(sets) ``` ### Posterior inclusion probabilities Previously we've determined that summing over 3 single effect regression models is approperate for our application. Here we summarize the variable selection results by posterior inclusion probability (PIP): ```{r} susie_plot(fitted, y="PIP", b=b) ``` The true causal variables are colored red. The 95% CS identified are circled in different colors. Of interest is the cluster around position 400. The true signal is 403 but apparently it does not have the highest PIP. To compare ranking of PIP and original z-score in that CS: ```{r} i <- fitted$sets$cs[[3]] z3 <- cbind(i,z_scores[i],fitted$pip[i]) colnames(z3) <- c('position', 'z-score', 'PIP') z3[order(z3[,2], decreasing = TRUE),] ``` ### Choice of priors Notice that by default SuSiE estimates prior effect size from data. For fine-mapping applications, however, we sometimes have knowledge of SuSiE prior effect size since it is parameterized as percentage of variance explained (PVE) by a non-zero effect, which, in the context of fine-mapping, is related to per-SNP heritability. It is possible to use `scaled_prior_variance` to specify this PVE and explicitly set `estimate_prior_variance=FALSE` to fix the prior effect to given value. In this data-set, SuSiE is robust to choice of priors. Here we set PVE to 0.2, and compare with previous results: ```{r} fitted = susie(X, Y[,1], L = 10, estimate_residual_variance = TRUE, estimate_prior_variance = FALSE, scaled_prior_variance = 0.2) susie_plot(fitted, y='PIP', b=b) ``` which largely remains unchanged. ### A note on covariate adjustment To include covariate `Z` in SuSiE, one approach is to regress it out from both `y` and `X`, and then run SuSiE on the residuals. The code below illustrates the procedure: ```{r, eval=FALSE} remove.covariate.effects <- function (X, Z, y) { # include the intercept term if (any(Z[,1]!=1)) Z = cbind(1, Z) A <- forceSymmetric(crossprod(Z)) SZy <- as.vector(solve(A,c(y %*% Z))) SZX <- as.matrix(solve(A,t(Z) %*% X)) y <- y - c(Z %*% SZy) X <- X - Z %*% SZX return(list(X = X,y = y,SZy = SZy,SZX = SZX)) } out = remove.covariate.effects(X, Z, Y[,1]) fitted_adjusted = susie(out$X, out$y, L = 10) , ``` Note that the covariates `Z` should have a column of ones as the first column. If not, the above function `remove.covariate.effects` will add such a column to `Z` before regressing it out. Data will be centered as a result. Also the scale of data is changed after regressing out `Z`. This introduces some subtleties in terms of interpreting the results. For this reason, we provide covariate adjustment procedure as a tip in the documentation and not part of `susieR::susie()` function. Cautions should be taken when applying this procedure and interpreting the result from it. ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` [N3finemapping]: https://github.com/stephenslab/susieR/blob/master/inst/datafiles/N3finemapping.rds susieR/vignettes/trend_filtering.Rmd0000644000176200001440000001050414051733237017364 0ustar liggesusers--- title: "Trend filtering" author: "Matthew Stephens" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Trend filtering demo} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` # Introduction Although we developed SuSiE primarily with the goal of performing variable selection in highly sparse settings -- and, in particular, for genetic fine-mapping -- the approach also has considerable potential for application to other large-scale regression problems. Here we briefly illustrate this potential by applying it to a non-parametric regression problem that at first sight seems to be ill-suited to our approach. In particular, it does not involve strict sparsity, and the underlying correlation structure of the explanatory variables is very different from the ``blocky" covariance structure of genetic data that SuSiE was designed for. Nonetheless, we will see that SuSiE performs well here despite this (partly due to its ability to capture non-sparse signals via Bayesian Model Averaging). Specifically, consider the non-parametric regression: $$y_t = \mu_t + e_t \quad t=1,\dots,T$$ where the goal is to estimate the underlying mean, $\mu_t$, under the assumption that it varies smoothly (or, more precisely, in a spatially-structured way) with $t$. One very simple way to capture spatial structure in $\mu$ is to model it as a (sparse) linear combination of step functions: $$\mu = Xb$$ where the $j$th column of $X$ is the step function with a step at $j$ ($j = 1,\dots,(T-1)$); that is $x_{tj}=0$ for $t<=j$ and 1 for $t>j$. The $j$th element of $b$ therefore determines the change in the mean $|\mu_j-\mu_{j+1}|$, and an assumption that $b$ is sparse encapsulates an assumption that $\mu$ is spatially structured (indeed, piecewise constant). This very simple approach is essentially 0th-order trend filtering (Tibshirani, 2014, *Annals of Statistics* 42, 285--323). Note that higher-order trend filtering can be similarly implemented using different basis functions; here we use 0th order only for simplicity. # Examples Here we apply SuSiE to perform 0th order trend filtering in some simple simulated examples. We have implemented in `susieR` 0.6.0 a funciton `susie_trendfilter` which internally creates $X$ matrix with step functions in the columns to match input $y$. The algebra have been optimized to work on such trendfiltering matrices. Here we simulate some data where $\mu$ is a step function with four steps, a 0th order trendfiltering problem. The regression is truly sparse and SuSiE should do well. ```{r} library(susieR) set.seed(1) n=1000 mu = c(rep(0,100),rep(1,100),rep(3,100),rep(-2,100),rep(0,600)) y = mu + rnorm(n) s = susie_trendfilter(y, 0, L=10) ``` Plot results: the truth is black, and susie estimate is red. ```{r} plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) s$sigma2 ``` In the next example mu increases linearly. Thus we are approximating a linear function by step functions. Here the truth is not trully ``sparse", so we might expect performance to be poor, but it is not too bad. ```{r} set.seed(1) mu = seq(0,4,length=1000) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) ``` Compare with the genlasso (lasso-based) solution (blue). (This will require installation of the `genlasso` package.) ```{r} library(genlasso) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ``` What happens if we have linear trend plus a sudden change. ```{r} set.seed(1) mu = seq(0,4,length=1000) mu = mu + c(rep(0,500),rep(4,500)) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ``` The two fits seem similar in accuracy. We can check this numerically: ```{r} sqrt(mean((mu-y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)])^2)) sqrt(mean((mu-predict(s))^2)) ``` susieR/vignettes/sparse_susie_eval.Rmd0000644000176200001440000000345113351217671017725 0ustar liggesusers--- title: "Evaluation of sparse version of SuSiE" author: "Kaiqian Zhang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{SuSiE with sparse matrix operations} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` ## Set up environment ```{r, warning=FALSE} library(Matrix) library(susieR) set.seed(1) ``` ## Overview In this vignette, we provide line profiles for revised version SuSiE, which allows for a sparse matrix structure. We compare speed performance when the form of the matrix `X` is dense and sparse. In this minimal example, we observe that given a large sparse matrix, if it is in the dense form, the speed is around `40%` slower than that in a sparse form. ## Simulate data We randomly simulate a `n=1000` by `p=1000` dense matrix and a sparse matrix at sparsity $99\%$, i.e. $99\%$ entries are zeros. ```{r} create_sparsity_mat = function(sparsity, n, p) { nonzero <- round(n*p*(1-sparsity)) nonzero.idx <- sample(n*p, nonzero) mat <- numeric(n*p) mat[nonzero.idx] <- 1 mat <- matrix(mat, nrow=n, ncol=p) return(mat) } ``` ```{r} n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,300,400,1000)] <- 10 X.dense <- create_sparsity_mat(0.99,n,p) X.sparse <- as(X.dense,"dgCMatrix") y <- c(X.dense %*% beta + rnorm(n)) ``` ## `X` in a dense form ```{r} susie.dense <- susie(X.dense,y) ``` ## `X` in a sparse form ```{r} susie.sparse <- susie(X.sparse,y) ``` ## Further step We encourage people who are insterested in improving SuSiE can get insights from those line profiles provided. susieR/vignettes/trendfiltering_derivations.bib0000644000176200001440000000104213542461263021644 0ustar liggesusers@article{Kim07l1trend, author = {Seung-Jean Kim and Kwangmoo Koh and Stephen Boyd and Dimitry Gorinevsky}, title = {$\ell_1$ trend filtering}, journal = "SIAM Review", volume = 51, number = 2, pages = {339--360}, year = {2009}} @article{Tibshirani2014, author = {Ryan J. Tibshirani}, title = {Adaptive Piecewise Polynomial Estimation via Trend Filtering}, year = {2014}, journal = {Annals of Statistics}, volume = {42}, number = {1}, pages = {285--323}} susieR/vignettes/finemapping_summary_statistics.Rmd0000644000176200001440000001002014075324172022522 0ustar liggesusers--- title: "Fine-mapping with summary statistics" author: "Yuxin Zou and Gao Wang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Fine-mapping with summary statistics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates how to use `susieR` with "summary statistics" in the context of genetic fine-mapping. We use the same simulated data as in [fine mapping vignette](finemapping.html). The simulated data is expression level of a gene ($y$) in $N \approx 600$ individuals. We want to identify with the genotype matrix $X_{N\times P}$ ($P=1001$) the genetic variables that causes changes in expression level. The data-set is shipped with `susieR`. It is simulated to have exactly 3 non-zero effects. ```{r} library(susieR) set.seed(1) ``` ## The data-set ```{r} data(N3finemapping) attach(N3finemapping) ``` Notice that we've simulated 2 sets of $Y$ as 2 simulation replicates. Here we'll focus on the first data-set. ```{r} dim(Y) ``` Here are the 3 "true" signals in the first data-set: ```{r} b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ``` ```{r} which(b != 0) ``` So the underlying causal variables are 403, 653 and 773. ## Summary statistics from simple regression Summary statistics of genetic association studies typically contain effect size ($\hat{\beta}$ coefficient from regression), p-value and minor allele frequencies. These information can be used to perform fine-mapping with given an additional input of correlation matrix between variables. The correlation matrix in genetics is typically referred to as LD matrix (LD for linkage disequilibrium). One may use external reference panels to estimate it when this matrix cannot be obtained from samples directly. Caution that LD matrix here has to be correlation matrix $r$, not $r^2$ or $abs(r)$. `univariate_regression` function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are $\hat{\beta}$ and $SE(\hat{\beta})$ from which z-scores can be derived. Alternatively you can obtain z-scores from $\hat{\beta}$ and p-values if you are provided with those information. Again we focus only on results from the first data-set: ```{r} sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ``` For this example the correlation matrix can be computed directly from data provide, ```{r} R <- cor(X) ``` ## Fine-mapping with `susieR` using summary statistics For starters, we assume there are at most 10 causal variables, i.e. set `L = 10`, although SuSiE is generally robust to the choice of `L`. SuSiE regression with summary statistics is implemented as `susie_rss` function, ```{r} fitted_rss <- susie_rss(z_scores, R, L = 10) ``` Using `summary` function, we can examine the posterior inclusion probability (PIP) for each variable, and the 95% credible sets. Here, we are the 95% credible sets. ```{r} summary(fitted_rss)$cs ``` The 3 causal signals have been captured by the 3 CS reported here. The 3rd CS contains many variables, including the true causal variable 403. We can also plot the posterior inclusion probability (PIP), ```{r} susie_plot(fitted_rss, y="PIP", b=b) ``` The true causal variables are colored red. The 95% CS identified are circled in different colors. The result should be very similar to using the individual level data for this example, as the z-scores and correlation matrix (LD reference) come from the same genotype data-set. ```{r, fig.width=6,fig.height=6} fitted = susie(X, Y[,1], L = 10) plot(fitted$pip, fitted_rss$pip, ylim=c(0,1)) ``` ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/vignettes/mwe.Rmd0000644000176200001440000000237614033741524015003 0ustar liggesusers--- title: "A minimal example" author: "Matthew Stephens" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{minimal example} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` In this short vignette, we fit a sparse linear regression model with up to $L > 0$ non-zero effects. Generally, there is no harm in over-stating $L$ (that is, the method is pretty robust to overfitting), except that computation will grow as $L$ grows. Here is a minimal example: ```{r} library(susieR) set.seed(1) n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,2,300,400)] <- 1 X <- matrix(rnorm(n*p),nrow=n,ncol=p) y <- X %*% beta + rnorm(n) res <- susie(X,y,L=10) plot(coef(res)[-1],pch = 20) ``` Plot the ground-truth outcomes vs. the predicted outcomes: ```{r fig.height=3.5, fig.width=3.5} plot(y,predict(res),pch = 20) ``` ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/vignettes/trendfiltering_derivations.Rnw0000644000176200001440000002761114051613310021654 0ustar liggesusers\documentclass[11pt]{article} \usepackage{Sweave} \usepackage{fullpage} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{bm} \usepackage{mathabx} \usepackage{xspace} \usepackage{natbib} \bibliographystyle{abbrvnat} \newcommand{\susie}{\textsl{SuSiE}\xspace} \def\vb{\bm b} \def\vy{\bm y} \def\vx{\bm x} \begin{document} \title{Implementation of SuSiE trend filtering} \author{Kaiqian Zhang} \maketitle %\VignetteIndexEntry{Implementation of SuSiE trend filtering} \section{Notation} We first describe the notation used in this text. We denote matrices by boldface uppercase letters ($\mathbf{A}$), vectors are denoted by boldface lowercase letters ($\mathbf{a}$), and scalars are denoted by non-boldface letters ($a$ or $A$). All vectors are column-vectors. Lowercase letters may represent elements of a vector or matrix if they have subscripts. For example, $a_{ij}$ is the $(i,j)$th element of $\mathbf{A}$, $a_i$ is the $i$th element of $\mathbf{a}$, and $\mathbf{a}_{i}$ is either the $i$th row or $i$th column of $\mathbf{A}$. For indexing, we will generally use capital non-boldface letters to denote the total number of elements and their lowercase non-boldface versions to denote the index. For example, $i = 1,\ldots,I$. We let $\mathbf{A}_{n \times p}$ denote that $\mathbf{A} \in \mathbb{R}^{n \times p}$. We denote the matrix transpose by $\mathbf{A}^T$, the matrix inverse by $\mathbf{A}^{-1}$, and the matrix determinant by $\det(\mathbf{A})$. Finally, sets will be denoted by calligraphic letters ($\mathcal{A}$). \section{Overview} Trend filtering is a useful statistical tool for nonparametric regression. \cite{Kim07l1trend} first proposed $\ell_1$ trend filtering for estimating underlying piecewise linear trends in time series data. This idea can be further extended to fit piecewise polynomial of degree $k$ to the data. In their paper, Kim et al. showed the equivalence between the $\ell_1$ trend filtering and the $\ell_1$-regularized least squares problem. This motivates us to think about the connection between trend filtering and sparse approximation in general. \section{Trend filtering and sparse regression} Trend filtering problem is defined mathematically as follows. For a given integer $k \geq 0$, the kth order trend filtering is defined by a penalized least squares optimization problem, \begin{align} \hat{\vb} = \underset{\vb}{\mathrm{argmin}} \frac{1}{2}|| \vy - \vb||_2^2 + \frac{n^k}{k!}\lambda||D_{k+1}\vb||_1, \end{align} where $\vy = [y_1 \dots y_n]^T$ is an n vector of observations, $\lambda$ is a tuning parameter, and $D_{k+1}$ is the discrete difference operator of order $k$. When order $k=0$, $D$ is defined \begin{align}\label{D1} D_{1} = \begin{bmatrix} -1 & 1 & 0 & \dots & 0 & 0\\ 0 & -1 & 1 & \dots & 0 & 0\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & 1\\ \end{bmatrix} \in \mathbb{R}^{(n-1)\times n}. \end{align} In this case, the components of the trend filtering estimate form a piecewise constant structure, with break points corresponding to the nonzero entries of $D_{1}\hat{\vb} = (\hat{b}_2 - \hat{b}_1, \dots, \hat{b}_n - \hat{b}_{n-1})$ \citep{Tibshirani2014}. And when $k\geq 1$, the operator $D_{k+1}$ is defined recursively, \begin{align}\label{D1_2} D_{k+1} = D_{1} \cdot D_{k} \in \mathbb{R}^{(n-k-1)\times n}, \end{align} where the dot product is matrix multiplication. Notice that $D_1$ here in \ref{D1_2} is the $(n-k-1)\times (n-k)$ version of $D_1$ described in \ref{D1}. Now we want to transform the trend filtering problem into a sparse regression problem. Let $\bm{\beta}=D_{k+1}\vb$. Then if $D_{k+1}$ were invertibe, we could write $\vb = (D_{k+1})^{-1}\bm{\beta}$ and the above problem would become \begin{align} \hat{\bm{\beta}} = \underset{\bm{\beta}}{\mathrm{argmin}} \frac{1}{2}||\vy - (D_{k+1})^{-1}\bm{\beta}||_2^2 + \frac{n^k}{k!}\lambda||\bm{\beta}||_1. \end{align} We can consider this a sparse regression with $\ell_1$ regularization problem, where the design matrix is $X_{k+1} = (D_{k+1})^{-1}$. \section{Modification of $D$} As we have seen, the trend filtering problem becomes a sparse regression with $\ell_1$ regularization if we consider the design matrix $X_{k+1} = (D_{k+1})^{-1}$. However, $D_{1} \in \mathbb{R}^{(n-1)\times n}$ is not invertible, so is $D_{k+1}$ for $k=1,2,\dots$ By observation, we complete $D_{1}$ as a square and symmetric matrix \begin{align} \hat{D}_{1} = \begin{bmatrix} -1 & 1 & 0 & \dots & 0 & 0\\ 0 & -1 & 1 & \dots & 0 & 0\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & 1\\ 0 & 0 & 0 & \dots & 0 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}. \end{align} And for $k\geq 1$, we obtain \begin{align} \hat{D}_{k+1} = \hat{D}_{1} \cdot \hat{D}_{k} \in \mathbb{R}^{n\times n}. \end{align} We notice that, by this modification, $\hat{D}_{k+1}$ has $k$ more rows added at the bottom without changing any previous entry. With this modification, we are able to invert $\hat{D}_{k+1}$ and consider the inverse matrix as a design matrix in the sparse regression. \section{Special structure of $\hat{D}^{-1}$} After determining the design matrix $X$ in the sparse regression problem, we could apply \susie algorithm to help us find a possible fit. Here, we denote $X_{k+1} = (\hat{D}_{k+1})^{-1}$, where $k$ is the order of trend filtering. Rather than generating $\hat{D}^{-1}$ and set this as an $X$ input, we exploit the special structure of $\hat{D}^{-1}$ and perform \susie on this specific trend filtering problem with $O(n)$ complexity. We will talk about how to make different computations linear in complexity by utilizing the special structure respectively. \section{Computation of $Xb$} \label{computation-on-Xb} In the trend filtering application, since $X_{k+1} = (\hat{D}_{k+1})^{-1}$, we obtain \begin{align} X_{k+1} \vb & = (\hat{D}_{k+1})^{-1} \vb = (\underbrace{\hat{D}_{1}\dots \hat{D}_{1}}_{k+1})^{-1} \vb \\ &= \underbrace{(\hat{D}_{1})^{-1} \dots (\hat{D}_{1})^{-1}}_{k+1} \vb \\ &= \underbrace{X_1 \dots X_1}_{k+1} \vb. \end{align} We notice that since \begin{align} X_1 = \begin{bmatrix} -1 & -1 & -1 & \dots & -1 & -1\\ 0 & -1 & -1 & \dots & -1 & -1\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & -1\\ 0 & 0 & 0 & \dots & 0 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_1 \vb & = -1 \cdot [b_1+b_2+\dots+b_n, b_2+\dots+b_n, \dots, b_{n-1}+b_n, b_n]^T \\ & = -1 \cdot \text{cumsum}(\text{reverse}(\vb)) . \end{align} Let $f: \mathbb{R}^n \to \mathbb{R}^n$ such that $f(\vx)= -\text{cumsum}(\text{reverse}(\vx))$ for any $\vx \in \mathbb{R}^n$. Then \begin{align} X_{k+1} \vb & = \underbrace{X_1 \dots X_1}_{k+1} \vb = f^{(k+1)}(\vb), \end{align} where $k$ is the order of trend filtering. \section{Computation of $X^Ty$} \label{computation-on-Xty} We consider $X_{k+1}^T\vy$. Here $X_{k+1} = (\hat{D}_{k+1})^{-1}$ in the trend filtering problem, and $\vy$ is an n vector. We have \begin{align} X_{k+1}^T \vy & = ((\hat{D}_{k+1})^{-1})^T \vy = ((\underbrace{\hat{D}_{1}\dots \hat{D}_{1}}_{k+1})^{-1})^T \vy \\ & = \underbrace{((\hat{D}_{1})^{-1})^T \dots ((\hat{D}_{1})^{-1})^T}_{k+1} \vy \\ & = \underbrace{X_1^T \dots X_1^T}_{k+1} \vy. \end{align} Similarly, we observe that since \begin{align} X_1^T = \begin{bmatrix} -1 & 0 & 0 & \dots & 0 & 0\\ -1 & -1 & 0 & \dots & 0 & 0\\ \vdots & \ddots & \\ -1 & -1 & -1 & \dots & -1 & 0\\ -1 & -1 & -1 & \dots & -1 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_1^T \vy & = -1 \cdot [y_1, y_1+y_2, \dots, y_1+y_2+\dots+y_n]^T \\ & = -1 \cdot \text{cumsum}(\vy). \end{align} Let $g: \mathbb{R}^n \to \mathbb{R}^n$ such that $g(\vx)= -\text{cumsum}(\vx)$ for any $\vx \in \mathbb{R}^n$. Then \begin{align} X_{k+1}^T \vy & = \underbrace{X_1^T \dots X_1^T}_{k+1} \vy = g^{(k+1)}(\vy), \end{align} where $k$ is the order of trend filtering. \section{Computation on $(X^2)^T 1$; i.e., colSums($X^2$)} \label{computation-on-d} To compute $(X_{k+1}^2)^T \bm{1}$, let's first explore the special structure of $X_{k+1}=(\hat{D}^{(k+1)})^{-1}$ for $k=0,1,2$. \begin{align} X_1 = \begin{bmatrix} -1 & -1 & -1 & -1 & -1 & \dots \\ 0 & -1 & -1 & -1 & -1 & \dots \\ 0 & 0 & -1 & -1 & -1 & \dots \\ 0 & 0 & 0 & -1 & -1 & \dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_2 = \begin{bmatrix} 1 & 2 & 3 & 4 & 5 & 6 & \dots \\ 0 & 1 & 2 & 3 & 4 & 5 & \dots \\ 0 & 0 & 1 & 2 & 3 & 4 &\dots \\ 0 & 0 & 0 & 1 & 2 & 3 &\dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_3 = \begin{bmatrix} -1 & -3 & -6 & -10 & -15 & \dots \\ 0 & -1 & -3 & -6 & -10 & \dots \\ 0 & 0 & -1 & -3 & -6 & \dots \\ 0 & 0 & 0 & -1 & -3 & \dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} Define a triangular rotate matrix $Q \in \mathbb{R}^{n\times n}$ such that (i) For any $i, j \leq n$, $Q_{ij} = 0$ if $i>j$. (ii) For any $k < n$, $Q_{ab} = Q_{cd}$ if $b-a = d-c = k$. We observe that if $X$ is a triangular rotate matrix, then \begin{align} X^T \bm{1} = \text{cumsum}(X_{1.}). \end{align} Since $X^2$ is still a triangular rotate matrix, we obtain \begin{align} (X^2)^T \bm{1} = \text{cumsum}(X^2_{1.}). \end{align} Since $X_{k+1} = (\hat{D}_{k+1})^{-1}$ is a triangular rotate matrix, \begin{align} (X_{k+1}^2)^T \bm{1} = \text{cumsum}((X_{k+1})_{1.}^2). \end{align} And, obviously, the first row of $X_{k+1}$ is \begin{equation} (X_{k+1})_{1.} = \begin{cases} \bm{-1} & \text{if } k = 0 \\ g^{(k)}(\bm{1}) & \text{if } k > 0. \end{cases} \end{equation} \section{Computation of $\mu$; i.e., column means} \label{computation-on-cm} For each column $j = 1,2,\dots,n$, \begin{equation} \mu_j = E[X_{.j}], \end{equation} and $\bm{\mu} = [\mu_1, \mu_2, \dots, \mu_n]^T \in \mathbb{R}^n$. Hence we get \begin{equation} \bm{\mu} = \frac{1}{n} X_{k+1}^T \bm{1} = \frac{1}{n} \text{cumsum}((X_{k+1})_{1.}), \end{equation} where $(X_{k+1})_{1.}$ is defined above in \ref{computation-on-d}. \section{Computation of $\sigma$; i.e., column standard deviations} \label{computation-on-csd} For each column $j=1,2,\dots, n$, \begin{align} \sigma_j & = \sqrt{E[X_{.j}^2] - E[X_{.j}]^2} \\ & = \sqrt{\frac{1}{n}\sum_{i=1}^{n}X_{ij}^2 - (\frac{1}{n}\sum_{i=1}^{n} X_{ij})^2}. \end{align} Hence, $\bm{\sigma} = [\sigma_1, \sigma_2, \dots, \sigma_n]^T \in \mathbb{R}^n$ becomes \begin{align} \bm{\sigma} & = \sqrt{E[X^2] - E[X]^2} \\ & = \sqrt{\frac{1}{n}\text{colSums}(X^2) - (\frac{1}{n}\text{colSums}(X))^2} \\ & = \sqrt{\frac{1}{n}(X^2)^T \bm{1} + (\frac{1}{n} X^T \bm{1})^2}, \end{align} where the first term involves \ref{computation-on-d} and the second term is computed in \ref{computation-on-cm}. Note that in the algorithm, we set the column standard deviation 1 when the column has variance 0 for computation convenience. \section{Computation on $(\hat{X}^2)^T 1$; i.e., colSums($\hat{X}^2$)} \label{computation-on-std-d} We want to compute colSums($\hat{X}^2$), where $\hat{X}$ is scaled by both column means $\bm{\mu} = [\mu_1, \mu_2, \ldots, \mu_n]^T \in \mathbb{R}^n$ and column standard deviations $\bm{\sigma} = [\sigma_1, \sigma_2, \dots, \sigma_n]^T \in \mathbb{R}^n$. We define $\hat{X} \in \mathbb{R}^{n \times n}$ such that for each $i = 1,2,\ldots,n$ and $j=1,2,\dots,n$, \begin{equation*} \hat{X}_{ij} = (X_{ij} - \mu_j)/\sigma_j, \end{equation*} where $X = X_{k+1} = (\hat{D}_{k+1})^{-1}$ if the order is $k$. \section{Conclusion} Computation details from section \ref{computation-on-Xb} to section \ref{computation-on-std-d} explain how we can benefit from the unique structure of matrices from trend filtering problem. As shown by our formula, we do not need to form any matrix and complete \susie algorithm with $O(n)$ complexity. \bibliography{trendfiltering_derivations} \end{document} susieR/vignettes/l0_initialization.Rmd0000644000176200001440000000376313350763557017651 0ustar liggesusers--- title: "SuSiE with L0Learn initialization example" author: "Kaiqian Zhang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{L0Learn initialization demo} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#") ``` `susieR` allows for a customized initialization. In this vignette we deomonstrate how to use [`L0Learn`](https://github.com/hazimehh/L0Learn) fit to initialize susieR. ```{r, warning=FALSE} library(susieR) library(L0Learn) ``` ## Simulate data First, we simulate data from the minimal example. ```{r} set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[c(1,2,300,400)] = 1 X = matrix(rnorm(n*p),nrow=n,ncol=p) y = X %*% beta + rnorm(n) ``` ## Fit L0Learn We start with fitting a L0-regularized model to the simulated data. ```{r} set.seed(1) L0fit = L0Learn.cvfit(X, y, penalty = "L0") ``` Let's choose the penalty strength parameter that minimizes the cross-validation error. ```{r} lambdaIndex = which.min(L0fit$cvMeans[[1]]) L0coef = as.numeric(coef(L0fit$fit, lambda = L0fit$fit$lambda[[1]][lambdaIndex])) effect.beta = L0coef[which(L0coef!=0)][-1] effect.index = (which(L0coef!=0)-1)[-1] length(effect.beta) effect.beta[1:10] effect.index[1:10] ``` The L0Learn model finds `r length(effect.beta)` effects, which will be used to initialize susie. ## Build an initialization object We create an initialization from `l0learn` coefficients using `susie_init_coef` function, ```{r} set.seed(1) s.init = susie_init_coef(effect.index, effect.beta, p) ``` ## Run susieR with initialization Now, we use `effect.index` and `effect.beta` obtained from L0Learn fit to create an `s.init` object. We then run susie with this initialization. ```{r} susieL0.fit = susie(X,y,s_init=s.init) susieL0.fit$sets$cs ``` ## References 1. Hussein Hazimeh and Rahul Mazumder. (2018). Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms. susieR/vignettes/susie_refine.Rmd0000644000176200001440000000531214113171573016664 0ustar liggesusers--- title: "Refine SuSiE model" author: "Yuxin Zou" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Refine SuSiE model} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` In this vignette, we demonstrate a procedure that helps SuSiE get out of local optimum. We simulate phenotype using UK Biobank genotypes from 50,000 individuals. There are 1001 SNPs. It is simulated to have exactly 2 non-zero effects at 234, 287. ```{r} library(susieR) library(curl) data_file <- tempfile(fileext = ".RData") data_url <- paste0("https://raw.githubusercontent.com/stephenslab/susieR/", "master/inst/datafiles/FinemappingConvergence1k.RData") curl_download(data_url,data_file) load(data_file) b <- FinemappingConvergence$true_coef susie_plot(FinemappingConvergence$z, y = "z", b=b) ``` The strongest marginal association is a non-effect SNP. Since the sample size is large, we use sufficient statistics ($X^\intercal X, X^\intercal y, y^\intercal y$ and sample size $n$) to fit susie model. It identifies 2 Credible Sets, one of them is false positive. This is because `susieR` get stuck around a local minimum. ```{r} fitted <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n)) susie_plot(fitted, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted),2))) ``` Our refine procedure to get out of local optimum is 1. fit a susie model, $s$ (suppose it has $K$ CSs). 2. for CS in $s$, set SNPs in CS to have prior weight 0, fit susie model --> we have K susie models: $t_1, \cdots, t_K$. 3. for each $k = 1, \cdots, K$, fit susie with initialization at $t_k$ ($\alpha, \mu, \mu^2$) --> $s_k$ 4. if $\max_k \text{elbo}(s_k) > \text{elbo}(s)$, set $s = s_{kmax}$ where $kmax = \arg_k \max \text{elbo}(s_k)$ and go to step 2; if no, break. We fit susie model with above procedure by setting `refine = TRUE`. ```{r} fitted_refine <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n, refine=TRUE)) susie_plot(fitted_refine, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted_refine),2))) ``` With the refine procedure, it identifies 2 CSs with the true signals, and the achieved evidence lower bound (ELBO) is higher. ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/vignettes/susierss_diagnostic.Rmd0000644000176200001440000001050614142575215020274 0ustar liggesusers--- title: "Diagnostic for fine-mapping with summary statistics" author: "Yuxin Zou" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Diagnostic for fine-mapping with summary statistics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates diagnostic plot for consistency between summary statistics and reference LD matrix. The `susie_rss` assumes the LD matrix accurately estimate the correlations among SNPs from the original GWAS genotype data. Typically, the LD matrix comes from some public database of genotypes in a suitable reference population. The inaccurate LD information leads to unreliable fine-mapping result. The diagnostic for consistency between summary statistics and refenrence LD matrix is based on the RSS model under the null with regularized LD matrix. $$ \hat{z} | R, s \sim N(0, (1-s)R + s I), 0 1 | residual_variance < 0)) stop("Residual variance should be a scaler between 0 and 1") if (is.null(residual_variance)) residual_variance = 1 if (is.null(prior_weights)){ prior_weights = rep(1/p,p) }else{ if(all(prior_weights == 0)){ stop("Prior weight should greater than 0 for at least one variable.") } prior_weights = prior_weights / sum(prior_weights) } if(length(prior_weights) != p) stop("Prior weights must have length p") if (p < L) L = p s = list(alpha = matrix(1/p,nrow = L,ncol = p), mu = matrix(0,nrow = L,ncol = p), mu2 = matrix(0,nrow = L,ncol = p), Rz = rep(0,p), KL = rep(as.numeric(NA),L), lbf = rep(as.numeric(NA),L), lbf_variable = matrix(as.numeric(NA),L,p), sigma2 = residual_variance, V = prior_variance, pi = prior_weights) if (is.null(null_weight)) s$null_index = 0 else s$null_index = p class(s) = "susie" return(s) } # Update a susie fit object in order to initialize susie model. init_finalize_rss = function (s, R = NULL, Rz = NULL) { if(length(s$V) == 1) s$V = rep(s$V,nrow(s$alpha)) # Check sigma2. if (!is.numeric(s$sigma2)) stop("Input residual variance sigma2 must be numeric") # Avoid problems with dimension if input is a 1 x 1 matrix. s$sigma2 = as.numeric(s$sigma2) if (length(s$sigma2) != 1) stop("Input residual variance sigma2 must be a scalar") if (s$sigma2 <= 0) stop("residual variance sigma2 must be positive (is your var(y) zero?)") # Check prior variance. if (!is.numeric(s$V)) stop("Input prior variance must be numeric") if (!all(s$V >= 0)) stop("prior variance must be non-negative") if (!all(dim(s$mu) == dim(s$mu2))) stop("dimension of mu and mu2 in input object do not match") if (!all(dim(s$mu) == dim(s$alpha))) stop("dimension of mu and alpha in input object do not match") if (nrow(s$alpha) != length(s$V)) stop("Input prior variance V must have length of nrow of alpha in ", "input object") # Update Rz. if (!missing(Rz)) s$Rz = Rz if (!missing(R)) s$Rz = compute_Xb(R,colSums(s$mu * s$alpha)) # Reset KL and lbf. s$KL = rep(as.numeric(NA),nrow(s$alpha)) s$lbf = rep(as.numeric(NA),nrow(s$alpha)) class(s) = "susie" return(s) } susieR/R/predict.susie.R0000644000176200001440000000372614051345167014647 0ustar liggesusers#' @title Extract regression coefficients from susie fit #' #' @param object A susie fit. #' #' @param \dots Additional arguments passed to the generic \code{coef} #' method. #' #' @return A p+1 vector, the first element being an intercept, and the #' remaining p elements being estimated regression coefficients. #' #' @importFrom stats coef #' #' @method coef susie #' #' @export coef.susie #' #' @export #' coef.susie = function (object, ...) { s = object return(c(s$intercept,colSums(s$alpha*s$mu)/s$X_column_scale_factors)) } #' @title Predict outcomes or extract coefficients from susie fit. #' #' @param object A susie fit. #' #' @param newx A new value for X at which to do predictions. #' #' @param type The type of output. For \code{type = "response"}, #' predicted or fitted outcomes are returned; for \code{type = #' "coefficients"}, the estimated coefficients are returned. #' #' @param \dots Other arguments used by generic predict function. These #' extra arguments are not used here. #' #' @return For \code{type = "response"}, predicted or fitted outcomes #' are returned; for \code{type = "coefficients"}, the estimated #' coefficients are returned. If the susie fit has intercept = #' \code{NA} (which is common when using \code{susie_suff_stat}) then #' predictions are computed using an intercept of 0, and a warning is #' emitted. #' #' @importFrom stats coef #' #' @method predict susie #' #' @export predict.susie #' #' @export #' predict.susie = function (object, newx = NULL, type = c("response","coefficients"), ...) { s = object type = match.arg(type) if (type == "coefficients") { if (!missing(newx)) stop("Do not supply newx when predicting coefficients") return(coef(s)) } if (missing(newx)) return(s$fitted) if (is.na(s$intercept)) { warning("The prediction assumes intercept = 0") return(drop(newx %*% coef(s)[-1])) } else return(drop(s$intercept + newx %*% coef(s)[-1])) } susieR/R/initialize.R0000644000176200001440000001023414104273074014213 0ustar liggesusers#' @title Initialize a susie object using regression coefficients #' #' @param coef_index An L-vector containing the the indices of the #' nonzero coefficients. #' #' @param coef_value An L-vector containing initial coefficient #' estimates. #' #' @param p A scalar giving the number of variables. #' #' @return A list with elements \code{alpha}, \code{mu} and \code{mu2} #' to be used by \code{susie}. #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[sample(1:1000,4)] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' #' # Initialize susie to ground-truth coefficients. #' s = susie_init_coef(which(beta != 0),beta[beta != 0],length(beta)) #' res = susie(X,y,L = 10,s_init=s) #' #' @export #' susie_init_coef = function (coef_index, coef_value, p) { L = length(coef_index) if (L <= 0) stop("Need at least one non-zero effect") if (!all(coef_value != 0)) stop("Input coef_value must be non-zero for all its elements") if (L != length(coef_value)) stop("Inputs coef_index and coef_value must of the same length") if (max(coef_index) > p) stop("Input coef_index exceeds the boundary of p") alpha = matrix(0,nrow = L,ncol = p) mu = matrix(0,nrow = L,ncol = p) for(i in 1:L){ alpha[i,coef_index[i]] = 1 mu[i,coef_index[i]] = coef_value[i] } out = list(alpha = alpha,mu = mu,mu2 = mu*mu) class(out) = c("susie","list") return(out) } # Set default susie initialization. init_setup = function (n, p, L, scaled_prior_variance, residual_variance, prior_weights, null_weight, varY, standardize) { if (!is.numeric(scaled_prior_variance) || scaled_prior_variance < 0) stop("Scaled prior variance should be positive number") if (scaled_prior_variance > 1 && standardize) stop("Scaled prior variance should be no greater than 1 when ", "standardize = TRUE") if(is.null(residual_variance)) residual_variance = varY if(is.null(prior_weights)){ prior_weights = rep(1/p,p) }else{ if(all(prior_weights == 0)){ stop("Prior weight should greater than 0 for at least one variable.") } prior_weights = prior_weights / sum(prior_weights) } if(length(prior_weights) != p) stop("Prior weights must have length p") if (p < L) L = p s = list(alpha = matrix(1/p,nrow = L,ncol = p), mu = matrix(0,nrow = L,ncol = p), mu2 = matrix(0,nrow = L,ncol = p), Xr = rep(0,n), KL = rep(as.numeric(NA),L), lbf = rep(as.numeric(NA),L), lbf_variable = matrix(as.numeric(NA),L,p), sigma2 = residual_variance, V = scaled_prior_variance*varY, pi = prior_weights) if (is.null(null_weight)) s$null_index = 0 else s$null_index = p class(s) = "susie" return(s) } # Update a susie fit object in order to initialize susie model. init_finalize = function (s, X = NULL, Xr = NULL) { if(length(s$V) == 1) s$V = rep(s$V,nrow(s$alpha)) # Check sigma2. if (!is.numeric(s$sigma2)) stop("Input residual variance sigma2 must be numeric") # Avoid problems with dimension if input is a 1 x 1 matrix. s$sigma2 = as.numeric(s$sigma2) if (length(s$sigma2) != 1) stop("Input residual variance sigma2 must be a scalar") if (s$sigma2 <= 0) stop("Residual variance sigma2 must be positive (is your var(Y) zero?)") # check prior variance if (!is.numeric(s$V)) stop("Input prior variance must be numeric") if (!all(s$V >= 0)) stop("prior variance must be non-negative") if (!all(dim(s$mu) == dim(s$mu2))) stop("dimension of mu and mu2 in input object do not match") if (!all(dim(s$mu) == dim(s$alpha))) stop("dimension of mu and alpha in input object do not match") if (nrow(s$alpha) != length(s$V)) stop("Input prior variance V must have length of nrow of alpha in ", "input object") # Update Xr. if (!missing(Xr)) s$Xr = Xr if (!missing(X)) s$Xr = compute_Xb(X,colSums(s$mu * s$alpha)) # Reset KL and lbf. s$KL = rep(as.numeric(NA),nrow(s$alpha)) s$lbf = rep(as.numeric(NA),nrow(s$alpha)) class(s) = "susie" return(s) } susieR/R/susie_rss.R0000644000176200001440000002143314142574553014104 0ustar liggesusers#' @title Sum of Single Effects (SuSiE) Regression using summary statistics #' #' @description \code{susie_rss} performs variable selection under a #' sparse Bayesian multiple linear regression of \eqn{Y} on \eqn{X} #' using only the z-scores from standard univariate regression #' of \eqn{Y} on each column of \eqn{X}, and an estimate \eqn{R} of #' the correlation matrix between columns of \eqn{X}. It does this by #' combining the "RSS likelihood" from Zhu and Stephens (2017) with #' the Sum of Single Effects" model from Wang et al (2020). #' #' #' @details In some applications, particularly genetic applications, #' it is desired to fit a regression model (\eqn{Y = X\tilde{b} + E} #' say, which we refer to as "the original regression model" or ORM) #' without access to the actual values of \eqn{Y} and \eqn{X}, but #' given only some summary statistics. \code{susie_rss} assumes the #' availability of \eqn{z} scores from standard univariate regression #' of \eqn{Y} on each column of \eqn{X}, and an estimate \eqn{R} of #' the correlation matrix between columns of \eqn{X} (\eqn{R} is #' sometimes called the LD matrix in genetic applications). See Zhu #' and Stephens (2017), and references therein, for further #' background. #' #' The \code{susie_rss} function is based on the model (2.10) from #' Zhu and Stephens, \eqn{z | R, b ~ N(Rb,R)} where \eqn{b} is a #' vector of length p representing the effects to be estimated. The #' effect \eqn{b_j} is simply a multiple of the coefficient #' \eqn{\tilde{b}_j} in the ORM, and so \eqn{b_j} is non-zero if and #' only if \eqn{\tilde{b}_j} is non-zero. In this sense the variable #' selection problem in this model is the same as the variable #' selection problem in the ORM, and so the credible sets and PIPs #' computed by \code{susie_rss} can be interpreted as credible sets #' and PIPs for the ORM. However, converting posterior estimates of #' \eqn{b_j} to estimates of \eqn{\tilde{b}_j} would require #' computation of the scaling factor (not done here). #' #' More precisely, \code{susie_rss} assumes the log-likelihood for #' \eqn{b} is \eqn{l(b; z,R) = -0.5(b'Rb - 2z'b)}, which is equivalent #' to model (2.10) from Zhu and Stephens if \eqn{R} is invertible, but #' does not require \eqn{R} to be invertible. It combines this #' likelihood with the \dQuote{susie prior} which assumes that \eqn{b #' = \sum_{l=1}^L b_l} where each \eqn{b_l} is a vector of length p #' with exactly one non-zero element; see \code{\link{susie}} and Wang #' et al (2020) for details. #' #' In practice, this is accomplished by calling \code{susie_suff_stat} #' with \code{XtX = R} and \code{Xty = z}, and fixing #' \code{residual_variance = 1}. (Values for \code{n} and \code{yty} #' are also required by \code{susie_suff_stat}. They do not affect #' inference when the residual variance is fixed, but they do affect #' the interpretation of \code{scaled_prior_variance}; we set #' \code{n=2, yty=1} so that \eqn{var(y) = yty/(n-1) = 1}.) Additional #' arguments to be passed to \code{\link{susie_suff_stat}} can be #' provided via \code{...}. #' #' @param z A p-vector of z scores. #' #' @param R A p by p correlation matrix. #' #' @param z_ld_weight This parameter is included for backwards #' compatibility with previous versions of the function, but it is no #' longer recommended to use a non-zero value. If \code{z_ld_weight #' > 0}, the matrix R used in the model is adjusted to be #' \code{cov2cor((1-w)*R + w*tcrossprod(z))}, where \code{w = #' z_ld_weight}. #' #' @param prior_variance The prior variance(s) for the non-zero #' element of \eqn{b_l}. It is either a scalar, or a vector of length #' L. When \code{estimate_prior_variance = TRUE} (highly recommended) #' this simply provides an initial value for the prior variance, and #' the default value of 50 is simply intended to be a large initial #' value. #' #' @param estimate_prior_variance If \code{estimate_prior_variance = #' TRUE}, which is highly recommended, the prior variance is estimated #' (this is a separate parameter for each of the L effects). If #' provided, \code{prior_variance} is then used as an initial value #' for the optimization. When \code{estimate_prior_variance = FALSE} #' (not recommended) the prior variance for each of the L effects is #' determined by the value supplied to \code{prior_variance}. #' #' @param check_prior When \code{check_prior = TRUE}, it checks if the #' estimated prior variance becomes unreasonably large (comparing with #' 10 * max(abs(z))^2). #' #' @param \dots Other parameters to be passed to #' \code{\link{susie_suff_stat}}. #' #' @return A \code{"susie"} object with the following #' elements: #' #' \item{alpha}{An L by p matrix of posterior inclusion probabilites.} #' #' \item{mu}{An L by p matrix of posterior means, conditional on #' inclusion.} #' #' \item{mu2}{An L by p matrix of posterior second moments, #' conditional on inclusion.} #' #' \item{lbf}{log-Bayes Factor for each single effect.} #' #' \item{lbf_variable}{log-Bayes Factor for each variable and single effect.} #' #' \item{V}{Prior variance of the non-zero elements of b.} #' #' \item{elbo}{The value of the variational lower bound, or #' \dQuote{ELBO} (objective function to be maximized), achieved at #' each iteration of the IBSS fitting procedure.} #' #' \item{Rr}{A vector of length p, equal to \code{R \%*\% colSums(alpha*mu)}.} #' #' \item{sets}{Credible sets estimated from model fit; see #' \code{\link{susie_get_cs}} for details.} #' #' \item{pip}{A vector of length p giving the (marginal) posterior #' inclusion probabilities for all p covariates.} #' #' \item{niter}{Number of IBSS iterations that were performed.} #' #' \item{converged}{\code{TRUE} or \code{FALSE} indicating whether #' the IBSS converged to a solution within the chosen tolerance #' level.} #' #' @references #' G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple #' new approach to variable selection in regression, with application #' to genetic fine-mapping. \emph{Journal of the Royal Statistical #' Society, Series B} \bold{82}, 1273-1300 \doi{10.1101/501114}. #' #' Y. Zou, P. Carbonetto, G. Wang and M. Stephens (2021). #' Fine-mapping from summary data with the \dQuote{Sum of Single Effects} #' model. \emph{bioRxiv} \doi{10.1101/2021.11.03.467167}. #' #' X. Zhu and M. Stephens (2017). Bayesian large-scale multiple #' regression with summary statistics from genome-wide association #' studies. \emph{Annals of Applied Statistics} \bold{11}, 1561-1592. #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' #' input_ss = compute_suff_stat(X,y,standardize = TRUE) #' ss = univariate_regression(X,y) #' R = with(input_ss,cov2cor(XtX)) #' zhat = with(ss,betahat/sebetahat) #' res = susie_rss(zhat,R) #' #' # Toy example illustrating behaviour susie_rss when the z-scores #' # are mostly consistent with a non-invertible correlation matrix. #' # Here the CS should contain both variables, and two PIPs should #' # be nearly the same. #' z = c(6,6.01) #' R = matrix(1,2,2) #' fit = susie_rss(z,R) #' print(fit$sets$cs) #' print(fit$pip) #' #' # In this second toy example, the only difference is that one #' # z-score is much larger than the other. Here we expect that the #' # second PIP will be much larger than the first. #' z = c(6,7) #' R = matrix(1,2,2) #' fit = susie_rss(z,R) #' print(fit$sets$cs) #' print(fit$pip) #' #' @export #' susie_rss = function (z, R, z_ld_weight = 0, prior_variance = 50, estimate_prior_variance=TRUE, check_prior=TRUE, ...) { # Check input R. if (nrow(R) != length(z)) stop(paste0("The dimension of correlation matrix (", nrow(R)," by ", ncol(R),") does not agree with expected (",length(z)," by ", length(z),")")) # Modify R by z_ld_weight; this modification was designed to ensure # the column space of R contained z, but susie_suff_stat does not # require this, and it is no longer recommended if (z_ld_weight > 0) { warning("As of version 0.11.0, use of non-zero z_ld_weight is no longer ", "recommended") R = muffled_cov2cor((1-z_ld_weight)*R + z_ld_weight * tcrossprod(z)) R = (R + t(R))/2 } # The choice of n=2, yty=1 is arbitrary except in that it ensures # var(y) = yty/(n-1) = 1 and because of this scaled_prior_variance = # prior_variance. s = susie_suff_stat(XtX = R,Xty = z,n = 2,yty = 1, scaled_prior_variance = prior_variance, estimate_prior_variance = estimate_prior_variance, residual_variance = 1,estimate_residual_variance = FALSE, standardize = FALSE,check_prior = check_prior,...) s$Rr = s$XtXr s$XtXr = NULL return(s) } susieR/R/update_each_effect_rss.R0000644000176200001440000000325513755042336016532 0ustar liggesusers# @title Update each effect once. # @param R a p by p symmetric and positive semidefinite correlation matrix. # @param z a p vector # @param s_init a list with elements sigma2, V, alpha, mu, Xr # @param Sigma sigma2*R + lambda I # @param estimate_prior_variance boolean indicating whether to estimate # prior variance # @param check_null_threshold float a threshold on the log scale to # compare likelihood between current estimate and zero the null # #' @importFrom Matrix diag update_each_effect_rss = function (R, z, s_init, Sigma, estimate_prior_variance = FALSE, estimate_prior_method = "optim", check_null_threshold = 0) { if (!estimate_prior_variance) estimate_prior_method = "none" # Repeat for each effect to update. s = s_init L = nrow(s$alpha) if(L > 0) for (l in 1:L) { # Remove lth effect from fitted values. s$Rz = s$Rz - R %*% (s$alpha[l,] * s$mu[l,]) # Compute residuals. r = z - s$Rz res = single_effect_regression_rss(as.vector(r),Sigma,s$V[l],s$pi, estimate_prior_method,check_null_threshold) # Update the variational estimate of the posterior mean. s$mu[l,] = res$mu s$alpha[l,] = res$alpha s$mu2[l,] = res$mu2 s$V[l] = res$V s$lbf[l] = res$lbf_model s$lbf_variable[l,] = res$lbf s$KL[l] = -res$lbf_model + SER_posterior_e_loglik_rss(R,Sigma,r,res$alpha * res$mu, res$alpha * res$mu2) s$Rz = s$Rz + R %*% (s$alpha[l,] * s$mu[l,]) } s$Rz = unname(as.matrix(s$Rz)) return(s) }susieR/R/single_effect_regression_ss.R0000644000176200001440000000374713755042336017635 0ustar liggesusers#' @rdname single_effect_regression #' #' @param Xty A p-vector. #' #' @param dXtX A p-vector containing the diagonal elements of #' \code{crossprod(X)}. #' #' @importFrom stats uniroot #' @importFrom stats optim #' #' @keywords internal #' single_effect_regression_ss = function (Xty, dXtX, V = 1, residual_variance = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0) { optimize_V = match.arg(optimize_V) betahat = (1/dXtX) * Xty shat2 = residual_variance/dXtX if (is.null(prior_weights)) prior_weights = rep(1/length(dXtX),length(dXtX)) if (optimize_V != "EM" && optimize_V != "none") V = optimize_prior_variance(optimize_V,betahat,shat2,prior_weights, alpha = NULL,post_mean2 = NULL,V_init = V, check_null_threshold = check_null_threshold) # log(bf) for each SNP. lbf = dnorm(betahat,0,sqrt(V + shat2),log = TRUE) - dnorm(betahat,0,sqrt(shat2),log = TRUE) # Deal with special case of infinite shat2 (e.g., happens if X does # not vary). lbf[is.infinite(shat2)] = 0 # w is proportional to BF, but subtract max for numerical stability # posterior prob on each SNP. maxlbf = max(lbf) w = exp(lbf - maxlbf) w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) alpha = w_weighted / weighted_sum_w post_var = (1/V + dXtX/residual_variance)^(-1) # Posterior variance. post_mean = (1/residual_variance) * post_var * Xty post_mean2 = post_var + post_mean^2 # Second moment. lbf_model = maxlbf + log(weighted_sum_w) # Analogue of loglik in the # non-summary case. if (optimize_V == "EM") V = optimize_prior_variance(optimize_V,betahat,shat2,prior_weights,alpha, post_mean2,check_null_threshold = check_null_threshold) return(list(alpha = alpha,mu = post_mean,mu2 = post_mean2,lbf = lbf, V = V,lbf_model = lbf_model)) } susieR/R/susieR-package.R0000644000176200001440000000031214051243075014710 0ustar liggesusers#' @keywords internal "_PACKAGE" # The following block is used by usethis to automatically manage # roxygen namespace tags. Modify with care! ## usethis namespace: start ## usethis namespace: end NULL susieR/R/elbo.R0000644000176200001440000000233513703336624013003 0ustar liggesusers# @title Get objective function from data and susie fit object. # @param data A flash data object. # @param f A flash fit object. # @keywords internal get_objective = function (X, Y, s) { return(Eloglik(X,Y,s) - sum(s$KL)) } # Expected loglikelihood for a susie fit. Eloglik = function (X, Y, s) { n = nrow(X) return(-(n/2) * log(2*pi*s$sigma2) - (1/(2*s$sigma2)) * get_ER2(X,Y,s)) } # Expected squared residuals. # s$Xr is column sum of Xr_L get_ER2 = function (X, Y, s) { Xr_L = compute_MXt(s$alpha * s$mu,X) # L by N matrix postb2 = s$alpha * s$mu2 # Posterior second moment. return(sum((Y - s$Xr)^2) - sum(Xr_L^2) + sum(attr(X,"d") * t(postb2))) } # @title posterior expected loglikelihood for a single effect regression # @param X an n by p matrix of covariates # @param Y an n vector of regression outcome # @param s2 the residual variance # @param Eb the posterior mean of b (p vector) (alpha * mu) # @param Eb2 the posterior second moment of b (p vector) (alpha * mu2) SER_posterior_e_loglik = function (X, Y, s2, Eb, Eb2) { n = nrow(X) return(-0.5*n*log(2*pi*s2) - 0.5/s2*(sum(Y*Y) - 2*sum(Y*compute_Xb(X,Eb)) + sum(attr(X,"d") * Eb2))) } susieR/R/remove_null_effects.R0000644000176200001440000000107014075324172016101 0ustar liggesusersremove_null_effects = function (s) { null_indices = (s$V == 0) s$alpha = s$alpha[!null_indices,,drop = FALSE] s$mu = s$mu[!null_indices,,drop = FALSE] s$mu2 = s$mu2[!null_indices,,drop = FALSE] s$lbf_variable = s$lbf_variable[!null_indices,,drop = FALSE] s$V = s$V[!null_indices,drop = FALSE] return(s) } add_null_effect = function (s, V) { p = ncol(s$alpha) s$alpha = rbind(s$alpha,1/p) s$mu = rbind(s$mu,rep(0,p)) s$mu2 = rbind(s$mu2,rep(0,p)) s$lbf_variable = rbind(s$lbf_variable,rep(0,p)) s$V = c(s$V,V) return(s) } susieR/R/compute_ss.R0000644000176200001440000000373114141046730014235 0ustar liggesusers#' @title Compute sufficient statistics for input to \code{susie_suff_stat} #' #' @description Computes the sufficient statistics \eqn{X'X, X'y, y'y} #' and \eqn{n} after centering (and possibly standardizing) the #' columns of \eqn{X} and centering \eqn{y} to have mean zero. We also #' store the column means of \eqn{X} and mean of \eqn{y}. #' #' @param X An n by p matrix of covariates. #' #' @param y An n vector. #' #' @param standardize Logical flag indicating whether to standardize #' columns of X to unit variance prior to computing summary data #' #' @return A list of sufficient statistics (\code{XtX, Xty, yty, n}) #' and \code{X_colmeans}, \code{y_mean}. #' #' @importFrom methods as #' @importFrom Matrix colMeans #' @importFrom Matrix crossprod #' #' @examples #' data(N2finemapping) #' ss = compute_suff_stat(N2finemapping$X, N2finemapping$Y[,1]) #' #' @export #' compute_suff_stat = function(X, y, standardize = FALSE) { y_mean = mean(y) y = y - y_mean n = nrow(X) mu = colMeans(X) s = compute_colSds(X) XtX = crossprod(X) XtX = as.matrix(XtX) XtX = XtX - n*tcrossprod(mu) if (standardize) { XtX = XtX/s XtX = t(XtX) XtX = XtX/s } Xty = drop(y %*% X) n = length(y) yty = sum(y^2) return(list(XtX = XtX,Xty = Xty,yty = yty,n = n, y_mean = y_mean,X_colmeans = mu)) } #' @title Compute sufficient statistics for input to \code{susie_suff_stat} #' #' @description This is a synonym for \code{compute_suff_stat} #' included for historical reasons (deprecated). #' #' @param X An n by p matrix of covariates. #' #' @param y An n vector. #' #' @param standardize Logical flag indicating whether to standardize #' columns of X to unit variance prior to computing summary data. #' #' @return A list of sufficient statistics (X'X, X'y, y'y and n) #' #' @importFrom methods as #' #' @examples #' data(N2finemapping) #' ss = compute_ss(N2finemapping$X, N2finemapping$Y[,1]) #' #' @export #' compute_ss = compute_suff_stat susieR/R/susie.R0000644000176200001440000006003214142636221013202 0ustar liggesusers#' @rdname susie #' #' @title Sum of Single Effects (SuSiE) Regression #' #' @description Performs a sparse Bayesian multiple linear regression #' of y on X, using the "Sum of Single Effects" model from Wang et al #' (2020). In brief, this function fits the regression model \eqn{y = #' \mu + X b + e}, where elements of \eqn{e} are \emph{i.i.d.} normal #' with zero mean and variance \code{residual_variance}, \eqn{\mu} is #' an intercept term and \eqn{b} is a vector of length p representing #' the effects to be estimated. The \dQuote{susie assumption} is that #' \eqn{b = \sum_{l=1}^L b_l} where each \eqn{b_l} is a vector of #' length p with exactly one non-zero element. The prior on the #' non-zero element is normal with zero mean and variance \code{var(y) #' * scaled_prior_variance}. The value of \code{L} is fixed, and #' should be chosen to provide a reasonable upper bound on the number #' of non-zero effects to be detected. Typically, the hyperparameters #' \code{residual_variance} and \code{scaled_prior_variance} will be #' estimated during model fitting, although they can also be fixed as #' specified by the user. See functions \code{\link{susie_get_cs}} and #' other functions of form \code{susie_get_*} to extract the most #' commonly-used results from a susie fit. #' #' @details The function \code{susie} implements the IBSS algorithm #' from Wang et al (2020). The option \code{refine = TRUE} implements #' an additional step to help reduce problems caused by convergence of #' the IBSS algorithm to poor local optima (which is rare in our #' experience, but can provide misleading results when it occurs). The #' refinement step incurs additional computational expense that #' increases with the number of CSs found in the initial run. #' #' The function \code{susie_suff_stat} implements essentially the same #' algorithms, but using sufficient statistics. (The statistics are #' sufficient for the regression coefficients \eqn{b}, but not for the #' intercept \eqn{\mu}; see below for how the intercept is treated.) #' If the sufficient statistics are computed correctly then the #' results from \code{susie_suff_stat} should be the same as (or very #' similar to) \code{susie}, although runtimes will differ as #' discussed below. The simplest sufficient statistics are the sample #' size \code{n}, and then the p by p matrix \eqn{X'X}, the p-vector #' \eqn{X'y}, and the sum of squared y values \eqn{y'y}, all computed #' after centering the columns of \eqn{X} and the vector \eqn{y} to #' have mean 0; these can be computed using \code{compute_suff_stat}. #' Alternatively the user can provide \code{n} and \code{bhat} (the #' univariate OLS estimates from regressing y on each column of X), #' \code{shat} (the standard errrors from these OLS regressions), the #' p by p symmetric, positive semidefinite correlation #' matrix \eqn{R = cov2cor(X'X)}, and the variance of \eqn{y}, again #' all computed from centered \eqn{X} and \eqn{y}. Note that here #' \code{R} and \code{bhat} should be computed using the same matrix #' \eqn{X}. If you do not have access to the original \eqn{X} to #' compute the matrix \code{R} then use \code{\link{susie_rss}}. #' #' The handling of the intercept term in \code{susie_suff_stat} needs #' some additional explanation. Computing the summary data after #' centering \code{X} and \code{y} effectively ensures that the #' resulting posterior quantities for \eqn{b} allow for an intercept #' in the model; however, the actual value of the intercept cannot be #' estimated from these centered data. To estimate the intercept term #' the user must also provide the column means of \eqn{X} and the mean #' of \eqn{y} (\code{X_colmeans} and \code{y_mean}). If these are not #' provided, they are treated as \code{NA}, which results in the #' intercept being \code{NA}. If for some reason you prefer to have #' the intercept be 0 instead of \code{NA} then set #' \code{X_colmeans = 0,y_mean = 0}. #' #' For completeness, we note that if \code{susie_suff_stat} is run on #' \eqn{X'X, X'y, y'y} computed \emph{without} centering \eqn{X} and #' \eqn{y}, and with \code{X_colmeans = 0,y_mean = 0}, this is #' equivalent to \code{susie} applied to \eqn{X, y} with #' \code{intercept = FALSE} (although results may differ due to #' different initializations of \code{residual_variance} and #' \code{scaled_prior_variance}). However, this usage is not #' recommended for for most situations. #' #' The computational complexity of \code{susie} is \eqn{O(npL)} per #' iteration, whereas \code{susie_suff_stat} is \eqn{O(p^2L)} per #' iteration (not including the cost of computing the sufficient #' statistics, which is dominated by the \eqn{O(np^2)} cost of #' computing \eqn{X'X}). Because of the cost of computing \eqn{X'X}, #' \code{susie} will usually be faster. However, if \eqn{n >> p}, #' and/or if \eqn{X'X} is already computed, then #' \code{susie_suff_stat} may be faster. #' #' @param X An n by p matrix of covariates. #' #' @param y The observed responses, a vector of length n. #' #' @param L Maximum number of non-zero effects in the susie #' regression model. If L is larger than the number of covariates, p, #' L is set to p. #' #' @param scaled_prior_variance The prior variance, divided by #' \code{var(y)} (or by \code{(1/(n-1))yty} for #' \code{susie_suff_stat}); that is, the prior variance of each #' non-zero element of b is \code{var(y) * scaled_prior_variance}. The #' value provided should be either a scalar or a vector of length #' \code{L}. If \code{estimate_prior_variance = TRUE}, this provides #' initial estimates of the prior variances. #' #' @param residual_variance Variance of the residual. If #' \code{estimate_residual_variance = TRUE}, this value provides the #' initial estimate of the residual variance. By default, it is set to #' \code{var(y)} in \code{susie} and \code{(1/(n-1))yty} in #' \code{susie_suff_stat}. #' #' @param prior_weights A vector of length p, in which each entry #' gives the prior probability that corresponding column of X has a #' nonzero effect on the outcome, y. #' #' @param null_weight Prior probability of no effect (a number between #' 0 and 1, and cannot be exactly 1). #' #' @param standardize If \code{standardize = TRUE}, standardize the #' columns of X to unit variance prior to fitting (or equivalently #' standardize XtX and Xty to have the same effect). Note that #' \code{scaled_prior_variance} specifies the prior on the #' coefficients of X \emph{after} standardization (if it is #' performed). If you do not standardize, you may need to think more #' carefully about specifying \code{scaled_prior_variance}. Whatever #' your choice, the coefficients returned by \code{coef} are given for #' \code{X} on the original input scale. Any column of \code{X} that #' has zero variance is not standardized. #' #' @param intercept If \code{intercept = TRUE}, the intercept is #' fitted; it \code{intercept = FALSE}, the intercept is set to #' zero. Setting \code{intercept = FALSE} is generally not #' recommended. #' #' @param estimate_residual_variance If #' \code{estimate_residual_variance = TRUE}, the residual variance is #' estimated, using \code{residual_variance} as an initial value. If #' \code{estimate_residual_variance = FALSE}, the residual variance is #' fixed to the value supplied by \code{residual_variance}. #' #' @param estimate_prior_variance If \code{estimate_prior_variance = #' TRUE}, the prior variance is estimated (this is a separate #' parameter for each of the L effects). If provided, #' \code{scaled_prior_variance} is then used as an initial value for #' the optimization. When \code{estimate_prior_variance = FALSE}, the #' prior variance for each of the L effects is determined by the #' value supplied to \code{scaled_prior_variance}. #' #' @param estimate_prior_method The method used for estimating prior #' variance. When \code{estimate_prior_method = "simple"} is used, the #' likelihood at the specified prior variance is compared to the #' likelihood at a variance of zero, and the setting with the larger #' likelihood is retained. #' #' @param check_null_threshold When the prior variance is estimated, #' compare the estimate with the null, and set the prior variance to #' zero unless the log-likelihood using the estimate is larger by this #' threshold amount. For example, if you set #' \code{check_null_threshold = 0.1}, this will "nudge" the estimate #' towards zero when the difference in log-likelihoods is small. A #' note of caution that setting this to a value greater than zero may #' lead the IBSS fitting procedure to occasionally decrease the ELBO. #' #' @param prior_tol When the prior variance is estimated, compare the #' estimated value to \code{prior_tol} at the end of the computation, #' and exclude a single effect from PIP computation if the estimated #' prior variance is smaller than this tolerance value. #' #' @param residual_variance_upperbound Upper limit on the estimated #' residual variance. It is only relevant when #' \code{estimate_residual_variance = TRUE}. #' #' @param s_init A previous susie fit with which to initialize. #' #' @param coverage A number between 0 and 1 specifying the #' \dQuote{coverage} of the estimated confidence sets. #' #' @param min_abs_corr Minimum absolute correlation allowed in a #' credible set. The default, 0.5, corresponds to a squared #' correlation of 0.25, which is a commonly used threshold for #' genotype data in genetic studies. #' #' @param compute_univariate_zscore If \code{compute_univariate_zscore #' = TRUE}, the univariate regression z-scores are outputted for each #' variable. #' #' @param na.rm Drop any missing values in y from both X and y. #' #' @param max_iter Maximum number of IBSS iterations to perform. #' #' @param tol A small, non-negative number specifying the convergence #' tolerance for the IBSS fitting procedure. The fitting procedure #' will halt when the difference in the variational lower bound, or #' \dQuote{ELBO} (the objective function to be maximized), is #' less than \code{tol}. #' #' @param verbose If \code{verbose = TRUE}, the algorithm's progress, #' and a summary of the optimization settings, are printed to the #' console. #' #' @param track_fit If \code{track_fit = TRUE}, \code{trace} #' is also returned containing detailed information about the #' estimates at each iteration of the IBSS fitting procedure. #' #' @param residual_variance_lowerbound Lower limit on the estimated #' residual variance. It is only relevant when #' \code{estimate_residual_variance = TRUE}. #' #' @param refine If \code{refine = TRUE}, then an additional #' iterative refinement procedure is used, after the IBSS algorithm, #' to check and escape from local optima (see details). #' #' @param n_purity Passed as argument \code{n_purity} to #' \code{\link{susie_get_cs}}. #' #' @return A \code{"susie"} object with some or all of the following #' elements: #' #' \item{alpha}{An L by p matrix of posterior inclusion probabilites.} #' #' \item{mu}{An L by p matrix of posterior means, conditional on #' inclusion.} #' #' \item{mu2}{An L by p matrix of posterior second moments, #' conditional on inclusion.} #' #' \item{Xr}{A vector of length n, equal to \code{X \%*\% colSums(alpha #' * mu)}.} #' #' \item{lbf}{log-Bayes Factor for each single effect.} #' #' \item{lbf_variable}{log-Bayes Factor for each variable and single effect.} #' #' \item{intercept}{Intercept (fixed or estimated).} #' #' \item{sigma2}{Residual variance (fixed or estimated).} #' #' \item{V}{Prior variance of the non-zero elements of b, equal to #' \code{scaled_prior_variance * var(y)}.} #' #' \item{elbo}{The value of the variational lower bound, or #' \dQuote{ELBO} (objective function to be maximized), achieved at #' each iteration of the IBSS fitting procedure.} #' #' \item{fitted}{Vector of length n containing the fitted values of #' the outcome.} #' #' \item{sets}{Credible sets estimated from model fit; see #' \code{\link{susie_get_cs}} for details.} #' #' \item{pip}{A vector of length p giving the (marginal) posterior #' inclusion probabilities for all p covariates.} #' #' \item{z}{A vector of univariate z-scores.} #' #' \item{niter}{Number of IBSS iterations that were performed.} #' #' \item{converged}{\code{TRUE} or \code{FALSE} indicating whether #' the IBSS converged to a solution within the chosen tolerance #' level.} #' #' \code{susie_suff_stat} returns also outputs: #' #' \item{XtXr}{A p-vector of \code{t(X)} times the fitted values, #' \code{X \%*\% colSums(alpha*mu)}.} #' #' @references #' G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple #' new approach to variable selection in regression, with application #' to genetic fine-mapping. \emph{Journal of the Royal Statistical #' Society, Series B} \bold{82}, 1273-1300 \doi{10.1101/501114}. #' #' Y. Zou, P. Carbonetto, G. Wang and M. Stephens (2021). #' Fine-mapping from summary data with the \dQuote{Sum of Single Effects} #' model. \emph{bioRxiv} \doi{10.1101/2021.11.03.467167}. #' #' @seealso \code{\link{susie_get_cs}} and other \code{susie_get_*} #' functions for extracting results; \code{\link{susie_trendfilter}} for #' applying the SuSiE model to non-parametric regression, particularly #' changepoint problems, and \code{\link{susie_rss}} for applying the #' SuSiE model when one only has access to limited summary statistics #' related to \eqn{X} and \eqn{y} (typically in genetic applications). #' #' @examples #' # susie example #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' res1 = susie(X,y,L = 10) #' susie_get_cs(res1) # extract credible sets from fit #' plot(beta,coef(res1)[-1]) #' abline(a = 0,b = 1,col = "skyblue",lty = "dashed") #' plot(y,predict(res1)) #' abline(a = 0,b = 1,col = "skyblue",lty = "dashed") #' #' # susie_suff_stat example #' input_ss = compute_suff_stat(X,y) #' res2 = with(input_ss, #' susie_suff_stat(XtX = XtX,Xty = Xty,yty = yty,n = n, #' X_colmeans = X_colmeans,y_mean = y_mean,L = 10)) #' plot(coef(res1),coef(res2)) #' abline(a = 0,b = 1,col = "skyblue",lty = "dashed") #' #' @importFrom stats var #' @importFrom utils modifyList #' @importFrom crayon magenta #' #' @export #' susie = function (X,y,L = min(10,ncol(X)), scaled_prior_variance = 0.2, residual_variance = NULL, prior_weights = NULL, null_weight = NULL, standardize = TRUE, intercept = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim", "EM", "simple"), check_null_threshold = 0, prior_tol = 1e-9, residual_variance_upperbound = Inf, s_init = NULL, coverage = 0.95, min_abs_corr = 0.5, compute_univariate_zscore = FALSE, na.rm = FALSE, max_iter = 100, tol = 1e-3, verbose = FALSE, track_fit = FALSE, residual_variance_lowerbound = var(drop(y))/1e4, refine = FALSE, n_purity = 100) { # Process input estimate_prior_method. estimate_prior_method = match.arg(estimate_prior_method) # Check input X. if (!(is.double(X) & is.matrix(X)) & !inherits(X,"CsparseMatrix") & is.null(attr(X,"matrix.type"))) stop("Input X must be a double-precision matrix, or a sparse matrix, or ", "a trend filtering matrix") if (is.numeric(null_weight) && null_weight == 0) null_weight = NULL if (!is.null(null_weight) && is.null(attr(X,"matrix.type"))) { if (!is.numeric(null_weight)) stop("Null weight must be numeric") if (null_weight < 0 || null_weight >= 1) stop("Null weight must be between 0 and 1") if (missing(prior_weights)) prior_weights = c(rep(1/ncol(X) * (1 - null_weight),ncol(X)),null_weight) else prior_weights = c(prior_weights * (1-null_weight),null_weight) X = cbind(X,0) } if (anyNA(X)) stop("Input X must not contain missing values") if (anyNA(y)) { if (na.rm) { samples_kept = which(!is.na(y)) y = y[samples_kept] X = X[samples_kept,] } else stop("Input y must not contain missing values") } p = ncol(X) if (p > 1000 & !requireNamespace("Rfast",quietly = TRUE)) message(magenta("For an X with many columns, please consider installing ", "the Rfast package for more efficient credible set (CS) ", "calculations.")) # Check input y. n = nrow(X) mean_y = mean(y) # Center and scale input. if (intercept) y = y - mean_y # Set three attributes for matrix X: attr(X,'scaled:center') is a # p-vector of column means of X if center=TRUE, a p vector of zeros # otherwise; 'attr(X,'scaled:scale') is a p-vector of column # standard deviations of X if scale=TRUE, a p vector of ones # otherwise; 'attr(X,'d') is a p-vector of column sums of # X.standardized^2,' where X.standardized is the matrix X centered # by attr(X,'scaled:center') and scaled by attr(X,'scaled:scale'). out = compute_colstats(X,center = intercept,scale = standardize) attr(X,"scaled:center") = out$cm attr(X,"scaled:scale") = out$csd attr(X,"d") = out$d # Initialize susie fit. s = init_setup(n,p,L,scaled_prior_variance,residual_variance,prior_weights, null_weight,as.numeric(var(y)),standardize) if (!missing(s_init) && !is.null(s_init)) { if (!inherits(s_init,"susie")) stop("s_init should be a susie object") if (max(s_init$alpha) > 1 || min(s_init$alpha) < 0) stop("s_init$alpha has invalid values outside range [0,1]; please ", "check your input") # First, remove effects with s_init$V = 0 s_init = susie_prune_single_effects(s_init) num_effects = nrow(s_init$alpha) if(missing(L)){ L = num_effects }else if(min(p,L) < num_effects){ warning(paste("Specified number of effects L =",min(p,L), "is smaller than the number of effects",num_effects, "in input SuSiE model. The SuSiE model will have", num_effects,"effects.")) L = num_effects } # expand s_init if L > num_effects. s_init = susie_prune_single_effects(s_init, min(p, L), s$V) s = modifyList(s,s_init) s = init_finalize(s,X = X) } else { s = init_finalize(s) } # Initialize elbo to NA. elbo = rep(as.numeric(NA),max_iter + 1) elbo[1] = -Inf; tracking = list() for (i in 1:max_iter) { if (track_fit) tracking[[i]] = susie_slim(s) s = update_each_effect(X,y,s,estimate_prior_variance,estimate_prior_method, check_null_threshold) if (verbose) print(paste0("objective:",get_objective(X,y,s))) # Compute objective before updating residual variance because part # of the objective s$kl has already been computed under the # residual variance before the update. elbo[i+1] = get_objective(X,y,s) if ((elbo[i+1] - elbo[i]) < tol) { s$converged = TRUE break } if (estimate_residual_variance) { s$sigma2 = pmax(residual_variance_lowerbound, estimate_residual_variance(X,y,s)) if (s$sigma2 > residual_variance_upperbound) s$sigma2 = residual_variance_upperbound if (verbose) print(paste0("objective:",get_objective(X,y,s))) } } # Remove first (infinite) entry, and trailing NAs. elbo = elbo[2:(i+1)] s$elbo = elbo s$niter = i if (is.null(s$converged)) { warning(paste("IBSS algorithm did not converge in",max_iter,"iterations!")) s$converged = FALSE } if (intercept) { # Estimate unshrunk intercept. s$intercept = mean_y - sum(attr(X,"scaled:center") * (colSums(s$alpha * s$mu)/attr(X,"scaled:scale"))) s$fitted = s$Xr + mean_y } else { s$intercept = 0 s$fitted = s$Xr } s$fitted = drop(s$fitted) names(s$fitted) = `if`(is.null(names(y)),rownames(X),names(y)) if (track_fit) s$trace = tracking # SuSiE CS and PIP. if (!is.null(coverage) && !is.null(min_abs_corr)) { s$sets = susie_get_cs(s,coverage = coverage,X = X, min_abs_corr = min_abs_corr, n_purity = n_purity) s$pip = susie_get_pip(s,prune_by_cs = FALSE,prior_tol = prior_tol) } if (!is.null(colnames(X))) { variable_names = colnames(X) if (!is.null(null_weight)) { variable_names[length(variable_names)] = "null" names(s$pip) = variable_names[-p] } else names(s$pip) = variable_names colnames(s$alpha) = variable_names colnames(s$mu) = variable_names colnames(s$mu2) = variable_names colnames(s$lbf_variable) = variable_names } # report z-scores from univariate regression. if (compute_univariate_zscore) { if (!is.null(null_weight) && null_weight != 0) X = X[,1:(ncol(X) - 1)] s$z = calc_z(X,y,center = intercept,scale = standardize) } # For prediction. s$X_column_scale_factors = attr(X,"scaled:scale") if (refine) { if (!missing(s_init) && !is.null(s_init)) warning("The given s_init is not used in refinement") if (!is.null(null_weight) && null_weight != 0) { ## if null_weight is specified, we compute the original prior_weight pw_s = s$pi[-s$null_index]/(1-null_weight) if (!compute_univariate_zscore) ## if null_weight is specified, and the extra 0 column is not ## removed from compute_univariate_zscore, we remove it here X = X[,1:(ncol(X) - 1)] } else pw_s = s$pi conti = TRUE while (conti & length(s$sets$cs)>0) { m = list() for(cs in 1:length(s$sets$cs)){ pw_cs = pw_s pw_cs[s$sets$cs[[cs]]] = 0 if(all(pw_cs == 0)){ break } s2 = susie(X,y,L = L,scaled_prior_variance = scaled_prior_variance, residual_variance = residual_variance, prior_weights = pw_cs, s_init = NULL,null_weight = null_weight, standardize = standardize,intercept = intercept, estimate_residual_variance = estimate_residual_variance, estimate_prior_variance = estimate_prior_variance, estimate_prior_method = estimate_prior_method, check_null_threshold = check_null_threshold, prior_tol = prior_tol,coverage = coverage, residual_variance_upperbound = residual_variance_upperbound, min_abs_corr = min_abs_corr,compute_univariate_zscore = FALSE, na.rm = na.rm,max_iter = max_iter,tol = tol,verbose = FALSE, track_fit = FALSE,residual_variance_lowerbound = var(drop(y))/1e4, refine = FALSE) sinit2 = s2[c("alpha","mu","mu2")] class(sinit2) = "susie" s3 = susie(X,y,L = L,scaled_prior_variance = scaled_prior_variance, residual_variance = residual_variance,prior_weights = pw_s, s_init = sinit2,null_weight = null_weight, standardize = standardize,intercept = intercept, estimate_residual_variance = estimate_residual_variance, estimate_prior_variance = estimate_prior_variance, estimate_prior_method = estimate_prior_method, check_null_threshold = check_null_threshold, prior_tol = prior_tol,coverage = coverage, residual_variance_upperbound = residual_variance_upperbound, min_abs_corr = min_abs_corr,compute_univariate_zscore = FALSE, na.rm = na.rm,max_iter = max_iter,tol = tol,verbose = FALSE, track_fit = FALSE,residual_variance_lowerbound = var(drop(y))/1e4, refine = FALSE) m = c(m,list(s3)) } if(length(m) == 0){ conti = FALSE }else{ elbo = sapply(m,function(x) susie_get_objective(x)) if ((max(elbo) - susie_get_objective(s)) <= 0) conti = FALSE else s = m[[which.max(elbo)]] } } } return(s) } susieR/R/univariate_regression.R0000644000176200001440000000711414051463366016472 0ustar liggesusers#' @title Perform Univariate Linear Regression Separately for Columns of X #' #' @description This function performs the univariate linear #' regression y ~ x separately for each column x of X. Each regression #' is implemented using \code{.lm.fit()}. The estimated effect size #' and stardard error for each variable are outputted. #' #' @param X n by p matrix of regressors. #' #' @param y n-vector of response variables. #' #' @param Z Optional n by k matrix of covariates to be included in all #' regresions. If Z is not \code{NULL}, the linear effects of #' covariates are removed from y first, and the resulting residuals #' are used in place of y. #' #' @param center If \code{center = TRUE}, center X, y and Z. #' #' @param scale If \code{scale = TRUE}, scale X, y and Z. #' #' @param return_residuals Whether or not to output the residuals if Z #' is not \code{NULL}. #' #' @return A list with two vectors containing the least-squares #' estimates of the coefficients (\code{betahat}) and their standard #' errors (\code{sebetahat}). Optionally, and only when a matrix of #' covariates \code{Z} is provided, a third vector \code{residuals} #' containing the residuals is returned. #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' res = univariate_regression(X,y) #' plot(res$betahat/res$sebetahat) #' #' @importFrom stats lm #' @importFrom stats .lm.fit #' @importFrom stats coef #' @importFrom stats summary.lm #' #' @export #' univariate_regression = function (X, y, Z = NULL, center = TRUE, scale = FALSE, return_residuals = FALSE) { y_na = which(is.na(y)) if (length(y_na)) { X = X[-y_na,] y = y[-y_na] } if (center) { y = y - mean(y) X = scale(X,center = TRUE,scale = scale) } else X = scale(X,center = FALSE,scale = scale) X[is.nan(X)] = 0 if (!is.null(Z)) { if (center) Z = scale(Z,center = TRUE,scale = scale) y = .lm.fit(Z,y)$residuals } output = try(do.call(rbind, lapply(1:ncol(X), function (i) { g = .lm.fit(cbind(1,X[,i]),y) return(c(coef(g)[2],calc_stderr(cbind(1,X[,i]), g$residuals)[2])) })), silent = TRUE) # Exception occurs, fall back to a safer but slower calculation. if (inherits(output,"try-error")) { output = matrix(0,ncol(X),2) for (i in 1:ncol(X)) { fit = summary(lm(y ~ X[,i]))$coef if (nrow(fit) == 2) output[i,] = as.vector(summary(lm(y ~ X[,i]))$coef[2,1:2]) else output[i,] = c(0,0) } } if (return_residuals && !is.null(Z)) return(list(betahat = output[,1],sebetahat = output[,2],residuals = y)) else return(list(betahat = output[,1],sebetahat = output[,2])) } # Computes the z-scores (t-statistics) for association between Y and # each column of X. calc_z = function (X, Y, center = FALSE, scale = FALSE) { univariate_z = function(X,Y,center,scale) { out = univariate_regression(X,Y,center = center,scale = scale) return(out$betahat/out$sebetahat) } if (is.null(dim(Y))) return(univariate_z(X,Y,center,scale)) else return(do.call(cbind,lapply(1:ncol(Y), function(i) univariate_z(X,Y[,i], center = center, scale = scale)))) } susieR/R/data.R0000644000176200001440000001325314142574152012772 0ustar liggesusers#' @name N2finemapping #' #' @title Simulated Fine-mapping Data with Two Effect Variables #' #' @docType data #' #' @description This data set contains a genotype matrix for 574 #' individuals and 1,002 variables. The variables are genotypes after #' centering and scaling, and therefore retain the correlation #' structure of the original genotype data. Two of the variables have #' non-zero effects on the multivariate response. The response data #' are generated under a multivariate linear regression model. See #' Wang \emph{et al} (2020) for details. #' #' @format \code{N2finemapping} is a list with the following elements: #' #' \describe{ #' #' \item{X}{Centered and scaled genotype data.} #' #' \item{chrom}{Chromomsome of the original data, in hg38 coordinates.} #' #' \item{pos}{Chromomosomal position of the original data, in hg38 #' coordinates. The information can be used to compare impact of using #' other genotype references of the same variables in \code{susie_rss} #' application.} #' #' \item{true_coef}{Simulated effect sizes.} #' #' \item{residual_variance}{Simulated residual covariance matrix.} #' #' \item{Y}{Simulated multivariate response.} #' #' \item{allele_freq}{Allele frequencies based on the original #' genotype data.} #' #' \item{V}{Suggested prior covariance matrix for effect sizes of #' the two non-zero effect variables.} #' } #' #' @keywords data #' #' @references #' G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple #' new approach to variable selection in regression, with application #' to genetic fine-mapping. \emph{Journal of the Royal Statistical #' Society, Series B} \doi{10.1101/501114}. #' #' @examples #' data(N2finemapping) NULL #' @name N3finemapping #' #' @title Simulated Fine-mapping Data with Three Effect Variables. #' #' @docType data #' #' @description The data-set contains a matrix of 574 #' individuals and 1,001 variables. These variables are real-world #' genotypes centered and scaled, and therefore retains the #' correlation structure of variables in the original genotype data. 3 #' out of the variables have non-zero effects. The response data is #' generated under a multivariate linear regression model. See Wang #' \emph{et al} (2020) for more details. #' #' @format \code{N3finemapping} is a list with the following elements: #' #' \describe{ #' #' \item{X}{N by P variable matrix of centered and scaled genotype #' data.} #' #' \item{chrom}{Chromomsome of the original data, in hg38 coordinate.} #' #' \item{pos}{Chromomosomal positoin of the original data, in hg38 #' coordinate. The information can be used to compare impact of using #' other genotype references of the same variables in susie_rss #' application.} #' #' \item{true_coef}{The simulated effect sizes.} #' #' \item{residual_variance}{The simulated residual covariance matrix.} #' #' \item{Y}{The simulated response variables.} #' #' \item{allele_freq}{Allele frequency of the original genotype data.} #' #' \item{V}{Prior covariance matrix for effect size of the three #' non-zero effect variables.} } #' #' @keywords data #' #' @references #' G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple #' new approach to variable selection in regression, with application #' to genetic fine-mapping. \emph{Journal of the Royal Statistical #' Society, Series B} \doi{10.1101/501114}. #' #' @examples #' data(N3finemapping) NULL #' @name FinemappingConvergence #' #' @title Simulated Fine-mapping Data with Convergence Problem. #' #' @description Data simulated using real genotypes from 50,000 #' individuals and 200 SNPs. Two of the SNPs have non-zero effects #' on the multivariate response. The response data are generated under #' a linear regression model. The simulated response and the columns #' of the genotype matrix are centered. #' #' @format \code{FinemappingConvergence} is a list with the following #' elements: #' #' \describe{ #' #' \item{XtX}{Summary statistics computed using the centered and #' scaled genotype matrix.} #' #' \item{Xty}{Summary statistics computed using the centered and #' scaled genotype data, and the centered simulated response.} #' #' \item{yty}{yty is computed using the centered simulated response.} #' #' \item{n}{The sample size (50,000).} #' #' \item{true_coef}{The coefficients used to simulate the responses.} #' #' \item{z}{z-scores from a simple (single-SNP) linear regression.}} #' #' @docType data #' #' @keywords data #' #' @seealso A similar data set with more SNPs is used in the #' \dQuote{Refine SuSiE model} vignette. #' #' @examples #' data(FinemappingConvergence) NULL #' @name SummaryConsistency #' #' @title Simulated Fine-mapping Data with LD matrix From Reference Panel. #' #' @description Data simulated using real genotypes from 10,000 #' individuals and 200 SNPs. One SNP have non-zero effect #' on the multivariate response. The response data are generated under #' a linear regression model. There is also one SNP with flipped allele #' between summary statistics and the reference panel. #' #' @format \code{SummaryConsistency} is a list with the following #' elements: #' #' \describe{ #' #' \item{z}{z-scores computed by fitting univariate simple regression #' variable-by-variable.} #' #' \item{ldref}{LD matrix estimated from the reference panel.} #' #' \item{flip_id}{The index of the SNP with the flipped allele.} #' #' \item{signal_id}{The index of the SNP with the non-zero effect.}} #' #' @seealso A similar data set with more samples is used in the #' \dQuote{Diagnostic for fine-mapping with summary statistics} #' vignette. #' #' @docType data #' #' @keywords data #' #' @examples #' data(SummaryConsistency) NULL susieR/R/estimate_residual_variance.R0000644000176200001440000000104514051462763017433 0ustar liggesusers# @title Estimate residual variance # @param X an n by p matrix of covariantes # @param y an n vector of data # @param s a susie fit estimate_residual_variance = function (X, y, s) { n = nrow(X) return((1/n)*get_ER2(X,y,s)) } # @title Estimate residual variance for summary statistics # @param XtX a p by p matrix # @param Xty a p vector # @param s a susie fit # @param yty a scaler, y'y, where y is centered to have mean 0 # @param n sample size estimate_residual_variance_ss = function (XtX, Xty, s, yty, n) (1/n)*get_ER2_ss(XtX,Xty,s,yty) susieR/R/elbo_rss.R0000644000176200001440000000376314055430065013673 0ustar liggesusers# @title Get objective function from data and susie fit object. # @param R p by p corelation matrix # @param z length p vector # @param s a susie fit object get_objective_rss = function (R, z, s) Eloglik_rss(s$sigma2, R,z,s) - sum(s$KL) # @title expected loglikelihood for a susie fit Eloglik_rss = function (sigma2, R, z, s) { d = sigma2 * attr(R,"eigen")$values + attr(R,"lambda") if(attr(R,"lambda") == 0) result = -(sum(d != 0)/2) * log(2*pi*sigma2) - 0.5*get_ER2_rss(sigma2, R,z,s) else result = -(length(z)/2)*log(2*pi) - 0.5*sum(log(d)) - 0.5*get_ER2_rss(sigma2, R,z,s) return(result) } # @title expected squared residuals # @importFrom Matrix diag get_ER2_rss = function (sigma2, R, z, s) { d = sigma2 * attr(R,"eigen")$values + attr(R,"lambda") Dinv = 1/d Dinv[is.infinite(Dinv)] = 0 SinvR = attr(R,"eigen")$vectors %*% ((Dinv*attr(R,"eigen")$values) * t(attr(R,"eigen")$vectors)) Utz = crossprod(attr(R,"eigen")$vectors,z) zSinvz = sum(Utz * (Dinv * Utz)) Z = s$alpha * s$mu if(attr(R,"lambda") == 0) RSinvR = R/sigma2 else RSinvR = R %*% SinvR RZ2 = sum((Z%*%RSinvR) * Z) zbar = colSums(Z) postb2 = s$alpha * s$mu2 # Posterior second moment. return(zSinvz - 2*sum((SinvR %*% z) * zbar) + sum(zbar * (RSinvR %*% zbar)) - RZ2 + sum(diag(RSinvR) * t(postb2))) } # @title posterior expected loglikelihood for a single effect regression # @param R a p by p symmetric and positive semidefinite correlation matrix. # @param Sigma residual_var * R + lambda I # @param r residuals # @param Eb the posterior mean of b (p vector) (alpha * mu) # @param Eb2 the posterior second moment of b (p vector) (alpha * mu2) SER_posterior_e_loglik_rss = function (R, Sigma, r, Ez, Ez2) { eigenS = attr(Sigma,"eigenS") Dinv = 1/(eigenS$values) Dinv[is.infinite(Dinv)] = 0 rR = R %*% r SinvEz = eigenS$vectors %*% (Dinv * crossprod(eigenS$vectors, Ez)) return(-0.5*(-2*sum(rR*SinvEz) + sum(attr(Sigma,"RjSinvRj") * as.vector(Ez2)))) } susieR/R/single_effect_regression.R0000644000176200001440000002105414102275444017112 0ustar liggesusers#' @rdname single_effect_regression #' #' @title Bayesian single-effect linear regression #' #' @description These methods fit the regression model \eqn{y = Xb + #' e}, where elements of e are \emph{i.i.d.} \eqn{N(0,s^2)}, and b is #' a p-vector of effects to be estimated. The assumption is that b has #' exactly one non-zero element, with all elements equally likely to #' be non-zero. The prior on the coefficient of the non-zero element #' is \eqn{N(0,V)}. #' #' @details \code{single_effect_regression_ss} performs single-effect #' linear regression with summary data, in which only the statistcs #' \eqn{X^Ty} and diagonal elements of \eqn{X^TX} are provided to the #' method. #' #' \code{single_effect_regression_rss} performs single-effect linear #' regression with z scores. That is, this function fits the #' regression model \eqn{z = R*b + e}, where e is \eqn{N(0,Sigma)}, #' \eqn{Sigma = residual_var*R + lambda*I}, and the b is a p-vector of #' effects to be estimated. The assumption is that b has exactly one #' non-zero element, with all elements equally likely to be non-zero. #' The prior on the non-zero element is \eqn{N(0,V)}. The required #' summary data are the p-vector \code{z} and the p by p matrix #' \code{Sigma}. The summary statistics should come from the same #' individuals. #' #' @param y An n-vector. #' #' @param X An n by p matrix of covariates. #' #' @param V A scalar giving the (initial) prior variance #' #' @param residual_variance The residual variance. #' #' @param prior_weights A p-vector of prior weights. #' #' @param optimize_V The optimization method to use for fitting the #' prior variance. #' #' @param check_null_threshold Scalar specifying threshold on the #' log-scale to compare likelihood between current estimate and zero #' the null. #' #' @return A list with the following elements: #' #' \item{alpha}{Vector of posterior inclusion probabilities; #' \code{alpha[i]} is posterior probability that the ith coefficient #' is non-zero.} #' #' \item{mu}{Vector of posterior means (conditional on inclusion).} #' #' \item{mu2}{Vector of posterior second moments (conditional on #' inclusion).} #' #' \item{lbf}{Vector of log-Bayes factors for each variable.} #' #' \item{lbf_model}{Log-Bayes factor for the single effect regression.} #' #' \code{single_effect_regression} and \code{single_effect_regression_ss} #' additionally output: #' #' \item{V}{Prior variance (after optimization if \code{optimize_V != #' "none"}).} #' #' \item{loglik}{The log-likelihood, \eqn{\log p(y | X, V)}.} #' #' @importFrom stats dnorm #' @importFrom stats uniroot #' @importFrom stats optim #' @importFrom Matrix colSums #' #' @keywords internal #' single_effect_regression = function (y, X, V, residual_variance = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0) { optimize_V = match.arg(optimize_V) Xty = compute_Xty(X,y) betahat = (1/attr(X,"d")) * Xty shat2 = residual_variance/attr(X,"d") if (is.null(prior_weights)) prior_weights = rep(1/ncol(X),ncol(X)) if (optimize_V != "EM" && optimize_V != "none") V = optimize_prior_variance(optimize_V,betahat,shat2,prior_weights, alpha = NULL,post_mean2 = NULL,V_init = V, check_null_threshold = check_null_threshold) # log(bf) for each SNP lbf = dnorm(betahat,0,sqrt(V + shat2),log = TRUE) - dnorm(betahat,0,sqrt(shat2),log = TRUE) # Deal with special case of infinite shat2 (e.g., happens if X does # not vary). lbf[is.infinite(shat2)] = 0 maxlbf = max(lbf) # w is proportional to BF, but subtract max for numerical stability. w = exp(lbf - maxlbf) # Posterior prob for each SNP. w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) alpha = w_weighted / weighted_sum_w post_var = (1/V + attr(X,"d")/residual_variance)^(-1) # Posterior variance. post_mean = (1/residual_variance) * post_var * Xty post_mean2 = post_var + post_mean^2 # Second moment. # BF for single effect model. lbf_model = maxlbf + log(weighted_sum_w) loglik = lbf_model + sum(dnorm(y,0,sqrt(residual_variance),log = TRUE)) if(optimize_V == "EM") V = optimize_prior_variance(optimize_V,betahat,shat2,prior_weights, alpha,post_mean2, check_null_threshold = check_null_threshold) return(list(alpha = alpha,mu = post_mean,mu2 = post_mean2,lbf = lbf, lbf_model = lbf_model,V = V,loglik = loglik)) } # Estimate prior variance. est_V_uniroot = function (betahat, shat2, prior_weights) { V.u = uniroot(negloglik.grad.logscale,c(-10,10),extendInt = "upX", betahat = betahat,shat2 = shat2,prior_weights = prior_weights) return(exp(V.u$root)) } optimize_prior_variance = function (optimize_V, betahat, shat2, prior_weights, alpha = NULL, post_mean2 = NULL, V_init = NULL, check_null_threshold = 0) { V = V_init if (optimize_V != "simple") { if(optimize_V == "optim") { lV = optim(par = log(max(c(betahat^2-shat2,1),na.rm = TRUE)), fn = neg.loglik.logscale,betahat = betahat,shat2 = shat2, prior_weights = prior_weights,method = "Brent",lower = -30, upper = 15)$par ## if the estimated one is worse than current one, don't change it. if(neg.loglik.logscale(lV, betahat = betahat,shat2 = shat2,prior_weights = prior_weights) > neg.loglik.logscale(log(V), betahat = betahat, shat2 = shat2,prior_weights = prior_weights)){ lV = log(V) } V = exp(lV) } else if (optimize_V == "uniroot") V = est_V_uniroot(betahat,shat2,prior_weights) else if (optimize_V == "EM") V = sum(alpha * post_mean2) else stop("Invalid option for optimize_V method") } # Set V exactly 0 if that beats the numerical value by # check_null_threshold in loglik. check_null_threshold = 0.1 is # exp(0.1) = 1.1 on likelihood scale; it means that for parsimony # reasons we set estiate of V to zero, if its numerical estimate is # only "negligibly" different from zero. We use a likelihood ratio # of exp(check_null_threshold) to define "negligible" in this # context. This is fairly modest condition compared to, say, a # formal LRT with p-value 0.05. But the idea is to be lenient to # non-zeros estimates unless they are indeed small enough to be # neglible. See more intuition at # https://stephens999.github.io/fiveMinuteStats/LR_and_BF.html if (loglik(0,betahat,shat2,prior_weights) + check_null_threshold >= loglik(V,betahat,shat2,prior_weights)) V = 0 return(V) } # In these functions, s2 represents residual_variance, and shat2 is an # estimate of it. # The log likelihood function for SER model (based on summary data # betahat, shat2) as a function of prior variance V. # #' @importFrom Matrix colSums #' @importFrom stats dnorm loglik = function (V, betahat, shat2, prior_weights) { #log(bf) for each SNP lbf = dnorm(betahat,0,sqrt(V+shat2),log = TRUE) - dnorm(betahat,0,sqrt(shat2),log = TRUE) # Deal with special case of infinite shat2 (e.g., happens if X does # not vary). lbf[is.infinite(shat2)] = 0 maxlbf = max(lbf) w = exp(lbf - maxlbf) # w = BF/BFmax w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) return(log(weighted_sum_w) + maxlbf) } neg.loglik.logscale = function(lV,betahat,shat2,prior_weights) -loglik(exp(lV),betahat,shat2,prior_weights) #' @importFrom Matrix colSums #' @importFrom stats dnorm loglik.grad = function(V, betahat, shat2, prior_weights) { # log(bf) for each SNP. lbf = dnorm(betahat,0,sqrt(V + shat2),log = TRUE) - dnorm(betahat,0,sqrt(shat2),log = TRUE) # Deal with special case of infinite shat2 (e.g., happens if X does # not vary). lbf[is.infinite(shat2)] = 0 maxlbf = max(lbf) w = exp(lbf - maxlbf) # w = BF/BFmax w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) alpha = w_weighted / weighted_sum_w return(sum(alpha * lbf.grad(V,shat2,betahat^2/shat2))) } # Define loglikelihood and gradient as function of lV:=log(V) # to improve numerical optimization negloglik.grad.logscale = function (lV, betahat, shat2, prior_weights) -exp(lV) * loglik.grad(exp(lV),betahat,shat2,prior_weights) # Vector of gradients of logBF_j for each j, with respect to prior # variance V. lbf.grad = function (V, shat2, T2) { l = 0.5*(1/(V + shat2)) * ((shat2/(V + shat2))*T2 - 1) l[is.nan(l)] = 0 return(l) } lbf = function (V, shat2, T2) { l = 0.5*log(shat2/(V + shat2)) + 0.5*T2*(V/(V + shat2)) l[is.nan(l)] = 0 return(l) } susieR/R/summary.susie.R0000644000176200001440000000401614055430120014667 0ustar liggesusers#' @title Summarize Susie Fit. #' #' @description \code{summary} method for the \dQuote{susie} class. #' #' @param object A susie fit. #' #' @param \dots Additional arguments passed to the generic \code{summary} #' or \code{print.summary} method. #' #' @return \code{summary.susie} returns a list containing a data frame #' of variables and a data frame of credible sets. #' #' @method summary susie #' #' @export summary.susie #' #' @export #' summary.susie = function (object, ...) { if (is.null(object$sets)) stop("Cannot summarize SuSiE object because credible set information ", "is not available") variables = data.frame(cbind(1:length(object$pip),object$pip,-1)) colnames(variables) = c("variable","variable_prob","cs") rownames(variables) = NULL if (object$null_index > 0) variables = variables[-object$null_index,] if (!is.null(object$sets$cs)) { cs = data.frame(matrix(NA,length(object$sets$cs),5)) colnames(cs) = c("cs","cs_log10bf","cs_avg_r2","cs_min_r2","variable") for (i in 1:length(object$sets$cs)) { variables$cs[variables$variable %in% object$sets$cs[[i]]] = object$sets$cs_index[[i]] cs$cs[i] = object$sets$cs_index[[i]] cs$cs_log10bf[i] = log10(exp(object$lbf[cs$cs[i]])) cs$cs_avg_r2[i] = object$sets$purity$mean.abs.corr[i]^2 cs$cs_min_r2[i] = object$sets$purity$min.abs.corr[i]^2 cs$variable[i] = paste(object$sets$cs[[i]],collapse=",") } variables = variables[order(variables$variable_prob,decreasing = TRUE),] } else cs = NULL out = list(vars = variables,cs = cs) class(out) = c("summary.susie","list") return(out) } #' @rdname summary.susie #' #' @param x A susie summary. #' #' @method print summary.susie #' #' @export print.summary.susie #' #' @export #' print.summary.susie = function (x, ...) { cat("\nVariables in credible sets:\n\n") print.data.frame(x$vars[which(x$vars$cs > 0),],row.names = FALSE) cat("\nCredible sets summary:\n\n") print.data.frame(x$cs,row.names = FALSE) } susieR/R/trend_filtering_multiplication.R0000644000176200001440000000521413755042336020356 0ustar liggesusers# @title Compute unscaled X %*% b using the special structure of trend # filtering # @param order is the order of trend filtering # @param b an n=p vector # @return an n vector compute_tf_Xb = function(order,b) { for (i in 1:(order+1)) b = rev(-1*cumsum(rev(b))) return(b) } # @title Compute unscaled t(X) %*% y using the special structure of # trend filtering # @param order is the order of trend filtering # @param y an n vector # @return an n vector compute_tf_Xty = function(order, y) { for (i in 1:(order+1)) y = -1*cumsum(y) return(y) } # @title Compute colSums(X*X) for X under four scenarios # @param order is the order of trend filtering # @param n the length of y # @param cm column means of X # @param csd column standard deviations of X # @param intercept a boolean denotes whether mean centering X # @param standardize a boolean denotes whether scaling X by standard deviation # @return an n vector compute_tf_d = function (order, n, cm, csd, standardize = FALSE, intercept = FALSE) { if (intercept) { # When standardize = TRUE, intercept = TRUE: by special # observation d = [n-1, n-1, ...] d = rep(n-1,n) if (order == 0) d[n] = 0 # When standardize = FALSE, intercept = TRUE: # d = [n-1, n-1, ...] * (csd^2) if (!standardize) d = d*csd^2 return(d) } else { # When standardize = FALSE, intercept = FALSE: d = colSums(X^2) base = rep(-1,n) if (order == 0) d = cumsum(base^2) else { for (i in 1:order) base = cumsum(base) d = cumsum(base^2) } # When standardize = TRUE, intercept = TRUE: # d = colSums(X^2) / (csd^2) if (standardize) d = d/csd^2 return(d) } } # @title Compute column mean of the trend filtering matrix X. # @param order is the order of trend filtering # @param n the length of y # @return an n vector compute_tf_cm = function (order, n) { base = rep(1,n) for (i in 1:(order+1)) base = -cumsum(base) return(base/n) } # @title Compute column standard deviation of the trend filtering # matrix X # @param order is the order of trend filtering # @param n is the length of y # @return an n vector compute_tf_csd = function (order, n) { cm = compute_tf_cm(order,n) csd = sqrt((compute_tf_d(order,n)/n - cm^2)*n/(n-1)) csd[which(csd == 0)] = 1 return(csd) } # @title A fast way to compute colSums(X*X), where X is a # mean-centered and standardized trend filtering matrix. # @param order order of trend filtering # @param n the length of y # @return an n vector compute_tf_std_d = function (order, n) { res = rep(n-1,n) if (order == 0) res[n] = 0 return(res) } susieR/R/susie_rss_lambda.R0000644000176200001440000002077714141050376015405 0ustar liggesusers# Performs sum of single-effect (SuSiE) linear regression with z # scores (with lambda). The summary data required are the p by p # correlation matrix R, the p vector z. The summary stats should come # from the same individuals. This function fits the regression model z # = sum_l Rb_l + e, where e is N(0,residual_variance * R + lambda I) # and the sum_l b_l is a p vector of effects to be estimated. The # assumption is that each b_l has exactly one non-zero element, with # all elements equally likely to be non-zero. The prior on the # non-zero element is N(0,var = prior_variance). # #' @importFrom stats optimize susie_rss_lambda = function(z, R, maf = NULL, maf_thresh = 0, L = 10, lambda = 0, prior_variance = 50, residual_variance = NULL, r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim", "EM", "simple"), check_null_threshold = 0, prior_tol = 1e-9, max_iter = 100, s_init = NULL, intercept_value = 0, coverage = 0.95, min_abs_corr = 0.5, tol = 1e-3, verbose = FALSE, track_fit = FALSE, check_R = TRUE, check_z = FALSE) { # Check input R. if (nrow(R) != length(z)) stop(paste0("The dimension of correlation matrix (",nrow(R)," by ", ncol(R),") does not agree with expected (",length(z)," by ", length(z),")")) if (!is_symmetric_matrix(R)) stop("R is not a symmetric matrix") if (!(is.double(R) & is.matrix(R)) & !inherits(R,"CsparseMatrix")) stop("Input R must be a double-precision matrix or a sparse matrix") # MAF filter. if (!is.null(maf)) { if (length(maf) != length(z)) stop(paste0("The length of maf does not agree with expected ",length(z))) id = which(maf > maf_thresh) R = R[id,id] z = z[id] } if (any(is.infinite(z))) stop("z contains infinite values") # Check for NAs in R. if (anyNA(R)) stop("R matrix contains missing values") # Replace NAs in z with zero. if (anyNA(z)) { warning("NA values in z-scores are replaced with 0") z[is.na(z)] = 0 } if (is.numeric(null_weight) && null_weight == 0) null_weight = NULL if (!is.null(null_weight)) { if (!is.numeric(null_weight)) stop("Null weight must be numeric") if (null_weight < 0 || null_weight >= 1) stop("Null weight must be between 0 and 1") if (missing(prior_weights)) prior_weights = c(rep(1/ncol(R)*(1-null_weight),ncol(R)),null_weight) else prior_weights = c(prior_weights * (1-null_weight),null_weight) R = cbind(rbind(R,0),0) z = c(z,0) } # Eigen decomposition for R, filter on eigenvalues. p = ncol(R) attr(R,"eigen") = eigen(R,symmetric = TRUE) if (check_R && any(attr(R,"eigen")$values < -r_tol)) stop(paste0("The correlation matrix (",nrow(R)," by ",ncol(R), "is not a positive semidefinite matrix. ", "The smallest eigenvalue is ",min(attr(R,"eigen")$values), ". You can bypass this by \"check_R = FALSE\" which instead ", "sets negative eigenvalues to 0 to allow for continued ", "computations.")) # Check whether z in space spanned by the non-zero eigenvectors of R. if (check_z) { proj = check_projection(R,z) if (!proj$status) warning("Input z does not lie in the space of non-zero eigenvectors ", "of R.") else message("Input z is in space spanned by the non-zero eigenvectors of ", "R.") } R = set_R_attributes(R,r_tol) if (lambda == "estimate"){ colspace = which(attr(R,"eigen")$values > 0) if (length(colspace) == length(z)) lambda = 0 else { znull = crossprod(attr(R,"eigen")$vectors[,-colspace], z) # U2^T z lambda = sum(znull^2)/length(znull) } } # Initialize susie fit. s = init_setup_rss(p,L,prior_variance,residual_variance,prior_weights, null_weight) if (!missing(s_init) && !is.null(s_init)) { if (!inherits(s_init,"susie")) stop("s_init should be a susie object") if (max(s_init$alpha) > 1 || min(s_init$alpha) < 0) stop("s_init$alpha has invalid values outside range [0,1]; please ", "check your input") # First, remove effects with s_init$V = 0 s_init = susie_prune_single_effects(s_init) num_effects = nrow(s_init$alpha) if(missing(L)){ L = num_effects }else if(L < num_effects){ warning(paste("Specified number of effects L =",L, "is smaller than the number of effects",num_effects, "in input SuSiE model. It will have",num_effects,"effects.")) L = num_effects } # expand s_init if L > num_effects. s_init = susie_prune_single_effects(s_init, L, s$V) s = modifyList(s,s_init) s = init_finalize_rss(s,R = R) } else s = init_finalize_rss(s) s$sigma2 = s$sigma2 - lambda estimate_prior_method = match.arg(estimate_prior_method) # Intialize elbo to NA. elbo = rep(NA,max_iter+1) elbo[1] = -Inf; tracking = list() attr(R,"lambda") = lambda Sigma = update_Sigma(R,s$sigma2,z) # sigma2*R + lambda*I for (i in 1:max_iter) { if (track_fit) tracking[[i]] = susie_slim(s) s = update_each_effect_rss(R,z,s,Sigma,estimate_prior_variance, estimate_prior_method,check_null_threshold) if (verbose) print(paste0("before estimate sigma2 objective:", get_objective_rss(R,z,s))) # Compute objective before updating residual variance because part # of the objective s$kl has already been computed under the # residual variance, before the update. elbo[i+1] = get_objective_rss(R,z,s) if ((elbo[i+1] - elbo[i]) < tol) { s$converged = TRUE break } if (estimate_residual_variance) { if (lambda == 0) { est_sigma2 = (1/sum(attr(R,"eigen")$values != 0))*get_ER2_rss(1,R,z,s) if (est_sigma2 < 0) stop("Estimating residual variance failed: the estimated value ", "is negative") if (est_sigma2 > 1) est_sigma2 = 1 } else { est_sigma2 = optimize(Eloglik_rss, interval = c(1e-4, 1-lambda), R = R, z = z, s = s, maximum = TRUE)$maximum if(Eloglik_rss(est_sigma2, R, z, s) < Eloglik_rss(1-lambda, R, z, s)){ est_sigma2 = 1-lambda } } s$sigma2 = est_sigma2 if (verbose) print(paste0("after estimate sigma2 objective:", get_objective_rss(R,z,s))) Sigma = update_Sigma(R,s$sigma2,z) } } # Remove first (infinite) entry, and trailing NAs. elbo = elbo[2:(i+1)] s$elbo = elbo s$niter = i s$lambda = lambda if (is.null(s$converged)) { warning(paste("IBSS algorithm did not converge in",max_iter,"iterations!")) s$converged = FALSE } s$intercept = intercept_value s$fitted = s$Rz s$X_column_scale_factors = attr(R,"scaled:scale") if (track_fit) s$trace = tracking # SuSiE CS and PIP. if (!is.null(coverage) && !is.null(min_abs_corr)) { R = muffled_cov2cor(R) s$sets = susie_get_cs(s,coverage = coverage,Xcorr = R, min_abs_corr = min_abs_corr) s$pip = susie_get_pip(s,prune_by_cs = FALSE,prior_tol = prior_tol) } if (!is.null(names(z))) { variable_names = names(z) if (!is.null(null_weight)) variable_names = c("null",variable_names) colnames(s$alpha) = variable_names colnames(s$mu) = variable_names colnames(s$mu2) = variable_names colnames(s$lbf_variable) = variable_names names(s$pip) = variable_names } return(s) } update_Sigma = function (R, sigma2, z) { Sigma = sigma2*R + attr(R,"lambda") * diag(length(z)) eigenS = attr(R,"eigen") eigenS$values = sigma2 * eigenS$values + attr(R,"lambda") Dinv = 1/(eigenS$values) Dinv[is.infinite(Dinv)] = 0 attr(Sigma,"eigenS") = eigenS # Sigma^(-1) R_j = U (sigma2 D + lambda)^(-1) D U^T e_j attr(Sigma,"SinvRj") = eigenS$vectors %*% (Dinv * attr(R,"eigen")$values * t(eigenS$vectors)) if (attr(R,"lambda") == 0) attr(Sigma,"RjSinvRj") = attr(R,"d")/sigma2 else { tmp = t(eigenS$vectors) attr(Sigma,"RjSinvRj") = colSums(tmp * (Dinv*(attr(R,"eigen")$values^2) * tmp)) } return(Sigma) } susieR/R/single_effect_regression_rss.R0000644000176200001440000001065214033741524020003 0ustar liggesusers#' @rdname single_effect_regression #' #' @param z A p-vector of z scores. #' #' @param Sigma \code{residual_var*R + lambda*I} #' #' @keywords internal #' single_effect_regression_rss = function (z, Sigma, V = 1, prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", "EM", "simple"), check_null_threshold = 0) { p = length(z) shat2 = 1/attr(Sigma,"RjSinvRj") if (is.null(prior_weights)) prior_weights = rep(1/p,p) if (optimize_V != "EM" && optimize_V != "none") V = optimize_prior_variance_rss(optimize_V,z,Sigma,prior_weights, alpha = NULL,post_mean2 = NULL,V_init = V, check_null_threshold=check_null_threshold) lbf = sapply(1:p, function(j) -0.5 * log(1 + (V/shat2[j])) + 0.5 * (V/(1 + (V/shat2[j]))) * sum(attr(Sigma,"SinvRj")[,j] * z)^2 ) # Deal with special case of infinite shat2 (e.g., happens if X does not # vary). lbf[is.infinite(shat2)] = 0 # w is proportional to BF, but subtract max for numerical stability. maxlbf = max(lbf) w = exp(lbf-maxlbf) # posterior prob on each SNP w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) alpha = w_weighted / weighted_sum_w post_var = (attr(Sigma,"RjSinvRj") + 1/V)^(-1) # Posterior variance. post_mean = sapply(1:p,function(j) (post_var[j]) * sum(attr(Sigma,"SinvRj")[,j] * z)) post_mean2 = post_var + post_mean^2 # Second moment. lbf_model = maxlbf + log(weighted_sum_w) # Analogue of loglik in the # non-summary case. if (optimize_V=="EM") V = optimize_prior_variance_rss(optimize_V,z,Sigma,prior_weights, alpha,post_mean2,check_null_threshold = check_null_threshold) return(list(alpha = alpha,mu = post_mean,mu2 = post_mean2,lbf = lbf, V = V,lbf_model = lbf_model)) } loglik_rss = function (V, z, Sigma, prior_weights) { p = length(z) shat2 = 1/attr(Sigma,"RjSinvRj") # log(bf) for each SNP. lbf = sapply(1:p,function (j) -0.5 * log(1 + (V/shat2[j])) + 0.5 * (V/(1 + (V/shat2[j]))) * sum(attr(Sigma,"SinvRj")[,j] * z)^2) # Deal with special case of infinite shat2 (e.g., happens if X does # not vary). lbf[is.infinite(shat2)] = 0 maxlbf = max(lbf) w = exp(lbf-maxlbf) # w = BF/BFmax w_weighted = w * prior_weights weighted_sum_w = sum(w_weighted) return(log(weighted_sum_w) + maxlbf) } neg.loglik_z.logscale_rss = function (lV, z, Sigma, prior_weights) -loglik_rss(exp(lV),z,Sigma,prior_weights) optimize_prior_variance_rss = function (optimize_V, z, Sigma, prior_weights, alpha = NULL, post_mean2 = NULL, V_init = NULL, check_null_threshold = 0) { V = V_init if (optimize_V != "simple") { if(optimize_V == "optim") { lV = optim(par = log(max(c((colSums(attr(Sigma,"SinvRj") * z)^2) - (1/attr(Sigma,"RjSinvRj")),1e-6),na.rm = TRUE)), fn = neg.loglik_z.logscale_rss,z = z,Sigma = Sigma, prior_weights = prior_weights,method = "Brent", lower = -30,upper = 15)$par ## if the estimated one is worse than current one, don't change it. if(neg.loglik_z.logscale_rss(lV, z = z,Sigma = Sigma,prior_weights = prior_weights) > neg.loglik_z.logscale_rss(log(V), z = z,Sigma = Sigma,prior_weights = prior_weights)){ lV = log(V) } V = exp(lV) } else if (optimize_V == "EM") V = sum(alpha * post_mean2) else stop("Invalid option for optimize_V") } # Set V exactly 0 if that beats the numerical value. By # check_null_threshold in loglik. check_null_threshold = 0.1 is # exp(0.1) = 1.1 on likelihood scale; it means that for parsimony # reasons we set estiate of V to zero, if its numerical estimate is # only "negligibly" different from zero. We use a likelihood ratio # of exp(check_null_threshold) to define "negligible" in this # context. This is fairly modest condition compared to, say, a # formal LRT with p-value 0.05. But the idea is to be lenient to # non-zeros estimates unless they are indeed small enough to be # neglible. See more intuition at # https://stephens999.github.io/fiveMinuteStats/LR_and_BF.html if (loglik_rss(0,z,Sigma,prior_weights) + check_null_threshold >= loglik_rss(V,z,Sigma,prior_weights)) V = 0 return(V) } susieR/R/update_each_effect_ss.R0000644000176200001440000000337413755042336016352 0ustar liggesusers# @title update each effect once # @param XtX a p by p matrix, X'X # @param Xty a p vector # @param s_init a list with elements sigma2, V, alpha, mu, Xr # @param estimate_prior_variance boolean indicating whether to # estimate prior variance # @param estimate_prior_method The method used for estimating prior # variance, 'optim' or 'EM'. # @param check_null_threshold float a threshold on the log scale to # compare likelihood between current estimate and zero the null # #' @importFrom Matrix diag update_each_effect_ss = function (XtX, Xty, s_init, estimate_prior_variance = FALSE, estimate_prior_method = "optim", check_null_threshold = 0) { if (!estimate_prior_variance) estimate_prior_method = "none" # Repeat for each effect to update. s = s_init L = nrow(s$alpha) if (L > 0) { for (l in 1:L) { # Remove lth effect from fitted values. s$XtXr = s$XtXr - XtX %*% (s$alpha[l,] * s$mu[l,]) # Compute residuals. XtR = Xty - s$XtXr res = single_effect_regression_ss(as.matrix(XtR),attr(XtX,"d"),s$V[l], s$sigma2,s$pi,estimate_prior_method,check_null_threshold) # Update the variational estimate of the posterior mean. s$mu[l,] = res$mu s$alpha[l,] = res$alpha s$mu2[l,] = res$mu2 s$V[l] = res$V s$lbf[l] = res$lbf_model s$lbf_variable[l,] = res$lbf s$KL[l] = -res$lbf_model + SER_posterior_e_loglik_ss(attr(XtX,"d"),XtR,s$sigma2, res$alpha * res$mu,res$alpha * res$mu2) s$XtXr = s$XtXr + XtX %*% (s$alpha[l,] * s$mu[l,]) } } s$XtXr = unname(as.matrix(s$XtXr)) return(s) } susieR/R/susie_zzz_auto.R0000644000176200001440000001267114075324172015161 0ustar liggesusers#' @title Attempt at Automating SuSiE for Hard Problems #' #' @description \code{susie_auto} is an attempt to automate reliable #' running of susie even on hard problems. It implements a three-stage #' strategy for each L: first, fit susie with very small residual #' error; next, estimate residual error; finally, estimate the prior #' variance. If the last step estimates some prior variances to be #' zero, stop. Otherwise, double L, and repeat. Initial runs are #' performed with relaxed tolerance; the final run is performed using #' the default susie tolerance. #' #' @param X An n by p matrix of covariates. #' #' @param y The observed responses, a vector of length n. #' #' @param L_init The initial value of L. #' #' @param L_max The largest value of L to consider. #' #' @param verbose If \code{verbose = TRUE}, the algorithm's progress, #' and a summary of the optimization settings, are printed to the #' console. #' #' @param init_tol The tolerance to passed to \code{susie} during #' early runs (set large to shorten the initial runs). #' #' @param standardize If \code{standardize = TRUE}, standardize the #' columns of X to unit variance prior to fitting. Note that #' \code{scaled_prior_variance} specifies the prior on the #' coefficients of X \emph{after} standardization (if it is #' performed). If you do not standardize, you may need to think more #' carefully about specifying \code{scaled_prior_variance}. Whatever #' your choice, the coefficients returned by \code{coef} are given for #' \code{X} on the original input scale. Any column of \code{X} that #' has zero variance is not standardized. #' #' @param intercept If \code{intercept = TRUE}, the intercept is #' fitted; it \code{intercept = FALSE}, the intercept is set to #' zero. Setting \code{intercept = FALSE} is generally not #' recommended. #' #' @param max_iter Maximum number of IBSS iterations to perform. #' #' @param tol A small, non-negative number specifying the convergence #' tolerance for the IBSS fitting procedure. The fitting procedure #' will halt when the difference in the variational lower bound, or #' \dQuote{ELBO} (the objective function to be maximized), is #' less than \code{tol}. #' #' @param \dots Additional arguments passed to \code{\link{susie}}. #' #' @return See \code{\link{susie}} for a description of return values. #' #' @seealso \code{\link{susie}} #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' res = susie_auto(X,y) #' plot(beta,coef(res)[-1]) #' abline(a = 0,b = 1,col = "skyblue",lty = "dashed") #' plot(y,predict(res)) #' abline(a = 0,b = 1,col = "skyblue",lty = "dashed") #' #' @importFrom stats sd #' #' @export #' susie_auto = function (X, y, L_init = 1, L_max = 512, verbose = FALSE, init_tol = 1, standardize = TRUE, intercept = TRUE, max_iter = 100,tol = 1e-2, ...) { L = L_init if (verbose) message(paste0("Trying L=",L)) s.0 = susie(X,y,L = L,residual_variance = 0.01*sd(y)^2,tol = init_tol, scaled_prior_variance = 1,estimate_residual_variance = FALSE, estimate_prior_variance = FALSE,standardize = standardize, intercept = intercept,max_iter = max_iter,...) s.1 = susie(X,y,s_init = s.0,tol = init_tol, estimate_residual_variance = TRUE, estimate_prior_variance = FALSE, standardize = standardize,intercept = intercept, max_iter = max_iter,...) s.2 = susie(X,y,s_init = s.1,tol = init_tol, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, standardize = standardize,intercept = intercept, max_iter = max_iter,...) # We call it converged---i.e., L is "big enough"---if there are any # prior variances set to zero. converged = !all(s.2$V > 0) while (!converged & (L <= L_max)) { for (i in 1:L) { s.2 = add_null_effect(s.2,1) # Add in L more effects. s.2$sigma2 = 0.01*sd(y)^2 # Set residual variance to be small # again for next iteration. } L = 2*L if (verbose) message(paste0("Trying L=",L)) s.0 = susie(X,y,s_init = s.2,tol = init_tol, estimate_residual_variance = FALSE, estimate_prior_variance = FALSE, standardize = standardize,intercept = intercept, max_iter = max_iter,...) s.1 = susie(X,y,s_init = s.0,tol = init_tol, estimate_residual_variance = TRUE, estimate_prior_variance = FALSE, standardize = standardize,intercept = intercept, max_iter = max_iter,...) s.2 = susie(X,y,s_init = s.1,tol = init_tol, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, standardize = standardize,intercept = intercept, max_iter = max_iter,...) # We call it converged---i.e., L is "big enough"---if there are # any prior variances set to zero. converged = !all(s.2$V > 0) } # Final run at default tolerance to improve fit. s.2 = susie(X,y,s_init = s.2,estimate_residual_variance = TRUE, estimate_prior_variance = TRUE,tol = tol, standardize = standardize,intercept = intercept, max_iter = max_iter,...) return(s.2) } susieR/R/update_each_effect.R0000644000176200001440000000277714075324172015650 0ustar liggesusers# @title update each effect once # @param X an n by p matrix of regressor variables # @param y an n vector of response variable # @param s a SuSiE fit # @param estimate_prior_variance boolean indicating whether to # estimate prior variance # @param check_null_threshold float a threshold on the log scale to # compare likelihood between current estimate and zero the null update_each_effect = function (X, y, s, estimate_prior_variance = FALSE, estimate_prior_method = "optim", check_null_threshold) { if (!estimate_prior_variance) estimate_prior_method = "none" # Repeat for each effect to update. L = nrow(s$alpha) if (L > 0) for (l in 1:L) { # Remove lth effect from fitted values. s$Xr = s$Xr - compute_Xb(X,s$alpha[l,] * s$mu[l,]) # Compute residuals. R = y - s$Xr res = single_effect_regression(R,X,s$V[l],s$sigma2,s$pi, estimate_prior_method, check_null_threshold) # Update the variational estimate of the posterior mean. s$mu[l,] = res$mu s$alpha[l,] = res$alpha s$mu2[l,] = res$mu2 s$V[l] = res$V s$lbf[l] = res$lbf_model s$lbf_variable[l,] = res$lbf s$KL[l] = -res$loglik + SER_posterior_e_loglik(X,R,s$sigma2,res$alpha * res$mu, res$alpha * res$mu2) s$Xr = s$Xr + compute_Xb(X,s$alpha[l,] * s$mu[l,]) } return(s) } susieR/R/elbo_ss.R0000644000176200001440000000235513703336624013512 0ustar liggesusers# @title Get objective function from data and susie fit object. # @param XtX a p by p matrix, X'X # @param Xty a p vector, X'y, # @param s a susie fit object # @param yty a scaler, y'y, where y is centered to have mean 0 # @param n sample size get_objective_ss = function (XtX, Xty, s, yty, n) Eloglik_ss(XtX,Xty,s,yty,n) - sum(s$KL) # Expected loglikelihood for a susie fit. Eloglik_ss = function (XtX, Xty, s, yty, n) -n/2*log(2*pi*s$sigma2) - 1/(2*s$sigma2) * get_ER2_ss(XtX,Xty,s,yty) # Expected squared residuals. get_ER2_ss = function (XtX, Xty, s, yty) { B = s$alpha * s$mu XB2 = sum((B %*% XtX) * B) betabar = colSums(B) d = attr(XtX,"d") postb2 = s$alpha * s$mu2 # Posterior second moment. return(yty - 2*sum(betabar * Xty) + sum(betabar * (XtX %*% betabar)) - XB2 + sum(d * t(postb2))) } # @title posterior expected loglikelihood for a single effect regression # @param dXtX a p vector of diagonal elements of XtX # @param Xty a p vector # @param s2 the residual variance # @param Eb the posterior mean of b (p vector) (alpha * mu) # @param Eb2 the posterior second moment of b (p vector) (alpha * mu2) SER_posterior_e_loglik_ss = function (dXtX, Xty, s2, Eb, Eb2) -0.5/s2 * (-2*sum(Eb*Xty) + sum(dXtX * as.vector(Eb2))) susieR/R/susie_trendfilter.R0000644000176200001440000001043514055444232015610 0ustar liggesusers#' @title Apply susie to trend filtering (especially changepoint #' problems), a type of non-parametric regression. #' #' @description Fits the non-parametric Gaussian regression model #' \eqn{y = mu + e}, where the mean \eqn{mu} is modelled as \eqn{mu = #' Xb}, X is a matrix with columns containing an appropriate basis, #' and b is vector with a (sparse) SuSiE prior. In particular, when #' \code{order = 0}, the jth column of X is a vector with the first j #' elements equal to zero, and the remaining elements equal to 1, so #' that \eqn{b_j} corresponds to the change in the mean of y between #' indices j and j+1. For background on trend filtering, see #' Tibshirani (2014). See also the "Trend filtering" vignette, #' \code{vignette("trend_filtering")}. #' #' @details This implementation exploits the special structure of X, #' which means that the matrix-vector product \eqn{X^Ty} is fast to #' compute; in particular, the computation time is \eqn{O(n)} rather #' than \eqn{O(n^2)} if \code{X} were formed explicitly. For #' implementation details, see the "Implementation of SuSiE trend #' filtering" vignette by running #' \code{vignette("trendfiltering_derivations")}. #' #' @param y An n-vector of observations ordered in time or space #' (assumed to be equally spaced). #' #' @param order An integer specifying the order of trend filtering. #' The default, \code{order = 0}, corresponds to "changepoint" #' problems (\emph{i.e.}, piecewise constant \eqn{mu}). Although #' \code{order > 0} is implemented, we do not recommend its use; in #' practice, we have found problems with convergence of the algorithm #' to poor local optima, producing unreliable inferences. #' #' @param standardize Logical indicating whether to standardize the X #' variables ("basis functions"); \code{standardize = FALSE} is #' recommended as these basis functions already have a natural scale. #' #' @param use_mad Logical indicating whether to use the "median #' absolute deviation" (MAD) method to the estimate residual #' variance. If \code{use_mad = TRUE}, susie is run twice, first by #' fixing the residual variance to the MAD value, then a second time, #' initialized to the first fit, but with residual variance estimated #' the usual way (by maximizing the ELBO). We have found this strategy #' typically improves reliability of the results by reducing a #' tendency to converge to poor local optima of the ELBO. #' #' @param ... Other arguments passed to \code{\link{susie}}. #' #' @return A "susie" fit; see \code{\link{susie}} for details. #' #' @references R. J. Tibshirani (2014). Adaptive piecewise polynomial #' estimation via trend filtering. \emph{Annals of Statistics} #' \bold{42}, 285-323. #' #' @examples #' set.seed(1) #' mu = c(rep(0,50),rep(1,50),rep(3,50),rep(-2,50),rep(0,200)) #' y = mu + rnorm(400) #' s = susie_trendfilter(y) #' plot(y) #' lines(mu,col = 1,lwd = 3) #' lines(predict(s),col = 2,lwd = 2) #' #' # Calculate credible sets (indices of y that occur just before #' # changepoints). #' susie_get_cs(s) #' #' # Plot with credible sets for changepoints. #' susie_plot_changepoint(s,y) #' #' @importFrom Matrix sparseMatrix #' #' @export #' susie_trendfilter = function (y, order = 0, standardize = FALSE, use_mad = TRUE, ...) { if (order > 0) warning("order > 0 is not recommended") n = length(y) X = sparseMatrix(i = NULL,j = NULL,dims = c(n,n)) attr(X,"matrix.type") = "tfmatrix" attr(X,"order") = order if (use_mad && !("s_init" %in% names(list(...)))) { mad = estimate_mad_residual_variance(y) s_mad_init = suppressWarnings(susie(X = X,y = y,standardize = standardize, estimate_residual_variance = FALSE,residual_variance = mad,...)) s = susie(X = X,y = y,standardize = standardize, s_init = s_mad_init,...) } else s = susie(X = X,y = y,standardize = standardize,...) return(s) } # @title estimate residual variance using MAD estimator # @param y an n-vector # @return a scalar of estimated residual variance estimate_mad_residual_variance = function (y) { sigma2 = 0.5*(median(abs(diff(y))/0.6745)^2) if (sigma2 == 0) { stop("Cannot use median absolute deviation (MAD) to initialize residual variance because MAD = 0 for the input data. Please set 'use_mad = FALSE'") } return(sigma2) } susieR/R/susie_ss.R0000644000176200001440000003614614142636237013727 0ustar liggesusers#' @rdname susie #' #' @param bhat A p-vector of estimated effects. #' #' @param shat A p-vector of standard errors. #' #' @param R A p by p correlation matrix. It should be estimated from #' the same samples used to compute \code{bhat} and \code{shat}. Using #' an out-of-sample matrix may produce unreliable results. #' #' @param n The sample size. #' #' @param var_y The sample variance of y, defined as \eqn{y'y/(n-1)}. #' When the sample variance cannot be provided, the coefficients #' (returned from \code{coef}) are computed on the "standardized" X, y #' scale. #' #' @param XtX A p by p matrix \eqn{X'X} in which the columns of X #' are centered to have mean zero. #' #' @param Xty A p-vector \eqn{X'y} in which y and the columns of X are #' centered to have mean zero. #' #' @param yty A scalar \eqn{y'y} in which y is centered to have mean #' zero. #' #' @param X_colmeans A p-vector of column means of \code{X}. If both #' \code{X_colmeans} and \code{y_mean} are provided, the intercept #' is estimated; otherwise, the intercept is NA. #' #' @param y_mean A scalar containing the mean of \code{y}. If both #' \code{X_colmeans} and \code{y_mean} are provided, the intercept #' is estimated; otherwise, the intercept is NA. #' #' @param maf Minor allele frequency; to be used along with #' \code{maf_thresh} to filter input summary statistics. #' #' @param maf_thresh Variants having a minor allele frequency smaller #' than this threshold are not used. #' #' @param r_tol Tolerance level for eigenvalue check of positive #' semidefinite matrix of R. #' #' @param check_input If \code{check_input = TRUE}, #' \code{susie_suff_stat} performs additional checks on \code{XtX} and #' \code{Xty}. The checks are: (1) check that \code{XtX} is positive #' semidefinite; (2) check that \code{Xty} is in the space spanned by #' the non-zero eigenvectors of \code{XtX}. #' #' @param check_prior If \code{check_prior = TRUE}, it checks if the #' estimated prior variance becomes unreasonably large (comparing with #' 10 * max(abs(z))^2). #' #' @param n_purity Passed as argument \code{n_purity} to #' \code{\link{susie_get_cs}}. #' #' @importFrom crayon red magenta #' #' @export #' susie_suff_stat = function (bhat, shat, R, n, var_y, XtX, Xty, yty, X_colmeans = NA, y_mean = NA, maf = NULL, maf_thresh = 0, L = 10, scaled_prior_variance = 0.2, residual_variance = NULL, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim","EM","simple"), check_null_threshold = 0, prior_tol = 1e-9, r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, standardize = TRUE, max_iter = 100, s_init = NULL, coverage = 0.95, min_abs_corr = 0.5, tol = 1e-3, verbose = FALSE, track_fit = FALSE, check_input = FALSE, refine = FALSE, check_prior = FALSE, n_purity = 100) { # Process input estimate_prior_method. estimate_prior_method = match.arg(estimate_prior_method) if (missing(n)) stop("n must be provided") if (any(n <= 1)) stop("n must be more than 1") # Check sufficient statistics. missing_bhat = c(missing(bhat), missing(shat), missing(R)) missing_XtX = c(missing(XtX), missing(Xty), missing(yty)) if (all(missing_bhat) & all(missing_XtX)) stop("Please provide either all of bhat, shat, R, n, var_y or all of ", "XtX, Xty, yty, n") if (any(missing_bhat) & any(missing_XtX)) stop("Please provide either all of bhat, shat, R, n, var_y or all of ", "XtX, Xty, yty, n") if (all(missing_bhat) & any(missing_XtX)) stop("Please provide all of XtX, Xty, yty, n") if (all(missing_XtX) & any(missing_bhat)) stop("Please provide all of bhat, shat, R, n, var_y") if ((!any(missing_XtX)) & (!all(missing_bhat))) warning("Only using information from XtX, Xty, yty, n") if (!any(missing_bhat)) { if (!all(missing_XtX)) warning("Only using information from bhat, shat, R, n, var_y") # Compute XtX, Xty, yty from bhat, shat, R, n, var_y. if (length(shat) == 1) shat = rep(shat,length(bhat)) if (length(bhat) != length(shat)) stop("The length of bhat does not agree with length of shat") if (anyNA(bhat) || anyNA(shat)) stop("The input summary statistics have missing values") if (any(shat == 0)) stop("shat contains one or more zeros") that = bhat/shat that[is.na(that)] = 0 R2 = that^2/(that^2 + n-2) sigma2 = (n-1)*(1-R2)/(n-2) # Convert any input R to correlation matrix. # If R has 0 colums and rows, cov2cor produces NaN and warning. X0 = diag(R) == 0 R = muffled_cov2cor(R) # Change the columns and rows with NaN to 0. if (sum(X0) > 0) R[X0,] = R[,X0] = 0 if (missing(var_y)) { XtX = (n-1)*R Xty = sqrt(sigma2) * sqrt(n-1) * that var_y = 1 } else { XtXdiag = var_y * sigma2/(shat^2) Xty = that * var_y * sigma2/shat XtX = R XtX = R * sqrt(XtXdiag) XtX = t(XtX) XtX = XtX * sqrt(XtXdiag) XtX = XtX + t(XtX) XtX = XtX/2 } yty = var_y * (n-1) } if (ncol(XtX) > 1000 & !requireNamespace("Rfast",quietly = TRUE)) message(magenta("For large R or large XtX, consider installing the ", "Rfast package for better performance.")) # Check input XtX. if (ncol(XtX) != length(Xty)) stop(paste0("The dimension of XtX (",nrow(XtX)," by ",ncol(XtX), ") does not agree with expected (",length(Xty)," by ", length(Xty),")")) if (!is_symmetric_matrix(XtX)) { message(red("XtX is not symmetric; forcing XtX to be symmetric by ", "replacing XtX with (XtX + t(XtX))/2")) XtX = XtX + t(XtX) XtX = XtX/2 } # MAF filter. if (!is.null(maf)) { if (length(maf) != length(Xty)) stop(paste("The length of maf does not agree with expected",length(Xty))) id = which(maf > maf_thresh) XtX = XtX[id,id] Xty = Xty[id] } if (any(is.infinite(Xty))) stop("Input Xty or zscores contains infinite values") if (!(is.double(XtX) & is.matrix(XtX)) & !inherits(XtX,"CsparseMatrix")) stop("Input XtX or R must be a double-precision matrix, or a sparse matrix") if (anyNA(XtX)) stop("Input XtX or R matrix contains NAs") # Replace NAs in z with zeros. if (anyNA(Xty)) { warning("NA values in Xty or z-scores are replaced with 0") Xty[is.na(Xty)] = 0 } if (check_input) { # Check whether XtX is positive semidefinite. semi_pd = check_semi_pd(XtX,r_tol) if (!semi_pd$status) stop("XtX is not a positive semidefinite matrix") # Check whether Xty in space spanned by the non-zero eigenvectors of XtX proj = check_projection(semi_pd$matrix,Xty) if (!proj$status) warning("Xty does not lie in the space of the non-zero eigenvectors ", "of XtX") } if (is.numeric(null_weight) && null_weight == 0) null_weight = NULL if (!is.null(null_weight)) { if (!is.numeric(null_weight)) stop("Null weight must be numeric") if (null_weight < 0 || null_weight >= 1) stop("Null weight must be between 0 and 1") if (is.null(prior_weights)) prior_weights = c(rep(1/ncol(XtX)*(1-null_weight),ncol(XtX)),null_weight) else prior_weights = c(prior_weights*(1 - null_weight),null_weight) XtX = cbind(rbind(XtX,0),0) Xty = c(Xty,0) } p = ncol(XtX) if (standardize) { dXtX = diag(XtX) csd = sqrt(dXtX/(n-1)) csd[csd == 0] = 1 XtX = (1/csd) * XtX XtX = t(XtX) XtX = XtX / csd Xty = Xty / csd } else csd = rep(1,length = p) attr(XtX,"d") = diag(XtX) attr(XtX,"scaled:scale") = csd # Check that X_colmeans has length 1 or p. if (length(X_colmeans) == 1) X_colmeans = rep(X_colmeans,p) if (length(X_colmeans) != p) stop("The length of X_colmeans does not agree with number of variables") # Initialize susie fit. s = init_setup(0,p,L,scaled_prior_variance,residual_variance,prior_weights, null_weight,yty/(n-1),standardize) s$Xr = NULL s$XtXr = rep(0,p) if (!missing(s_init)&& !is.null(s_init)) { if (!inherits(s_init,"susie")) stop("s_init should be a susie object") if (max(s_init$alpha) > 1 || min(s_init$alpha) < 0) stop("s_init$alpha has invalid values outside range [0,1]; please ", "check your input") # First, remove effects with s_init$V = 0 s_init = susie_prune_single_effects(s_init) num_effects = nrow(s_init$alpha) if(missing(L)){ # if L is not specified, we set it as in s_init. L = num_effects }else if(min(p, L) < num_effects){ warning(paste("Specified number of effects L =",min(p, L), "is smaller than the number of effects",num_effects, "in input SuSiE model. The initialized SuSiE model will have", num_effects,"effects.")) L = num_effects } # expand s_init if L > num_effects. s_init = susie_prune_single_effects(s_init, min(p, L), s$V) s = modifyList(s,s_init) s = init_finalize(s,X = XtX) s$XtXr = s$Xr s$Xr = NULL } else s = init_finalize(s) # Initialize elbo to NA. elbo = rep(as.numeric(NA),max_iter + 1) elbo[1] = -Inf; tracking = list() bhat = (1/attr(XtX,"d")) * Xty shat = sqrt(s$sigma2/attr(XtX,"d")) z = bhat/shat zm = max(abs(z[!is.nan(z)])) for (i in 1:max_iter) { if (track_fit) tracking[[i]] = susie_slim(s) s = update_each_effect_ss(XtX,Xty,s,estimate_prior_variance, estimate_prior_method,check_null_threshold) if(check_prior){ if(any(s$V > 100*(zm^2))){ stop('The estimated prior variance is unreasonably large. Please check the input.') } } if (verbose) print(paste0("objective:",get_objective_ss(XtX,Xty,s,yty,n))) # Compute objective before updating residual variance because part # of the objective s$kl has already been computed under the # residual variance before the update. elbo[i+1] = get_objective_ss(XtX,Xty,s,yty,n) if(is.infinite(elbo[i+1])){ stop('The objective becomes infinite. Please check the input.') } if ((elbo[i+1] - elbo[i]) < tol) { s$converged = TRUE break } if (estimate_residual_variance) { est_sigma2 = estimate_residual_variance_ss(XtX,Xty,s,yty,n) if (est_sigma2 < 0) stop("Estimating residual variance failed: the estimated value ", "is negative") s$sigma2 = est_sigma2 if (verbose) print(paste0("objective:",get_objective_ss(XtX,Xty,s,yty,n))) } } elbo = elbo[2:(i+1)] # Remove first (infinite) entry, and trailing NAs. s$elbo = elbo s$niter = i if (is.null(s$converged)) { warning(paste("IBSS algorithm did not converge in",max_iter,"iterations! Please check consistency between summary statistics and LD matrix. See https://stephenslab.github.io/susieR/articles/susierss_diagnostic.html")) s$converged = FALSE } s$X_column_scale_factors = attr(XtX,"scaled:scale") # Compute intercept. s$intercept = y_mean - sum(X_colmeans * (colSums(s$alpha * s$mu)/s$X_column_scale_factors)) if (track_fit) s$trace = tracking # SuSiE CS and PIP. if (!is.null(coverage) && !is.null(min_abs_corr)) { if(any(!(diag(XtX) %in% c(0,1)))){ s$sets = susie_get_cs(s,coverage = coverage,Xcorr = muffled_cov2cor(XtX), min_abs_corr = min_abs_corr, check_symmetric = FALSE, n_purity = n_purity) }else s$sets = susie_get_cs(s,coverage = coverage,Xcorr = XtX, min_abs_corr = min_abs_corr, check_symmetric = FALSE, n_purity = n_purity) s$pip = susie_get_pip(s,prune_by_cs = FALSE,prior_tol = prior_tol) } if (!is.null(colnames(XtX))) { variable_names = colnames(XtX) if (!is.null(null_weight)) { variable_names[length(variable_names)] = "null" names(s$pip) = variable_names[-p] } else names(s$pip) = variable_names colnames(s$alpha) = variable_names colnames(s$mu) = variable_names colnames(s$mu2) = variable_names colnames(s$lbf_variable) = variable_names } if (refine) { if (!missing(s_init) && !is.null(s_init)) warning("The given s_init is not used in refinement") if (!is.null(null_weight) && null_weight != 0) { ## if null_weight is specified ## we remove the extra 0 column XtX = XtX[1:(p-1), 1:(p-1)] Xty = Xty[1:(p-1)] pw_s = s$pi[-s$null_index]/(1 - null_weight) } else pw_s = s$pi conti = TRUE while (conti & length(s$sets$cs)>0) { m = list() for(cs in 1:length(s$sets$cs)) { pw_cs = pw_s pw_cs[s$sets$cs[[cs]]] = 0 if(all(pw_cs == 0)){ break } s2 = susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n, L = L, X_colmeans = X_colmeans, y_mean = y_mean, prior_weights = pw_cs, s_init = NULL, scaled_prior_variance = scaled_prior_variance, residual_variance = residual_variance, estimate_residual_variance = estimate_residual_variance, estimate_prior_variance = estimate_prior_variance, estimate_prior_method = estimate_prior_method, check_null_threshold = check_null_threshold, prior_tol = prior_tol, r_tol = r_tol, max_iter = max_iter, null_weight = null_weight, standardize = standardize, coverage = coverage, min_abs_corr = min_abs_corr, tol = tol, verbose = FALSE, track_fit = FALSE, check_input = FALSE, refine = FALSE) sinit2 = s2[c("alpha","mu","mu2")] class(sinit2) = "susie" s3 = susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n, L = L, X_colmeans = X_colmeans, y_mean = y_mean, prior_weights = pw_s, s_init = sinit2, scaled_prior_variance = scaled_prior_variance, residual_variance = residual_variance, estimate_residual_variance = estimate_residual_variance, estimate_prior_variance = estimate_prior_variance, estimate_prior_method = estimate_prior_method, check_null_threshold = check_null_threshold, prior_tol = prior_tol, r_tol = r_tol, max_iter = max_iter, null_weight = null_weight, standardize = standardize, coverage = coverage, min_abs_corr = min_abs_corr, tol = tol, verbose = FALSE, track_fit = FALSE, check_input = FALSE, refine = FALSE) m = c(m,list(s3)) } if(length(m) == 0){ conti = FALSE }else{ elbo = sapply(m, function(x) susie_get_objective(x)) if ((max(elbo) - susie_get_objective(s)) <= 0) conti = FALSE else s = m[[which.max(elbo)]] } } } return(s) } susieR/R/set_R_attributes.R0000644000176200001440000000227613755042336015411 0ustar liggesusers# @title sets the attributes for the R matrix # @param R a p by p LD matrix # @param r_tol tolerance level for eigen value check of positive # semidefinite matrix of R. # @return R with attribute e.g., attr(R, 'eigenR') is the eigen # decomposition of R. set_R_attributes = function (R, r_tol) { if (is.null(attr(R,"eigen"))) eigenR = eigen(R,symmetric = TRUE) else eigenR = attr(R,"eigen") # Drop small eigenvalues. eigenR$values[abs(eigenR$values) < r_tol] = 0 if(any(eigenR$values < 0)) { min_lambda = min(eigenR$values) eigenR$values[eigenR$values < 0] = 0 warning(paste0("The input correlation matrix has negative eigenvalues ", "(smallest one is ", min_lambda, "). The correlation ", "matrix is adjusted such that these negative eigenvalues ", "are now zeros. You can ignore this message, only if you ", "believe the negative eigenvalue is result of numerical ", "rounding errors.")) } res = eigenR$vectors %*% (t(eigenR$vectors) * eigenR$values) attr(res,"eigen") = eigenR attr(res,"d") = diag(res) attr(res,"scaled:scale") = rep(1,length = nrow(R)) return(res) } susieR/R/susie_plots.R0000644000176200001440000002466614055430030014431 0ustar liggesusers#' @rdname susie_plots #' #' @title SuSiE Plots. #' #' @description \code{susie_plot} produces a per-variable summary of #' the SuSiE credible sets. \code{susie_plot_iteration} produces a #' diagnostic plot for the susie model fitting. For #' \code{susie_plot_iteration}, several plots will be created if #' \code{track_fit = TRUE} when calling \code{susie}. #' #' @param model A SuSiE fit, typically an output from #' \code{\link{susie}} or one of its variants. For \code{suse_plot}, #' the susie fit must have \code{model$z}, \code{model$PIP}, and may #' include \code{model$sets}. \code{model} may also be a vector of #' z-scores or PIPs. #' #' @param y A string indicating what to plot: either \code{"z"} for #' z-scores, \code{"PIP"} for posterior inclusion probabilities, #' \code{"log10PIP"} for posterior inclusion probabiliities on the #' (base-10) log-scale. For any other setting, the data are plotted as #' is. #' #' @param add_bar If \code{add_bar = TRUE}, add horizontal bar to #' signals in credible interval. #' #' @param pos This can be either be (1) a numeric vector of indices of #' subset of variables to plot, or (2) a list with the following list #' elements: \code{pos$attr}, \code{pos$start} and \code{pos$end}, #' where \code{pos$attr} is a character string of the name of index #' variable in \code{model} object, and \code{pos$start} and #' \code{pos$end} are boundaries of indices to plot. See the provided #' examples. #' #' @param b For simulated data, set \code{b = TRUE} to highlight #' "true" effects (highlights in red). #' #' @param max_cs The largest credible set to display, either based on #' purity (set \code{max_cs} between 0 and 1), or based on size (set #' \code{max_cs > 1}). #' #' @param add_legend If \code{add_legend = TRUE}, add a legend to #' annotate the size and purity of each CS discovered. It can also be #' specified as location where legends should be added, e.g., #' \code{add_legend = "bottomright"} (default location is #' \code{"topright"}). #' #' @param \dots Additional arguments passed to #' \code{\link[graphics]{plot}}. #' #' @return Invisibly returns \code{NULL}. #' #' @seealso \code{\link{susie_plot_changepoint}} #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[sample(1:1000,4)] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' res = susie(X,y,L = 10) #' susie_plot(res,"PIP") #' susie_plot(res,"PIP",add_bar = TRUE) #' susie_plot(res,"PIP",add_legend = TRUE) #' susie_plot(res,"PIP", pos=1:500, add_legend = TRUE) #' # Plot selected regions with adjusted x-axis position label #' res$genomic_position = 1000 + (1:length(res$pip)) #' susie_plot(res,"PIP",add_legend = TRUE, #' pos = list(attr = "genomic_position",start = 1000,end = 1500)) #' # True effects are shown in red. #' susie_plot(res,"PIP",b = beta,add_legend = TRUE) #' #' @importFrom utils head #' @importFrom stats pnorm #' @importFrom graphics plot #' @importFrom graphics segments #' @importFrom graphics points #' @importFrom graphics legend #' @importFrom graphics par #' #' @export #' susie_plot = function (model, y, add_bar = FALSE, pos = NULL, b = NULL, max_cs = 400, add_legend = NULL, ...) { is_susie = inherits(model,"susie") ylab = y color = c( "dodgerblue2", "green4", "#6A3D9A", # purple "#FF7F00", # orange "gold1", "skyblue2", "#FB9A99", # lt pink "palegreen2", "#CAB2D6", # lt purple "#FDBF6F", # lt orange "gray70", "khaki2", "maroon", "orchid1", "deeppink1", "blue1", "steelblue4", "darkturquoise", "green1", "yellow4", "yellow3", "darkorange4", "brown" ) if (y == "z") { if (is_susie) { if (is.null(model$z)) stop("z-scores are not available from SuSiE fit; please set ", "compute_univariate_zscore = TRUE in susie() call") zneg = -abs(model$z) } else zneg = -abs(model) p = -log10(2*pnorm(zneg)) ylab = "-log10(p)" } else if (y == "PIP") { if (is_susie) p = model$pip else p = model } else if (y == "log10PIP") { if (is_susie) p = log10(model$pip) else p = log10(model) ylab = "log10(PIP)" } else { if (is_susie) stop("Need to specify z or PIP or log10PIP for SuSiE fits") p = model } if(is.null(b)) b = rep(0,length(p)) if(is.null(pos)) pos = 1:length(p) start = 0 if (inherits(pos,"list")) { # Check input. if (is.null(pos$attr) || is.null(pos$start) || is.null(pos$end)) stop("pos argument should be a list of list(attr=,start=,end=)") if (!(pos$attr %in% names(model))) stop(paste("Cannot find attribute",pos$attr,"in input model object")) if (pos$start >= pos$end) stop("Position start should be smaller than end") start = min(min(model[[pos$attr]]),pos$start) end = max(max(model[[pos$attr]]),pos$end) # Add zeros to alpha and p. alpha = matrix(0,nrow(model$alpha),end - start + 1) new_p = rep(min(p),end - start + 1) pos_with_value = model[[pos$attr]] - start + 1 new_p[pos_with_value] = p alpha[,pos_with_value] = model$alpha p = new_p model$alpha = alpha # Adjust model$cs. if (!is.null(model$sets$cs)) { for (i in 1:length(model$sets$cs)) model$sets$cs[[i]] = pos_with_value[model$sets$cs[[i]]] } # Change "pos" object to be indices. start_adj = -min(min(model[[pos$attr]]) - pos$start,0) end_adj = max(max(model[[pos$attr]]) - pos$end,0) pos = (1 + start_adj):(length(p) - end_adj) } else { if (!all(pos %in% 1:length(p))) stop("Provided position is outside the range of variables") } legend_text = list(col = vector(),purity = vector(),size = vector()) # scipen0 = options()$scipen # options(scipen = 10) args = list(...) if (!exists("xlab", args)) args$xlab = "variable" if (!exists("ylab", args)) args$ylab = ylab if (!exists("pch", args)) args$pch = 16 args$x = pos + start args$y = p[pos] do.call(plot, args) if (is_susie && !is.null(model$sets$cs)) { for(i in rev(1:nrow(model$alpha))){ if (!is.null(model$sets$cs_index) && !(i %in% model$sets$cs_index)) next purity = model$sets$purity[which(model$sets$cs_index == i),1] if (!is.null(model$sets$purity) && max_cs < 1 && purity >= max_cs) { x0 = intersect(pos,model$sets$cs[[which(model$sets$cs_index == i)]]) y1 = p[x0] } else if (n_in_CS(model, model$sets$requested_coverage)[i] < max_cs) { x0 = intersect(pos, which(in_CS(model,model$sets$requested_coverage)[i,] > 0)) y1 = p[x0] } else { x0 = NULL y1 = NULL } if (is.null(x0)) next if (add_bar) { y0 = rep(0,length(x0)) x1 = x0 segments(x0+start,y0,x1+start,y1,lwd = 1.5,col = "gray") } points(x0+start,y1,col = head(color,1),cex = 1.5,lwd = 2.5) legend_text$col = append(head(color,1), legend_text$col) # Rotate color. color = c(color[-1],color[1]) legend_text$purity = append(round(purity,4),legend_text$purity) legend_text$size = append(length(x0),legend_text$size) } if (length(legend_text$col) > 0 && !is.null(add_legend) && !identical(add_legend, FALSE)) { # Plot legend. text = vector() for (i in 1:length(legend_text$col)) { if (legend_text$size[i] == 1) text[i] = paste0("L",i,": C=1") else text[i] = paste0("L",i,": C=",legend_text$size[i],"/R=", legend_text$purity[i]) } if (!(add_legend %in% c("bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", "center"))) { add_legend = "topright" } legend(add_legend,text,bty = "n",col = legend_text$col,cex = 0.65, pch = 15) } } points(pos[b != 0] + start,p[b != 0] + start,col = 2,pch = 16) # options(scipen = scipen0) return(invisible()) } #' @rdname susie_plots #' #' @param L An integer specifying the number of credible sets to plot. #' #' @param file_prefix Prefix to path of output plot file. If not #' specified, the plot, or plots, will be saved to a temporary #' directory generated using \code{\link{tempdir}}. #' #' @param pos Indices of variables to plot. If \code{pos = NULL} all #' variables are plotted. #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[sample(1:1000,4)] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' res = susie(X,y,L = 10) #' susie_plot_iteration(res, L=10) #' #' @importFrom grDevices pdf #' @importFrom grDevices dev.off #' @importFrom reshape melt #' @importFrom ggplot2 ggplot #' @importFrom ggplot2 aes_string #' @importFrom ggplot2 geom_col #' @importFrom ggplot2 ggtitle #' @importFrom ggplot2 theme_classic #' #' @export #' susie_plot_iteration = function (model, L, file_prefix, pos = NULL) { get_layer = function (obj, k, idx, vars) { alpha = melt(obj$alpha[1:k,vars,drop = FALSE]) colnames(alpha) = c("L","variables","alpha") alpha$L = as.factor(alpha$L) ggplot(alpha,aes_string("variables","alpha",group = "L")) + geom_col(aes_string(fill = "L")) + ggtitle(paste("Iteration",idx)) + theme_classic() } k = min(nrow(model$alpha),L) if (is.null(pos)) vars = 1:ncol(model$alpha) else vars = pos if (missing(file_prefix)) file_prefix = file.path(tempdir(),"susie_plot") pdf(paste0(file_prefix,".pdf"),8,3) if (is.null(model$trace)) print(get_layer(model,k,model$niter,vars)) else { for (i in 2:length(model$trace)) print(get_layer(model$trace[[i]],k,i-1,vars)) } dev.off() format = ".pdf" if (!is.null(model$trace)) { cmd = paste("convert -delay 30 -loop 0 -density 300 -dispose previous", paste0(file_prefix,".pdf"), "\\( -clone 0 -set delay 300 \\) -swap 0 +delete", "\\( +clone -set delay 300 \\) +swap +delete -coalesce", "-layers optimize",paste0(file_prefix,".gif")) message("Creating GIF animation...") if (file.exists(paste0(file_prefix,".gif"))) file.remove(paste0(file_prefix,".gif")) output = try(system(cmd)) if (inherits(output,"try-error")) stop("Cannot create GIF animation because convert command failed") else format = ".gif" } message(paste0("Iterplot saved to ",file_prefix,format,"\n")) return(invisible()) } susieR/R/sparse_multiplication.R0000644000176200001440000000453014141046730016464 0ustar liggesusers# @title Computes standardized.X %*% b using sparse multiplication trick # @param X an n by p unstandardized matrix with three attributes: # attr(X,"scaled:center"), attr(X,"scaled:scale") and attr(X,"d") # @param b a p vector # @return an n vector # #' @importFrom Matrix t #' @importFrom Matrix tcrossprod compute_Xb = function (X, b) { cm = attr(X,"scaled:center") csd = attr(X,"scaled:scale") # Scale Xb. if (!is.null(attr(X,"matrix.type"))) # When X is a trend filtering matrix. scaled.Xb = compute_tf_Xb(attr(X,"order"),b/csd) else # When X is an ordinary sparse/dense matrix. scaled.Xb = tcrossprod(X,t(b/csd)) # Center Xb. Xb = scaled.Xb - sum(cm*b/csd) return(as.numeric(Xb)) } # @title Computes t(standardized.X) %*% y using sparse multiplication trick # @param X an n by p unstandardized matrix with three attributes: # attr(X,"scaled:center"), attr(X,"scaled:scale") and attr(X,"d") # @param y an n vector # @return a p vector # #' @importFrom Matrix t #' @importFrom Matrix crossprod compute_Xty = function (X, y) { cm = attr(X,"scaled:center") csd = attr(X,"scaled:scale") ytX = crossprod(y,X) # Scale Xty. if (!is.null(attr(X,"matrix.type"))) # When X is a trend filtering matrix. scaled.Xty = compute_tf_Xty(attr(X,"order"),y)/csd else # When X is an ordinary sparse/dense matrix. scaled.Xty = t(ytX/csd) # Center Xty. centered.scaled.Xty = scaled.Xty - cm/csd * sum(y) return(as.numeric(centered.scaled.Xty)) } # @title Computes M %* %t(standardized.X) using sparse multiplication trick # @param M a L by p matrix # @param X an n by p unstandardized matrix with three attributes: # attr(X,"scaled:center"), attr(X,"scaled:scale") and attr(X,"d") # @return a L by n matrix # #' @importFrom Matrix t compute_MXt = function (M, X) { cm = attr(X,"scaled:center") csd = attr(X,"scaled:scale") if (!is.null(attr(X,"matrix.type"))) # When X is a trend filtering matrix. return(as.matrix(t(apply(M,1,function(b) compute_Xb(X,b))))) else # When X is an ordinary sparse/dense matrix. return(as.matrix(t(X %*% (t(M)/csd)) - drop(M %*% (cm/csd)))) # This should be the same as # # t(apply(M, 1, function(b) compute_Xb(X, b)))) # # as well as # # M %*% (t(X)/csd) - drop(tcrossprod(M,t(cm/csd))) # # but should be more memory-efficient. } susieR/R/susie_plot_changepoint.R0000644000176200001440000000354614061022471016622 0ustar liggesusers#' @title Plot changepoint data and susie fit using ggplot2 #' #' @description Plots original data, y, overlaid with line showing #' susie fitted value and shaded rectangles showing credible sets for #' changepoint locations. #' #' @param y An n-vector of observations that are ordered in time or #' space (assumed equally-spaced). #' #' @param s A susie fit generated by #' \code{susie_trendfilter(y,order = 0)}. #' #' @param line_col Color for the line showing fitted values. #' #' @param line_size Size of the lines showing fitted values #' #' @param cs_col Color of the shaded rectangles showing credible #' sets. #' #' @return A ggplot2 plot object. #' #' @examples #' set.seed(1) #' mu = c(rep(0,50),rep(1,50),rep(3,50),rep(-2,50),rep(0,300)) #' y = mu + rnorm(500) #' # Here we use a less sensitive tolerance so that the example takes #' # less time; in practice you will likely want to use a more stringent #' # setting such as tol = 0.001. #' s = susie_trendfilter(y,tol = 0.1) #' #' # Produces ggplot with credible sets for changepoints. #' susie_plot_changepoint(s,y) #' #' @importFrom ggplot2 ggplot #' @importFrom ggplot2 aes_string #' @importFrom ggplot2 geom_point #' @importFrom ggplot2 geom_line #' @importFrom ggplot2 annotate #' #' @export #' susie_plot_changepoint = function (s, y, line_col = "blue", line_size = 1.5, cs_col = "red") { df = data.frame(x = 1:length(y),y = y,mu = predict.susie(s)) CS = susie_get_cs(s)$cs p = ggplot(df) + geom_point(data = df,aes_string(x = "x",y = "y")) + geom_line(color = line_col,data = df,aes_string(x = "x",y = "mu"), size = line_size) for(i in 1:length(CS)) p = p + annotate("rect",fill = cs_col,alpha = 0.5, xmin = min(CS[[i]]) - 0.5,xmax = max(CS[[i]]) + 0.5, ymin = -Inf,ymax = Inf) return(p) } susieR/R/susie_utils.R0000644000176200001440000007425614143475372014450 0ustar liggesusers#' @rdname susie_get_methods #' #' @title Inferences From Fitted SuSiE Model #' #' @description These functions access basic properties or draw #' inferences from a fitted susie model. #' #' @param res A susie fit, typically an output from #' \code{\link{susie}} or one of its variants. For #' \code{susie_get_pip} and \code{susie_get_cs}, this may instead be #' the posterior inclusion probability matrix, \code{alpha}. #' #' @param last_only If \code{last_only = FALSE}, return the ELBO from #' all iterations; otherwise return the ELBO from the last iteration #' only. #' #' @param warning_tol Warn if ELBO is decreasing by this #' tolerance level. #' #' @return \code{susie_get_objective} returns the evidence lower bound #' (ELBO) achieved by the fitted susie model and, optionally, at each #' iteration of the IBSS fitting procedure. #' #' \code{susie_get_residual_variance} returns the (estimated or #' fixed) residual variance parameter. #' #' \code{susie_get_prior_variance} returns the (estimated or fixed) #' prior variance parameters. #' #' \code{susie_get_posterior_mean} returns the posterior mean for the #' regression coefficients of the fitted susie model. #' #' \code{susie_get_posterior_sd} returns the posterior standard #' deviation for coefficients of the fitted susie model. #' #' \code{susie_get_niter} returns the number of model fitting #' iterations performed. #' #' \code{susie_get_pip} returns a vector containing the posterior #' inclusion probabilities (PIPs) for all variables. #' #' \code{susie_get_lfsr} returns a vector containing the average lfsr #' across variables for each single-effect, weighted by the posterior #' inclusion probability (alpha). #' #' \code{susie_get_posterior_samples} returns a list containing the #' effect sizes samples and causal status with two components: \code{b}, #' an \code{num_variables} x \code{num_samples} matrix of effect #' sizes; \code{gamma}, an \code{num_variables} x \code{num_samples} #' matrix of causal status random draws. #' #' \code{susie_get_cs} returns credible sets (CSs) from a susie fit, #' as well as summaries of correlation among the variables included in #' each CS. If desired, one can filter out CSs that do not meet a #' specified \dQuote{purity} threshold; to do this, either \code{X} or #' \code{Xcorr} must be supplied. It returns a list with the following #' elements: #' #' \item{cs}{A list in which each list element is a vector containing #' the indices of the variables in the CS.} #' #' \item{coverage}{The nominal coverage specified for each CS.} #' #' \item{purity}{If \code{X} or \code{Xcorr} iis provided), the #' purity of each CS.} #' #' \item{cs_index}{If \code{X} or \code{Xcorr} is provided) the index #' (number between 1 and L) of each reported CS in the supplied susie #' fit.} #' #' @examples #' set.seed(1) #' n = 1000 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 1 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' s = susie(X,y,L = 10) #' susie_get_objective(s) #' susie_get_objective(s, last_only=FALSE) #' susie_get_residual_variance(s) #' susie_get_prior_variance(s) #' susie_get_posterior_mean(s) #' susie_get_posterior_sd(s) #' susie_get_niter(s) #' susie_get_pip(s) #' susie_get_lfsr(s) #' #' @export #' susie_get_objective = function (res, last_only = TRUE, warning_tol = 1e-6) { if (!all(diff(res$elbo) >= (-1*warning_tol))) warning("Objective is decreasing") if (last_only) return(res$elbo[length(res$elbo)]) else return(res$elbo) } #' @rdname susie_get_methods #' #' @export #' susie_get_posterior_mean = function (res, prior_tol = 1e-9) { # Drop the single-effects with estimated prior of zero. if (is.numeric(res$V)) include_idx = which(res$V > prior_tol) else include_idx = 1:nrow(res$alpha) # Now extract relevant rows from alpha matrix. if (length(include_idx) > 0) return(colSums((res$alpha*res$mu)[include_idx,,drop=FALSE])/ res$X_column_scale_factors) else return(numeric(ncol(res$mu))) } #' @rdname susie_get_methods #' #' @export #' susie_get_posterior_sd = function (res, prior_tol = 1e-9) { # Drop the single-effects with estimated prior of zero. if (is.numeric(res$V)) include_idx = which(res$V > prior_tol) else include_idx = 1:nrow(res$alpha) # Now extract relevant rows from alpha matrix. if (length(include_idx) > 0) return(sqrt(colSums((res$alpha * res$mu2 - (res$alpha*res$mu)^2)[include_idx,,drop=FALSE]))/ (res$X_column_scale_factors)) else return(numeric(ncol(res$mu))) } #' @rdname susie_get_methods #' #' @export #' susie_get_niter = function (res) res$niter #' @rdname susie_get_methods #' #' @export #' susie_get_prior_variance = function (res) res$V #' @rdname susie_get_methods #' #' @export #' susie_get_residual_variance = function (res) res$sigma2 #' @rdname susie_get_methods #' #' @importFrom stats pnorm #' #' @export #' susie_get_lfsr = function (res) { pos_prob = pnorm(0,mean = t(res$mu),sd = sqrt(res$mu2 - res$mu^2)) neg_prob = 1 - pos_prob return(1 - rowSums(res$alpha * t(pmax(pos_prob,neg_prob)))) } #' @rdname susie_get_methods #' #' @param susie_fit A susie fit, an output from \code{\link{susie}}. #' #' @param num_samples The number of draws from the posterior #' distribution. #' #' @importFrom stats rmultinom #' @importFrom stats rnorm #' #' @export #' susie_get_posterior_samples = function (susie_fit, num_samples) { # Remove effects having estimated prior variance equals zero. if (is.numeric(susie_fit$V)) include_idx = which(susie_fit$V > 1e-9) else include_idx = 1:nrow(susie_fit$alpha) posterior_mean = sweep(susie_fit$mu,2,susie_fit$X_column_scale_factors,"/") posterior_sd = sweep(sqrt(susie_fit$mu2 - (susie_fit$mu)^2),2, susie_fit$X_column_scale_factors,"/") pip = susie_fit$alpha L = nrow(pip) num_snps = ncol(pip) b_samples = matrix(as.numeric(NA),num_snps,num_samples) gamma_samples = matrix(as.numeric(NA),num_snps,num_samples) for (sample_i in 1:num_samples) { b = 0 if (length(include_idx) > 0) { for (l in include_idx) { gamma_l = rmultinom(1,1,pip[l,]) effect_size = rnorm(1,mean = posterior_mean[l,which(gamma_l != 0)], sd = posterior_sd[l,which(gamma_l != 0)]) b_l = gamma_l * effect_size b = b + b_l } } b_samples[, sample_i] = b gamma_samples[, sample_i] = as.numeric(b != 0) } return(list(b = b_samples,gamma = gamma_samples)) } #' @rdname susie_get_methods #' #' @param X n by p matrix of values of the p variables (covariates) in #' n samples. When provided, correlation between variables will be #' computed and used to remove CSs whose minimum correlation among #' variables is smaller than \code{min_abs_corr}. #' #' @param Xcorr p by p matrix of correlations between variables #' (covariates). When provided, it will be used to remove CSs whose #' minimum correlation among variables is smaller than #' \code{min_abs_corr}. #' #' @param coverage A number between 0 and 1 specifying desired #' coverage of each CS. #' #' @param min_abs_corr A "purity" threshold for the CS. Any CS that #' contains a pair of variables with correlation less than this #' threshold will be filtered out and not reported. #' #' @param dedup If \code{dedup = TRUE}, remove duplicate CSs. #' #' @param squared If \code{squared = TRUE}, report min, mean and #' median of squared correlation instead of the absolute correlation. #' #' @param check_symmetric If \code{check_symmetric = TRUE}, perform a #' check for symmetry of matrix \code{Xcorr} when \code{Xcorr} is #' provided (not \code{NULL}). #' #' @param n_purity The maximum number of credible set (CS) variables #' used in calculating the correlation (\dQuote{purity}) #' statistics. When the number of variables included in the CS is #' greater than this number, the CS variables are randomly subsampled. #' #' @param use_rfast Use the Rfast package for the purity calculations. #' By default \code{use_rfast = TRUE} if the Rfast package is #' installed. #' #' @importFrom crayon red #' #' @export #' susie_get_cs = function (res, X = NULL, Xcorr = NULL, coverage = 0.95, min_abs_corr = 0.5, dedup = TRUE, squared = FALSE, check_symmetric = TRUE, n_purity = 100, use_rfast) { if (!is.null(X) && !is.null(Xcorr)) stop("Only one of X or Xcorr should be specified") if (check_symmetric){ if (!is.null(Xcorr) && !is_symmetric_matrix(Xcorr)) { message(red("Xcorr is not symmetric; forcing Xcorr to be symmetric ", "by replacing Xcorr with (Xcorr + t(Xcorr))/2")) Xcorr = Xcorr + t(Xcorr) Xcorr = Xcorr/2 } } null_index = 0 include_idx = rep(TRUE,nrow(res$alpha)) if (!is.null(res$null_index)) null_index = res$null_index if (is.numeric(res$V)) include_idx = res$V > 1e-9 # L x P binary matrix. status = in_CS(res$alpha,coverage) # L-list of CS positions. cs = lapply(1:nrow(status),function(i) which(status[i,]!=0)) claimed_coverage = sapply(1:length(cs), function (i) sum(res$alpha[i,][cs[[i]]])) include_idx = include_idx * (lapply(cs,length) > 0) # FIXME: see issue 21 # https://github.com/stephenslab/susieR/issues/21 if (dedup) include_idx = include_idx * (!duplicated(cs)) include_idx = as.logical(include_idx) if (sum(include_idx) == 0) return(list(cs = NULL, coverage = NULL, requested_coverage = coverage)) cs = cs[include_idx] claimed_coverage = claimed_coverage[include_idx] # Compute and filter by "purity". if (missing(use_rfast)) use_rfast = requireNamespace("Rfast",quietly = TRUE) if (is.null(Xcorr) && is.null(X)) { names(cs) = paste0("L",which(include_idx)) return(list(cs = cs, coverage = claimed_coverage, requested_coverage = coverage)) } else { purity = NULL for (i in 1:length(cs)) { if (null_index > 0 && null_index %in% cs[[i]]) purity = rbind(purity,c(-9,-9,-9)) else purity = rbind(purity, matrix(get_purity(cs[[i]],X,Xcorr,squared,n_purity,use_rfast),1,3)) } purity = as.data.frame(purity) if (squared) colnames(purity) = c("min.sq.corr","mean.sq.corr","median.sq.corr") else colnames(purity) = c("min.abs.corr","mean.abs.corr","median.abs.corr") threshold = ifelse(squared,min_abs_corr^2,min_abs_corr) is_pure = which(purity[,1] >= threshold) if (length(is_pure) > 0) { cs = cs[is_pure] purity = purity[is_pure,] row_names = paste0("L",which(include_idx)[is_pure]) names(cs) = row_names rownames(purity) = row_names # Re-order CS list and purity rows based on purity. ordering = order(purity[,1],decreasing = TRUE) return(list(cs = cs[ordering], purity = purity[ordering,], cs_index = which(include_idx)[is_pure[ordering]], coverage = claimed_coverage[ordering], requested_coverage=coverage)) } else return(list(cs = NULL,coverage = NULL,requested_coverage = coverage)) } } #' @title Get Correlations Between CSs, using Variable with Maximum PIP From Each CS #' #' @description This function evaluates the correlation between single effect #' CSs. It is not part of the SuSiE inference. Rather, it is designed as #' a diagnostic tool to assess how correlated the reported CS are. #' #' @param model A SuSiE fit, typically an output from #' \code{\link{susie}} or one of its variants. #' #' @param X n by p matrix of values of the p variables (covariates) in #' n samples. When provided, correlation between variables will be #' computed and used to remove CSs whose minimum correlation among #' variables is smaller than \code{min_abs_corr}. #' #' @param Xcorr p by p matrix of correlations between variables #' (covariates). When provided, it will be used to remove CSs whose #' minimum correlation among variables is smaller than #' \code{min_abs_corr}. #' #' @param max When \code{max = FAFLSE}, return a matrix of CS #' correlations. When \code{max = TRUE}, return only the maximum #' absolute correlation among all pairs of correlations. #' #' @return A matrix of correlations between CSs, or the maximum #' absolute correlation when \code{max = TRUE}. #' #' @importFrom crayon red #' #' @export #' get_cs_correlation = function (model, X = NULL, Xcorr = NULL, max = FALSE) { if (is.null(model$sets$cs) || length(model$sets$cs) == 1) return(NA) if (!is.null(X) && !is.null(Xcorr)) stop("Only one of X or Xcorr should be specified") if (is.null(Xcorr) && is.null(X)) stop("One of X or Xcorr must be specified") if (!is.null(Xcorr) && !is_symmetric_matrix(Xcorr)) { message(red("Xcorr is not symmetric; forcing Xcorr to be symmetric ", "by replacing Xcorr with (Xcorr + t(Xcorr))/2")) Xcorr = Xcorr + t(Xcorr) Xcorr = Xcorr/2 } # Get index for the best PIP per CS max_pip_idx = sapply(model$sets$cs, function(cs) cs[which.max(model$pip[cs])]) if (is.null(Xcorr)) { X_sub = X[,max_pip_idx] cs_corr = muffled_corr(as.matrix(X_sub)) } else { cs_corr = Xcorr[max_pip_idx, max_pip_idx] } if (max) { cs_corr = max(abs(cs_corr[upper.tri(cs_corr)])) } return(cs_corr) } #' @rdname susie_get_methods #' #' @param prune_by_cs Whether or not to ignore single effects not in #' a reported CS when calculating PIP. #' #' @param prior_tol Filter out effects having estimated prior variance #' smaller than this threshold. #' #' @export #' susie_get_pip = function (res, prune_by_cs = FALSE, prior_tol = 1e-9) { if (inherits(res,"susie")) { # Drop null weight columns. if (!is.null(res$null_index) && res$null_index > 0) res$alpha = res$alpha[,-res$null_index,drop=FALSE] # Drop the single-effects with estimated prior of zero. if (is.numeric(res$V)) include_idx = which(res$V > prior_tol) else include_idx = 1:nrow(res$alpha) # Only consider variables in reported CS. # This is not what we do in the SuSiE paper. # So by default prune_by_cs = FALSE means we do not run the # following code. if (!is.null(res$sets$cs_index) && prune_by_cs) include_idx = intersect(include_idx,res$sets$cs_index) if (is.null(res$sets$cs_index) && prune_by_cs) include_idx = numeric(0) # now extract relevant rows from alpha matrix if (length(include_idx) > 0) res = res$alpha[include_idx,,drop = FALSE] else res = matrix(0,1,ncol(res$alpha)) } return(as.vector(1 - apply(1 - res,2,prod))) } # Find how many variables in the CS. # x is a probability vector. #' @keywords internal n_in_CS_x = function (x, coverage = 0.9) sum(cumsum(sort(x,decreasing = TRUE)) < coverage) + 1 # Return binary vector indicating if each point is in CS. # x is a probability vector. #' @keywords internal in_CS_x = function (x, coverage = 0.9) { n = n_in_CS_x(x,coverage) o = order(x,decreasing = TRUE) result = rep(0,length(x)) result[o[1:n]] = 1 return(result) } # Returns an l-by-p binary matrix indicating which variables are in # susie credible sets. #' @keywords internal in_CS = function (res, coverage = 0.9) { if (inherits(res,"susie")) res = res$alpha return(t(apply(res,1,function(x) in_CS_x(x,coverage)))) } #' @keywords internal n_in_CS = function(res, coverage = 0.9) { if (inherits(res,"susie")) res = res$alpha return(apply(res,1,function(x) n_in_CS_x(x,coverage))) } # Subsample and compute min, mean, median and max abs corr. # #' @importFrom stats median get_purity = function (pos, X, Xcorr, squared = FALSE, n = 100, use_rfast) { if (missing(use_rfast)) use_rfast = requireNamespace("Rfast",quietly = TRUE) if (use_rfast) { get_upper_tri = Rfast::upper_tri get_median = Rfast::med } else { get_upper_tri = function (R) R[upper.tri(R)] get_median = stats::median } if (length(pos) == 1) return(c(1,1,1)) else { # Subsample the columns if necessary. if (length(pos) > n) pos = sample(pos,n) if (is.null(Xcorr)) { X_sub = X[,pos] X_sub = as.matrix(X_sub) value = abs(get_upper_tri(muffled_corr(X_sub))) } else value = abs(get_upper_tri(Xcorr[pos,pos])) if (squared) value = value^2 return(c(min(value), sum(value)/length(value), get_median(value))) } } # Correlation function with specified warning muffled. # #' @importFrom stats cor muffled_corr = function (x) withCallingHandlers(cor(x), warning = function(w) { if (grepl("the standard deviation is zero",w$message)) invokeRestart("muffleWarning") }) # cov2cor function with specified warning muffled. # #' @importFrom stats cov2cor #' @keywords internal muffled_cov2cor = function (x) withCallingHandlers(cov2cor(x), warning = function(w) { if (grepl("had 0 or NA entries; non-finite result is doubtful", w$message)) invokeRestart("muffleWarning") }) # Check for symmetric matrix. #' @keywords internal is_symmetric_matrix = function (x) { if (requireNamespace("Rfast",quietly = TRUE)) return(Rfast::is.symmetric(x)) else return(isSymmetric(x)) } # Compute standard error for regression coef. # S = (X'X)^-1 \Sigma #' @keywords internal calc_stderr = function (X, residuals) sqrt(diag(sum(residuals^2)/(nrow(X) - 2) * chol2inv(chol(crossprod(X))))) # Return residuals of Y after removing the linear effects of the susie # model. # #' @importFrom stats coef #' @keywords internal get_R = function (X, Y, s) Y - X %*% coef(s) # Slim the result of fitted susie model. #' @keywords internal susie_slim = function (res) list(alpha = res$alpha,niter = res$niter,V = res$V,sigma2 = res$sigma2) # Prune single effects to given number L in susie model object. #' @keywords internal susie_prune_single_effects = function (s,L = 0,V = NULL) { num_effects = nrow(s$alpha) if (L == 0) { # Filtering will be based on non-zero elements in s$V. if (!is.null(s$V)) L = length(which(s$V > 0)) else L = num_effects } if (L == num_effects) { s$sets = NULL return(s) } if (!is.null(s$sets$cs_index)) effects_rank = c(s$sets$cs_index,setdiff(1:num_effects,s$sets$cs_index)) else effects_rank = 1:num_effects if (L > num_effects) { message(paste("Specified number of effects L =",L, "is greater the number of effects",num_effects, "in input SuSiE model. The SuSiE model will be expanded to have",L, "effects.")) s$alpha = rbind(s$alpha[effects_rank,], matrix(1/ncol(s$alpha),L - num_effects,ncol(s$alpha))) for (n in c("mu","mu2","lbf_variable")) if (!is.null(s[[n]])) s[[n]] = rbind(s[[n]][effects_rank,], matrix(0,L - num_effects,ncol(s[[n]]))) for (n in c("KL", "lbf")) if (!is.null(s[[n]])) s[[n]] = c(s[[n]][effects_rank],rep(NA, L-num_effects)) if (!is.null(V)) { if (length(V) > 1) V[1:num_effects] = s$V[effects_rank] else V = rep(V,L) } s$V = V } s$sets = NULL return(s) } #' @title Estimate s in \code{susie_rss} Model Using Regularized LD #' #' @description The estimated s gives information about the #' consistency between the z scores and LD matrix. A larger \eqn{s} #' means there is a strong inconsistency between z scores and LD #' matrix. The \dQuote{null-mle} method obtains mle of \eqn{s} under #' \eqn{z | R ~ N(0,(1-s)R + s I)}, \eqn{0 < s < 1}. The #' \dQuote{null-partialmle} method obtains mle of \eqn{s} under #' \eqn{U^T z | R ~ N(0,s I)}, in which \eqn{U} is a matrix containing #' the of eigenvectors that span the null space of R; that is, the #' eigenvectors corresponding to zero eigenvalues of R. The estimated #' \eqn{s} from \dQuote{null-partialmle} could be greater than 1. The #' \dQuote{null-pseudomle} method obtains mle of \eqn{s} under #' pseudolikelihood \eqn{L(s) = \prod_{j=1}^{p} p(z_j | z_{-j}, s, #' R)}, \eqn{0 < s < 1}. #' #' @param z A p-vector of z scores. #' #' @param R A p by p symmetric, positive semidefinite correlation #' matrix. #' #' @param r_tol Tolerance level for eigenvalue check of positive #' semidefinite matrix of R. #' #' @param method a string specifies the method to estimate \eqn{s}. #' #' @return A number between 0 and 1. #' #' @examples #' set.seed(1) #' n = 500 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 0.01 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' input_ss = compute_suff_stat(X,y,standardize = TRUE) #' ss = univariate_regression(X,y) #' R = cor(X) #' attr(R,"eigen") = eigen(R, symmetric = TRUE) #' zhat = with(ss,betahat/sebetahat) #' s = estimate_s_rss(zhat, R) #' #' @importFrom stats dnorm #' @importFrom stats optim #' #' @export #' estimate_s_rss = function (z, R, r_tol = 1e-08, method = "null-mle") { if (is.null(attr(R,"eigen"))) attr(R,"eigen") = eigen(R,symmetric = TRUE) eigenld = attr(R,"eigen") if(any(eigenld$values < -r_tol)) warning("The matrix R is not positive semidefinite. Negative ", "eigenvalues are set to zero") eigenld$values[eigenld$values < r_tol] = 0 if (method == "null-mle") { negloglikelihood = function(s, z, eigenld) 0.5 * sum(log((1-s)*eigenld$values + s)) + 0.5 * sum(z * eigenld$vectors %*% ((t(eigenld$vectors) * (1/((1-s)*eigenld$values + s))) %*% z)) s = optim(0.5,fn = negloglikelihood,z = z,eigenld = eigenld, method = "Brent",lower = 0,upper = 1)$par } else if (method == "null-partialmle") { colspace = which(eigenld$values > 0) if (length(colspace) == length(z)) s = 0 else{ znull = crossprod(eigenld$vectors[,-colspace], z) # U2^T z s = sum(znull^2)/length(znull) } } else if (method == "null-pseudomle") { pseudolikelihood = function(s, z, eigenld) { precision = eigenld$vectors %*% (t(eigenld$vectors) * (1/((1-s)*eigenld$values + s))) postmean = rep(0,length(z)) postvar = rep(0,length(z)) for (i in 1:length(z)) { postmean[i] = -(1/precision[i,i])*precision[i,-i] %*% z[-i] postvar[i] = 1/precision[i,i] } return(-sum(dnorm(z,mean = postmean,sd = sqrt(postvar),log = TRUE))) } s = optim(0.5,fn = pseudolikelihood,z = z,eigenld = eigenld, method = "Brent",lower = 0,upper = 1)$par } else stop("The method is not implemented") return(s) } #' @title Compute Distribution of z-scores of Variant j Given Other z-scores, and Detect Possible Allele Switch Issue #' #' @description Under the null, the rss model with regularized LD #' matrix is \eqn{z|R,s ~ N(0, (1-s)R + s I))}. We use a mixture of #' normals to model the conditional distribution of z_j given other z #' scores, \eqn{z_j | z_{-j}, R, s ~ \sum_{k=1}^{K} \pi_k #' N(-\Omega_{j,-j} z_{-j}/\Omega_{jj}, \sigma_{k}^2/\Omega_{jj})}, #' \eqn{\Omega = ((1-s)R + sI)^{-1}}, \eqn{\sigma_1, ..., \sigma_k} #' is a grid of fixed positive numbers. We estimate the mixture #' weights \eqn{\pi} We detect the possible allele switch issue #' using likelihood ratio for each variant. #' #' @param z A p-vector of z scores. #' #' @param R A p by p symmetric, positive semidefinite correlation #' matrix. #' #' @param r_tol Tolerance level for eigenvalue check of positive #' semidefinite matrix of R. #' #' @param s an estimated s from \code{estimate_s_rss} #' #' @return a list containing a ggplot2 plot object and a table. The plot #' compares observed z score vs the expected value. The possible allele #' switched variants are labeled as red points (log LR > 2 and abs(z) > 2). #' The table summarizes the conditional distribution for each variant #' and the likelihood ratio test. The table has the following columns: #' the observed z scores, the conditional expectation, the conditional #' variance, the standardized differences between the observed z score #' and expected value, the log likelihood ratio statistics. #' #' @importFrom stats dnorm #' @importFrom ggplot2 ggplot #' @importFrom ggplot2 geom_point #' @importFrom ggplot2 geom_abline #' @importFrom ggplot2 theme_bw #' @importFrom ggplot2 labs #' @importFrom ggplot2 aes #' @importFrom mixsqp mixsqp #' #' @examples #' # See also the vignette, "Diagnostic for fine-mapping with summary #' # statistics." #' set.seed(1) #' n = 500 #' p = 1000 #' beta = rep(0,p) #' beta[1:4] = 0.01 #' X = matrix(rnorm(n*p),nrow = n,ncol = p) #' X = scale(X,center = TRUE,scale = TRUE) #' y = drop(X %*% beta + rnorm(n)) #' ss = univariate_regression(X,y) #' R = cor(X) #' attr(R,"eigen") = eigen(R, symmetric = TRUE) #' zhat = with(ss,betahat/sebetahat) #' cond_dist = kriging_rss(zhat, R) #' cond_dist$plot #' #' @export #' kriging_rss = function (z, R, r_tol = 1e-08, s = estimate_s_rss(z,R,r_tol,method = "null-mle")) { if (is.null(attr(R,"eigen"))) attr(R,"eigen") = eigen(R,symmetric = TRUE) eigenld = attr(R,"eigen") if (any(eigenld$values < -r_tol)) warning("The matrix R is not positive semidefinite. Negative ", "eigenvalues are set to zero.") eigenld$values[eigenld$values < r_tol] = 0 if (s > 1) { warning("The given s is greater than 1. We replace it with 0.8.") s = 0.8 } if (s < 0) stop("The s must be non-negative") dinv = 1/((1-s)*eigenld$values + s) dinv[is.infinite(dinv)] = 0 precision = eigenld$vectors %*% (t(eigenld$vectors) * dinv) condmean = rep(0,length(z)) condvar = rep(0,length(z)) for(i in 1:length(z)){ condmean[i] = -(1/precision[i,i]) * precision[i,-i] %*% z[-i] condvar[i] = 1/precision[i,i] } z_std_diff = (z-condmean)/sqrt(condvar) # obtain grid a_min = 0.8 if (max(z_std_diff^2) < 1){ a_max = 2 }else{ a_max = 2*sqrt(max(z_std_diff^2)) } npoint = ceiling(log2(a_max/a_min)/log2(1.05)) a_grid = 1.05^((-npoint):0) * a_max # compute likelihood sd_mtx = outer(sqrt(condvar),a_grid) matrix_llik = dnorm(z - condmean,sd = sd_mtx,log = TRUE) lfactors = apply(matrix_llik,1,max) matrix_llik = matrix_llik - lfactors # estimate weight w = mixsqp(matrix_llik,log = TRUE,control = list(verbose = FALSE))$x logl0mix = as.numeric(log(exp(matrix_llik) %*% w)) + lfactors matrix_llik = dnorm(z + condmean, sd=sd_mtx, log = TRUE) lfactors = apply(matrix_llik,1,max) matrix_llik = matrix_llik - lfactors logl1mix = as.numeric(log(exp(matrix_llik) %*% w)) + lfactors logLRmix = logl1mix - logl0mix res = data.frame(z = z,condmean = condmean,condvar = condvar, z_std_diff = z_std_diff,logLR = logLRmix) p = ggplot(res) + geom_point(aes(y = z, x = condmean)) + labs(y = "Observed z scores", x = "Expected value") + geom_abline(intercept = 0, slope = 1) + theme_bw() idx = which(logLRmix > 2 & abs(z) > 2) if(length(idx) > 0) { p = p + geom_point(data = res[idx,], aes(y = z, x = condmean),col = "red") } return(list(plot = p, conditional_dist = res)) } # Compute the column means of X, the column standard deviations of X, # and rowSums(Y^2), where Y is the centered and/or scaled version of # X. # #' @importFrom Matrix rowSums #' @importFrom Matrix colMeans compute_colstats = function (X, center = TRUE, scale = TRUE) { n = nrow(X) p = ncol(X) if (!is.null(attr(X,"matrix.type"))) { # X is a trend filtering matrix. cm = compute_tf_cm(attr(X,"order"),p) csd = compute_tf_csd(attr(X,"order"),p) d = compute_tf_d(attr(X,"order"),p,cm,csd,scale,center) if (!center) cm = rep(0,p) if (!scale) csd = rep(1,p) } else { # X is an ordinary dense or sparse matrix. Set sd = 1 when the # column has variance 0. if (center) cm = colMeans(X,na.rm = TRUE) else cm = rep(0,p) if (scale) { csd = compute_colSds(X) csd[csd == 0] = 1 } else csd = rep(1,p) # These two lines of code should give the same result as # # Y = (t(X) - cm)/csd # d = rowSums(Y^2) # # for all four combinations of "center" and "scale", but do so # without having to modify X, or create copies of X in memory. In # particular the first line should be equivalent to colSums(X^2). d = n*colMeans(X)^2 + (n-1)*compute_colSds(X)^2 d = (d - n*cm^2)/csd^2 } return(list(cm = cm,csd = csd,d = d)) } # @title computes column standard deviations for any type of matrix # @details This should give the same result as matrixStats::colSds(X), # but allows for sparse matrices as well as dense ones. # @param X an n by p matrix of any type, e.g. sparse, dense. # @return a p vector of column standard deviations. # #' @importFrom Matrix rowSums #' @importFrom Matrix colSums #' @importFrom Matrix rowMeans #' @importFrom matrixStats colSds compute_colSds = function(X) { if (is.matrix(X)) y = colSds(X) else { n = nrow(X) X = t(X) X = X - rowMeans(X) y = sqrt(rowSums(X^2)/(n-1)) } return(y) } # @title Check whether A is positive semidefinite # @param A a symmetric matrix # @return a list of result: # \item{matrix}{The matrix with eigen decomposition} # \item{status}{whether A is positive semidefinite} # \item{eigenvalues}{eigenvalues of A truncated by r_tol} check_semi_pd = function (A, tol) { attr(A,"eigen") = eigen(A,symmetric = TRUE) v = attr(A,"eigen")$values v[abs(v) < tol] = 0 return(list(matrix = A, status = !any(v < 0), eigenvalues = v)) } # @title Check whether b is in space spanned by the non-zero eigenvectors # of A # @param A a p by p matrix # @param b a length p vector # @return a list of result: # \item{status}{whether b in space spanned by the non-zero # eigenvectors of A} # \item{msg}{msg gives the difference between the projected b and b if # status is FALSE} check_projection = function (A, b) { if (is.null(attr(A,"eigen"))) attr(A,"eigen") = eigen(A,symmetric = TRUE) v = attr(A,"eigen")$values B = attr(A,"eigen")$vectors[,v > .Machine$double.eps] msg = all.equal(as.vector(B %*% crossprod(B,b)),as.vector(b), check.names = FALSE) if (!is.character(msg)) return(list(status = TRUE,msg = NA)) else return(list(status = FALSE,msg = msg)) } 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It # will update the 4th digit of package version by revision number. # # To use this script, copy it to the .git/hooks directory of your # local repository to filename `pre-commit`, and make it executable. # ROOT_DIR=`git rev-parse --show-toplevel` MSG="[WARNING] Auto-versioning disabled because string 'Version: x.y.z.r' cannot be found in DESCRIPTION file." GREP_REGEX='^Version: [0-9]*\.[0-9]*\.[0-9]*\.[0-9]*' SED_REGEX='^Version: \([0-9]*\.[0-9]*\.[0-9]*\)\.[0-9]*' # `git diff HEAD` shows both staged and unstaged changes if [[ -z `git diff HEAD` ]] || [[ ! -f $ROOT_DIR/DESCRIPTION ]]; then exit 0 elif [[ -z `grep "$GREP_REGEX" $ROOT_DIR/DESCRIPTION` ]]; then echo -e "\e[1;31m$MSG\e[0m" else REV_ID=`git log --oneline | wc -l` REV_ID=`printf "%04d\n" $((REV_ID+1))` DATE=`date +%Y-%m-%d` echo "Version string bumped to revision $REV_ID on $DATE" sed -i "s/$SED_REGEX/Version: \1.$REV_ID/" $ROOT_DIR/DESCRIPTION sed -i "s/^Date: .*/Date: $DATE/" $ROOT_DIR/DESCRIPTION if [[ `git rev-parse --abbrev-ref HEAD` -eq "master" ]]; then cd $ROOT_DIR echo "Updating documentation ..." Rscript -e 'devtools::document()' &> /dev/null && git add man/*.Rd echo "Running unit tests ..." Rscript -e 'devtools::test()' Rscript -e 'library(susieR); tests = testthat::test_examples(".")' fi fi susieR/inst/misc/post-commit.sh0000744000176200001440000000126713364666256016273 0ustar liggesusers#!/bin/bash # # This script will be executed every time you run "git commit". It # will commit changes made to package DESCRIPTION by the pre-commit hook # # To use this script, copy it to the .git/hooks directory of your # local repository to filename `post-commit`, and make it executable. # ROOT_DIR=`git rev-parse --show-toplevel` # Only commit DESCRIPTION file when it is not staged (due to changes by pre-commit hook) if [[ -z `git diff HEAD` ]] || [[ ! -f $ROOT_DIR/DESCRIPTION ]] || [[ -z `git diff $ROOT_DIR/DESCRIPTION` ]]; then exit 0 else git add $ROOT_DIR/DESCRIPTION git commit --amend -C HEAD --no-verify echo "Amend current commit to incorporate version bump" fi susieR/inst/doc/0000755000176200001440000000000014143477740013261 5ustar liggesuserssusieR/inst/doc/finemapping.Rmd0000644000176200001440000001355514112242336016216 0ustar liggesusers--- title: "Fine-mapping example" author: "Gao Wang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Fine-mapping example} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates `susieR` in the context of genetic fine-mapping. We use simulated data of expression level of a gene ($y$) in $N \approx 600$ individuals. We want to identify with the genotype matrix $X_{N\times P}$ ($P=1001$) the genetic variables that causes changes in expression level. The simulated data set is simulated to have exactly 3 non-zero effects. ```{r} library(susieR) set.seed(1) ``` ## The data-set ```{r} data(N3finemapping) attach(N3finemapping) ``` The loaded dataset contains regression data $X$ and $y$, along with some other relevant properties in the context of genetic studies. It also contains the "true" regression coefficent the data is simulated from. Notice that we've simulated 2 sets of $Y$ as 2 simulation replicates. Here we'll focus on the first data-set. ```{r} dim(Y) ``` Here are the 3 "true" signals in the first data-set: ```{r} b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ``` ```{r} which(b != 0) ``` So the underlying causal variables are 403, 653 and 773. ## Simple regression summary statistics `univariate_regression` function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are $\hat{\beta}$ and $SE(\hat{\beta})$ from which z-scores can be derived. Again we focus only on results from the first data-set: ```{r} sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ``` ## Fine-mapping with `susieR` For starters, we assume there are at most 10 causal variables, i.e., set `L = 10`, although SuSiE is robust to the choice of `L`. The `susieR` function call is: ```{r} fitted <- susie(X, Y[,1], L = 10, verbose = TRUE) ``` ### Credible sets By default, we output 95% credible set: ```{r} print(fitted$sets) ``` The 3 causal signals have been captured by the 3 CS reported here. The 3rd CS contains many variables, including the true causal variable `403`. The minimum absolute correlation is 0.86. If we use the default 90% coverage for credible sets, we still capture the 3 signals, but "purity" of the 3rd CS is now 0.91 and size of the CS is also a bit smaller. ```{r} sets <- susie_get_cs(fitted, X = X, coverage = 0.9, min_abs_corr = 0.1) ``` ```{r} print(sets) ``` ### Posterior inclusion probabilities Previously we've determined that summing over 3 single effect regression models is approperate for our application. Here we summarize the variable selection results by posterior inclusion probability (PIP): ```{r} susie_plot(fitted, y="PIP", b=b) ``` The true causal variables are colored red. The 95% CS identified are circled in different colors. Of interest is the cluster around position 400. The true signal is 403 but apparently it does not have the highest PIP. To compare ranking of PIP and original z-score in that CS: ```{r} i <- fitted$sets$cs[[3]] z3 <- cbind(i,z_scores[i],fitted$pip[i]) colnames(z3) <- c('position', 'z-score', 'PIP') z3[order(z3[,2], decreasing = TRUE),] ``` ### Choice of priors Notice that by default SuSiE estimates prior effect size from data. For fine-mapping applications, however, we sometimes have knowledge of SuSiE prior effect size since it is parameterized as percentage of variance explained (PVE) by a non-zero effect, which, in the context of fine-mapping, is related to per-SNP heritability. It is possible to use `scaled_prior_variance` to specify this PVE and explicitly set `estimate_prior_variance=FALSE` to fix the prior effect to given value. In this data-set, SuSiE is robust to choice of priors. Here we set PVE to 0.2, and compare with previous results: ```{r} fitted = susie(X, Y[,1], L = 10, estimate_residual_variance = TRUE, estimate_prior_variance = FALSE, scaled_prior_variance = 0.2) susie_plot(fitted, y='PIP', b=b) ``` which largely remains unchanged. ### A note on covariate adjustment To include covariate `Z` in SuSiE, one approach is to regress it out from both `y` and `X`, and then run SuSiE on the residuals. The code below illustrates the procedure: ```{r, eval=FALSE} remove.covariate.effects <- function (X, Z, y) { # include the intercept term if (any(Z[,1]!=1)) Z = cbind(1, Z) A <- forceSymmetric(crossprod(Z)) SZy <- as.vector(solve(A,c(y %*% Z))) SZX <- as.matrix(solve(A,t(Z) %*% X)) y <- y - c(Z %*% SZy) X <- X - Z %*% SZX return(list(X = X,y = y,SZy = SZy,SZX = SZX)) } out = remove.covariate.effects(X, Z, Y[,1]) fitted_adjusted = susie(out$X, out$y, L = 10) , ``` Note that the covariates `Z` should have a column of ones as the first column. If not, the above function `remove.covariate.effects` will add such a column to `Z` before regressing it out. Data will be centered as a result. Also the scale of data is changed after regressing out `Z`. This introduces some subtleties in terms of interpreting the results. For this reason, we provide covariate adjustment procedure as a tip in the documentation and not part of `susieR::susie()` function. Cautions should be taken when applying this procedure and interpreting the result from it. ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` [N3finemapping]: https://github.com/stephenslab/susieR/blob/master/inst/datafiles/N3finemapping.rds susieR/inst/doc/l0_initialization.R0000644000176200001440000000233414143477602017025 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#") ## ---- warning=FALSE----------------------------------------------------------- library(susieR) library(L0Learn) ## ----------------------------------------------------------------------------- set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[c(1,2,300,400)] = 1 X = matrix(rnorm(n*p),nrow=n,ncol=p) y = X %*% beta + rnorm(n) ## ----------------------------------------------------------------------------- set.seed(1) L0fit = L0Learn.cvfit(X, y, penalty = "L0") ## ----------------------------------------------------------------------------- lambdaIndex = which.min(L0fit$cvMeans[[1]]) L0coef = as.numeric(coef(L0fit$fit, lambda = L0fit$fit$lambda[[1]][lambdaIndex])) effect.beta = L0coef[which(L0coef!=0)][-1] effect.index = (which(L0coef!=0)-1)[-1] length(effect.beta) effect.beta[1:10] effect.index[1:10] ## ----------------------------------------------------------------------------- set.seed(1) s.init = susie_init_coef(effect.index, effect.beta, p) ## ----------------------------------------------------------------------------- susieL0.fit = susie(X,y,s_init=s.init) susieL0.fit$sets$cs susieR/inst/doc/trend_filtering.R0000644000176200001440000000335414143477733016572 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) set.seed(1) n=1000 mu = c(rep(0,100),rep(1,100),rep(3,100),rep(-2,100),rep(0,600)) y = mu + rnorm(n) s = susie_trendfilter(y, 0, L=10) ## ----------------------------------------------------------------------------- plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) s$sigma2 ## ----------------------------------------------------------------------------- set.seed(1) mu = seq(0,4,length=1000) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) ## ----------------------------------------------------------------------------- library(genlasso) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ## ----------------------------------------------------------------------------- set.seed(1) mu = seq(0,4,length=1000) mu = mu + c(rep(0,500),rep(4,500)) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ## ----------------------------------------------------------------------------- sqrt(mean((mu-y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)])^2)) sqrt(mean((mu-predict(s))^2)) susieR/inst/doc/trend_filtering.Rmd0000644000176200001440000001050414051733237017076 0ustar liggesusers--- title: "Trend filtering" author: "Matthew Stephens" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Trend filtering demo} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` # Introduction Although we developed SuSiE primarily with the goal of performing variable selection in highly sparse settings -- and, in particular, for genetic fine-mapping -- the approach also has considerable potential for application to other large-scale regression problems. Here we briefly illustrate this potential by applying it to a non-parametric regression problem that at first sight seems to be ill-suited to our approach. In particular, it does not involve strict sparsity, and the underlying correlation structure of the explanatory variables is very different from the ``blocky" covariance structure of genetic data that SuSiE was designed for. Nonetheless, we will see that SuSiE performs well here despite this (partly due to its ability to capture non-sparse signals via Bayesian Model Averaging). Specifically, consider the non-parametric regression: $$y_t = \mu_t + e_t \quad t=1,\dots,T$$ where the goal is to estimate the underlying mean, $\mu_t$, under the assumption that it varies smoothly (or, more precisely, in a spatially-structured way) with $t$. One very simple way to capture spatial structure in $\mu$ is to model it as a (sparse) linear combination of step functions: $$\mu = Xb$$ where the $j$th column of $X$ is the step function with a step at $j$ ($j = 1,\dots,(T-1)$); that is $x_{tj}=0$ for $t<=j$ and 1 for $t>j$. The $j$th element of $b$ therefore determines the change in the mean $|\mu_j-\mu_{j+1}|$, and an assumption that $b$ is sparse encapsulates an assumption that $\mu$ is spatially structured (indeed, piecewise constant). This very simple approach is essentially 0th-order trend filtering (Tibshirani, 2014, *Annals of Statistics* 42, 285--323). Note that higher-order trend filtering can be similarly implemented using different basis functions; here we use 0th order only for simplicity. # Examples Here we apply SuSiE to perform 0th order trend filtering in some simple simulated examples. We have implemented in `susieR` 0.6.0 a funciton `susie_trendfilter` which internally creates $X$ matrix with step functions in the columns to match input $y$. The algebra have been optimized to work on such trendfiltering matrices. Here we simulate some data where $\mu$ is a step function with four steps, a 0th order trendfiltering problem. The regression is truly sparse and SuSiE should do well. ```{r} library(susieR) set.seed(1) n=1000 mu = c(rep(0,100),rep(1,100),rep(3,100),rep(-2,100),rep(0,600)) y = mu + rnorm(n) s = susie_trendfilter(y, 0, L=10) ``` Plot results: the truth is black, and susie estimate is red. ```{r} plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) s$sigma2 ``` In the next example mu increases linearly. Thus we are approximating a linear function by step functions. Here the truth is not trully ``sparse", so we might expect performance to be poor, but it is not too bad. ```{r} set.seed(1) mu = seq(0,4,length=1000) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) ``` Compare with the genlasso (lasso-based) solution (blue). (This will require installation of the `genlasso` package.) ```{r} library(genlasso) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ``` What happens if we have linear trend plus a sudden change. ```{r} set.seed(1) mu = seq(0,4,length=1000) mu = mu + c(rep(0,500),rep(4,500)) y = mu + rnorm(n) s = susie_trendfilter(y,0,L=10) y.tf = trendfilter(y,ord=0) y.tf.cv = cv.trendfilter(y.tf) plot(y,pch=".") lines(mu,col=1,lwd=3) lines(predict(s),col=2,lwd=2) lines(y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)],col=4,lwd=2) ``` The two fits seem similar in accuracy. We can check this numerically: ```{r} sqrt(mean((mu-y.tf$fit[,which(y.tf$lambda==y.tf.cv$lambda.min)])^2)) sqrt(mean((mu-predict(s))^2)) ``` susieR/inst/doc/susie_refine.html0000644000176200001440000033702714143477631016642 0ustar liggesusers Refine SuSiE model

Refine SuSiE model

Yuxin Zou

2021-11-12

In this vignette, we demonstrate a procedure that helps SuSiE get out of local optimum.

We simulate phenotype using UK Biobank genotypes from 50,000 individuals. There are 1001 SNPs. It is simulated to have exactly 2 non-zero effects at 234, 287.
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The strongest marginal association is a non-effect SNP.

Since the sample size is large, we use sufficient statistics (\(X^\intercal X, X^\intercal y, y^\intercal y\) and sample size \(n\)) to fit susie model. It identifies 2 Credible Sets, one of them is false positive. This is because susieR get stuck around a local minimum.

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Our refine procedure to get out of local optimum is

  1. fit a susie model, \(s\) (suppose it has \(K\) CSs).

  2. for CS in \(s\), set SNPs in CS to have prior weight 0, fit susie model –> we have K susie models: \(t_1, \cdots, t_K\).

  3. for each \(k = 1, \cdots, K\), fit susie with initialization at \(t_k\) (\(\alpha, \mu, \mu^2\)) –> \(s_k\)

  4. if \(\max_k \text{elbo}(s_k) > \text{elbo}(s)\), set \(s = s_{kmax}\) where \(kmax = \arg_k \max \text{elbo}(s_k)\) and go to step 2; if no, break.

We fit susie model with above procedure by setting refine = TRUE.

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With the refine procedure, it identifies 2 CSs with the true signals, and the achieved evidence lower bound (ELBO) is higher.

Session information

Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results.

susieR/inst/doc/sparse_susie_eval.Rmd0000644000176200001440000000345113351217671017437 0ustar liggesusers--- title: "Evaluation of sparse version of SuSiE" author: "Kaiqian Zhang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{SuSiE with sparse matrix operations} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` ## Set up environment ```{r, warning=FALSE} library(Matrix) library(susieR) set.seed(1) ``` ## Overview In this vignette, we provide line profiles for revised version SuSiE, which allows for a sparse matrix structure. We compare speed performance when the form of the matrix `X` is dense and sparse. In this minimal example, we observe that given a large sparse matrix, if it is in the dense form, the speed is around `40%` slower than that in a sparse form. ## Simulate data We randomly simulate a `n=1000` by `p=1000` dense matrix and a sparse matrix at sparsity $99\%$, i.e. $99\%$ entries are zeros. ```{r} create_sparsity_mat = function(sparsity, n, p) { nonzero <- round(n*p*(1-sparsity)) nonzero.idx <- sample(n*p, nonzero) mat <- numeric(n*p) mat[nonzero.idx] <- 1 mat <- matrix(mat, nrow=n, ncol=p) return(mat) } ``` ```{r} n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,300,400,1000)] <- 10 X.dense <- create_sparsity_mat(0.99,n,p) X.sparse <- as(X.dense,"dgCMatrix") y <- c(X.dense %*% beta + rnorm(n)) ``` ## `X` in a dense form ```{r} susie.dense <- susie(X.dense,y) ``` ## `X` in a sparse form ```{r} susie.sparse <- susie(X.sparse,y) ``` ## Further step We encourage people who are insterested in improving SuSiE can get insights from those line profiles provided. susieR/inst/doc/finemapping.html0000644000176200001440000044044314143477577016465 0ustar liggesusers Fine-mapping example

Fine-mapping example

Gao Wang

2021-11-12

This vignette demonstrates susieR in the context of genetic fine-mapping. We use simulated data of expression level of a gene (\(y\)) in \(N \approx 600\) individuals. We want to identify with the genotype matrix \(X_{N\times P}\) (\(P=1001\)) the genetic variables that causes changes in expression level.

The simulated data set is simulated to have exactly 3 non-zero effects.

The data-set

The loaded dataset contains regression data \(X\) and \(y\), along with some other relevant properties in the context of genetic studies. It also contains the “true” regression coefficent the data is simulated from.

Notice that we’ve simulated 2 sets of \(Y\) as 2 simulation replicates. Here we’ll focus on the first data-set.

Here are the 3 “true” signals in the first data-set:

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So the underlying causal variables are 403, 653 and 773.

Simple regression summary statistics

univariate_regression function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are \(\hat{\beta}\) and \(SE(\hat{\beta})\) from which z-scores can be derived. Again we focus only on results from the first data-set:

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Fine-mapping with susieR

For starters, we assume there are at most 10 causal variables, i.e., set L = 10, although SuSiE is robust to the choice of L.

The susieR function call is:

Posterior inclusion probabilities

Previously we’ve determined that summing over 3 single effect regression models is approperate for our application. Here we summarize the variable selection results by posterior inclusion probability (PIP):

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The true causal variables are colored red. The 95% CS identified are circled in different colors. Of interest is the cluster around position 400. The true signal is 403 but apparently it does not have the highest PIP. To compare ranking of PIP and original z-score in that CS:

Choice of priors

Notice that by default SuSiE estimates prior effect size from data. For fine-mapping applications, however, we sometimes have knowledge of SuSiE prior effect size since it is parameterized as percentage of variance explained (PVE) by a non-zero effect, which, in the context of fine-mapping, is related to per-SNP heritability. It is possible to use scaled_prior_variance to specify this PVE and explicitly set estimate_prior_variance=FALSE to fix the prior effect to given value.

In this data-set, SuSiE is robust to choice of priors. Here we set PVE to 0.2, and compare with previous results:

&nbsp;

 

which largely remains unchanged.

A note on covariate adjustment

To include covariate Z in SuSiE, one approach is to regress it out from both y and X, and then run SuSiE on the residuals. The code below illustrates the procedure:

Note that the covariates Z should have a column of ones as the first column. If not, the above function remove.covariate.effects will add such a column to Z before regressing it out. Data will be centered as a result. Also the scale of data is changed after regressing out Z. This introduces some subtleties in terms of interpreting the results. For this reason, we provide covariate adjustment procedure as a tip in the documentation and not part of susieR::susie() function. Cautions should be taken when applying this procedure and interpreting the result from it.

susieR/inst/doc/mwe.R0000644000176200001440000000140214143477604014170 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) set.seed(1) n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,2,300,400)] <- 1 X <- matrix(rnorm(n*p),nrow=n,ncol=p) y <- X %*% beta + rnorm(n) res <- susie(X,y,L=10) plot(coef(res)[-1],pch = 20) ## ----fig.height=3.5, fig.width=3.5-------------------------------------------- plot(y,predict(res),pch = 20) ## ----------------------------------------------------------------------------- sessionInfo() susieR/inst/doc/susie_refine.R0000644000176200001440000000264014143477630016064 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) library(curl) data_file <- tempfile(fileext = ".RData") data_url <- paste0("https://raw.githubusercontent.com/stephenslab/susieR/", "master/inst/datafiles/FinemappingConvergence1k.RData") curl_download(data_url,data_file) load(data_file) b <- FinemappingConvergence$true_coef susie_plot(FinemappingConvergence$z, y = "z", b=b) ## ----------------------------------------------------------------------------- fitted <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n)) susie_plot(fitted, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted),2))) ## ----------------------------------------------------------------------------- fitted_refine <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n, refine=TRUE)) susie_plot(fitted_refine, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted_refine),2))) ## ----------------------------------------------------------------------------- sessionInfo() susieR/inst/doc/finemapping_summary_statistics.R0000644000176200001440000000330114143477573021731 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) set.seed(1) ## ----------------------------------------------------------------------------- data(N3finemapping) attach(N3finemapping) ## ----------------------------------------------------------------------------- dim(Y) ## ----------------------------------------------------------------------------- b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ## ----------------------------------------------------------------------------- which(b != 0) ## ----------------------------------------------------------------------------- sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ## ----------------------------------------------------------------------------- R <- cor(X) ## ----------------------------------------------------------------------------- fitted_rss <- susie_rss(z_scores, R, L = 10) ## ----------------------------------------------------------------------------- summary(fitted_rss)$cs ## ----------------------------------------------------------------------------- susie_plot(fitted_rss, y="PIP", b=b) ## ---- fig.width=6,fig.height=6------------------------------------------------ fitted = susie(X, Y[,1], L = 10) plot(fitted$pip, fitted_rss$pip, ylim=c(0,1)) ## ----------------------------------------------------------------------------- sessionInfo() susieR/inst/doc/trendfiltering_derivations.pdf0000644000176200001440000054337214143477737021427 0ustar liggesusers%PDF-1.5 % 3 0 obj << /Length 3080 /Filter /FlateDecode >> stream xڭZY#~_inm?و 8ryu^wFaIf(8LduWE}}wu5ӵ2ڻۙrVBV~vw={ޚz~Vo⭞O|]7to|_W'\Ƒ۟hҪ MmjggZ3Iw/r~f ]ճq4 1Oon& Mi DB7^Htx}8IV Hk+ V@ko-QR#-BVp @kO(%ýLmyUtWZ4 q>gGaB.D7\vwҒ饑7(Oe{Dx_,e'4QORT5P[n_WbIK)c-̥I_tNи8b Lf{xiyw`ԓ"Ʒq֢.f[_6}b-ıqj9J<UMn~)0!\ow8(i\ՄZ5D2)cfegsf3Fli q ( 8۫f(7+$ }jGC7K:fgizK67HY{HNKќ{"F6H"xVg]+KH2vJꀔlhB/ג6Q G"xYt|`TЇ 3nc0퉵J61n~SW4ū: V" T,'4P4hA}L0I n%dP.JMZŘ0[2įA Y`Z~P~BٗCX ^"OԟK47ca8_Z۱#Vb KV U17ʶ89?+N.yL sQbiÐ ]Z 8og-=",V4%KzEٙ8)R%DIlrm=e(yƄTL $0m4=isƨ nwƞs#:C8?RcpT ۚ r<% 1A Hu9B3? 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A minimal example

Matthew Stephens

2021-11-12

In this short vignette, we fit a sparse linear regression model with up to \(L > 0\) non-zero effects. Generally, there is no harm in over-stating \(L\) (that is, the method is pretty robust to overfitting), except that computation will grow as \(L\) grows.

Here is a minimal example:

&nbsp;

 

Plot the ground-truth outcomes vs. the predicted outcomes:

&nbsp;

 

Session information

Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results.

susieR/inst/doc/finemapping_summary_statistics.Rmd0000644000176200001440000001002014075324172022234 0ustar liggesusers--- title: "Fine-mapping with summary statistics" author: "Yuxin Zou and Gao Wang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Fine-mapping with summary statistics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates how to use `susieR` with "summary statistics" in the context of genetic fine-mapping. We use the same simulated data as in [fine mapping vignette](finemapping.html). The simulated data is expression level of a gene ($y$) in $N \approx 600$ individuals. We want to identify with the genotype matrix $X_{N\times P}$ ($P=1001$) the genetic variables that causes changes in expression level. The data-set is shipped with `susieR`. It is simulated to have exactly 3 non-zero effects. ```{r} library(susieR) set.seed(1) ``` ## The data-set ```{r} data(N3finemapping) attach(N3finemapping) ``` Notice that we've simulated 2 sets of $Y$ as 2 simulation replicates. Here we'll focus on the first data-set. ```{r} dim(Y) ``` Here are the 3 "true" signals in the first data-set: ```{r} b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ``` ```{r} which(b != 0) ``` So the underlying causal variables are 403, 653 and 773. ## Summary statistics from simple regression Summary statistics of genetic association studies typically contain effect size ($\hat{\beta}$ coefficient from regression), p-value and minor allele frequencies. These information can be used to perform fine-mapping with given an additional input of correlation matrix between variables. The correlation matrix in genetics is typically referred to as LD matrix (LD for linkage disequilibrium). One may use external reference panels to estimate it when this matrix cannot be obtained from samples directly. Caution that LD matrix here has to be correlation matrix $r$, not $r^2$ or $abs(r)$. `univariate_regression` function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are $\hat{\beta}$ and $SE(\hat{\beta})$ from which z-scores can be derived. Alternatively you can obtain z-scores from $\hat{\beta}$ and p-values if you are provided with those information. Again we focus only on results from the first data-set: ```{r} sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ``` For this example the correlation matrix can be computed directly from data provide, ```{r} R <- cor(X) ``` ## Fine-mapping with `susieR` using summary statistics For starters, we assume there are at most 10 causal variables, i.e. set `L = 10`, although SuSiE is generally robust to the choice of `L`. SuSiE regression with summary statistics is implemented as `susie_rss` function, ```{r} fitted_rss <- susie_rss(z_scores, R, L = 10) ``` Using `summary` function, we can examine the posterior inclusion probability (PIP) for each variable, and the 95% credible sets. Here, we are the 95% credible sets. ```{r} summary(fitted_rss)$cs ``` The 3 causal signals have been captured by the 3 CS reported here. The 3rd CS contains many variables, including the true causal variable 403. We can also plot the posterior inclusion probability (PIP), ```{r} susie_plot(fitted_rss, y="PIP", b=b) ``` The true causal variables are colored red. The 95% CS identified are circled in different colors. The result should be very similar to using the individual level data for this example, as the z-scores and correlation matrix (LD reference) come from the same genotype data-set. ```{r, fig.width=6,fig.height=6} fitted = susie(X, Y[,1], L = 10) plot(fitted$pip, fitted_rss$pip, ylim=c(0,1)) ``` ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/inst/doc/mwe.Rmd0000644000176200001440000000237614033741524014515 0ustar liggesusers--- title: "A minimal example" author: "Matthew Stephens" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{minimal example} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` In this short vignette, we fit a sparse linear regression model with up to $L > 0$ non-zero effects. Generally, there is no harm in over-stating $L$ (that is, the method is pretty robust to overfitting), except that computation will grow as $L$ grows. Here is a minimal example: ```{r} library(susieR) set.seed(1) n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,2,300,400)] <- 1 X <- matrix(rnorm(n*p),nrow=n,ncol=p) y <- X %*% beta + rnorm(n) res <- susie(X,y,L=10) plot(coef(res)[-1],pch = 20) ``` Plot the ground-truth outcomes vs. the predicted outcomes: ```{r fig.height=3.5, fig.width=3.5} plot(y,predict(res),pch = 20) ``` ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/inst/doc/trend_filtering.html0000644000176200001440000042312114143477734017334 0ustar liggesusers Trend filtering

Trend filtering

Matthew Stephens

2021-11-12

Introduction

Although we developed SuSiE primarily with the goal of performing variable selection in highly sparse settings – and, in particular, for genetic fine-mapping – the approach also has considerable potential for application to other large-scale regression problems. Here we briefly illustrate this potential by applying it to a non-parametric regression problem that at first sight seems to be ill-suited to our approach. In particular, it does not involve strict sparsity, and the underlying correlation structure of the explanatory variables is very different from the ``blocky" covariance structure of genetic data that SuSiE was designed for. Nonetheless, we will see that SuSiE performs well here despite this (partly due to its ability to capture non-sparse signals via Bayesian Model Averaging).

Specifically, consider the non-parametric regression: \[y_t = \mu_t + e_t \quad t=1,\dots,T\] where the goal is to estimate the underlying mean, \(\mu_t\), under the assumption that it varies smoothly (or, more precisely, in a spatially-structured way) with \(t\). One very simple way to capture spatial structure in \(\mu\) is to model it as a (sparse) linear combination of step functions: \[\mu = Xb\] where the \(j\)th column of \(X\) is the step function with a step at \(j\) (\(j = 1,\dots,(T-1)\)); that is \(x_{tj}=0\) for \(t<=j\) and 1 for \(t>j\). The \(j\)th element of \(b\) therefore determines the change in the mean \(|\mu_j-\mu_{j+1}|\), and an assumption that \(b\) is sparse encapsulates an assumption that \(\mu\) is spatially structured (indeed, piecewise constant).

This very simple approach is essentially 0th-order trend filtering (Tibshirani, 2014, Annals of Statistics 42, 285–323). Note that higher-order trend filtering can be similarly implemented using different basis functions; here we use 0th order only for simplicity.

Examples

Here we apply SuSiE to perform 0th order trend filtering in some simple simulated examples. We have implemented in susieR 0.6.0 a funciton susie_trendfilter which internally creates \(X\) matrix with step functions in the columns to match input \(y\). The algebra have been optimized to work on such trendfiltering matrices. Here we simulate some data where \(\mu\) is a step function with four steps, a 0th order trendfiltering problem. The regression is truly sparse and SuSiE should do well.

Plot results: the truth is black, and susie estimate is red.

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In the next example mu increases linearly. Thus we are approximating a linear function by step functions. Here the truth is not trully ``sparse", so we might expect performance to be poor, but it is not too bad.

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Compare with the genlasso (lasso-based) solution (blue). (This will require installation of the genlasso package.)

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What happens if we have linear trend plus a sudden change.

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The two fits seem similar in accuracy. We can check this numerically:

susieR/inst/doc/finemapping_summary_statistics.html0000644000176200001440000043443314143477574022513 0ustar liggesusers Fine-mapping with summary statistics

Fine-mapping with summary statistics

Yuxin Zou and Gao Wang

2021-11-12

This vignette demonstrates how to use susieR with “summary statistics” in the context of genetic fine-mapping. We use the same simulated data as in fine mapping vignette. The simulated data is expression level of a gene (\(y\)) in \(N \approx 600\) individuals. We want to identify with the genotype matrix \(X_{N\times P}\) (\(P=1001\)) the genetic variables that causes changes in expression level. The data-set is shipped with susieR. It is simulated to have exactly 3 non-zero effects.

The data-set

Notice that we’ve simulated 2 sets of \(Y\) as 2 simulation replicates. Here we’ll focus on the first data-set.

Here are the 3 “true” signals in the first data-set:

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So the underlying causal variables are 403, 653 and 773.

Summary statistics from simple regression

Summary statistics of genetic association studies typically contain effect size (\(\hat{\beta}\) coefficient from regression), p-value and minor allele frequencies. These information can be used to perform fine-mapping with given an additional input of correlation matrix between variables. The correlation matrix in genetics is typically referred to as LD matrix (LD for linkage disequilibrium). One may use external reference panels to estimate it when this matrix cannot be obtained from samples directly. Caution that LD matrix here has to be correlation matrix \(r\), not \(r^2\) or \(abs(r)\).

univariate_regression function can be used to compute summary statistics by fitting univariate simple regression variable by variable. The results are \(\hat{\beta}\) and \(SE(\hat{\beta})\) from which z-scores can be derived. Alternatively you can obtain z-scores from \(\hat{\beta}\) and p-values if you are provided with those information. Again we focus only on results from the first data-set:

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For this example the correlation matrix can be computed directly from data provide,

Fine-mapping with susieR using summary statistics

For starters, we assume there are at most 10 causal variables, i.e. set L = 10, although SuSiE is generally robust to the choice of L.

SuSiE regression with summary statistics is implemented as susie_rss function,

Using summary function, we can examine the posterior inclusion probability (PIP) for each variable, and the 95% credible sets.

Here, we are the 95% credible sets.

The 3 causal signals have been captured by the 3 CS reported here. The 3rd CS contains many variables, including the true causal variable 403.

We can also plot the posterior inclusion probability (PIP),

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The true causal variables are colored red. The 95% CS identified are circled in different colors.

The result should be very similar to using the individual level data for this example, as the z-scores and correlation matrix (LD reference) come from the same genotype data-set.

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susieR/inst/doc/trendfiltering_derivations.Rnw0000644000176200001440000002761114051613310021366 0ustar liggesusers\documentclass[11pt]{article} \usepackage{Sweave} \usepackage{fullpage} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{bm} \usepackage{mathabx} \usepackage{xspace} \usepackage{natbib} \bibliographystyle{abbrvnat} \newcommand{\susie}{\textsl{SuSiE}\xspace} \def\vb{\bm b} \def\vy{\bm y} \def\vx{\bm x} \begin{document} \title{Implementation of SuSiE trend filtering} \author{Kaiqian Zhang} \maketitle %\VignetteIndexEntry{Implementation of SuSiE trend filtering} \section{Notation} We first describe the notation used in this text. We denote matrices by boldface uppercase letters ($\mathbf{A}$), vectors are denoted by boldface lowercase letters ($\mathbf{a}$), and scalars are denoted by non-boldface letters ($a$ or $A$). All vectors are column-vectors. Lowercase letters may represent elements of a vector or matrix if they have subscripts. For example, $a_{ij}$ is the $(i,j)$th element of $\mathbf{A}$, $a_i$ is the $i$th element of $\mathbf{a}$, and $\mathbf{a}_{i}$ is either the $i$th row or $i$th column of $\mathbf{A}$. For indexing, we will generally use capital non-boldface letters to denote the total number of elements and their lowercase non-boldface versions to denote the index. For example, $i = 1,\ldots,I$. We let $\mathbf{A}_{n \times p}$ denote that $\mathbf{A} \in \mathbb{R}^{n \times p}$. We denote the matrix transpose by $\mathbf{A}^T$, the matrix inverse by $\mathbf{A}^{-1}$, and the matrix determinant by $\det(\mathbf{A})$. Finally, sets will be denoted by calligraphic letters ($\mathcal{A}$). \section{Overview} Trend filtering is a useful statistical tool for nonparametric regression. \cite{Kim07l1trend} first proposed $\ell_1$ trend filtering for estimating underlying piecewise linear trends in time series data. This idea can be further extended to fit piecewise polynomial of degree $k$ to the data. In their paper, Kim et al. showed the equivalence between the $\ell_1$ trend filtering and the $\ell_1$-regularized least squares problem. This motivates us to think about the connection between trend filtering and sparse approximation in general. \section{Trend filtering and sparse regression} Trend filtering problem is defined mathematically as follows. For a given integer $k \geq 0$, the kth order trend filtering is defined by a penalized least squares optimization problem, \begin{align} \hat{\vb} = \underset{\vb}{\mathrm{argmin}} \frac{1}{2}|| \vy - \vb||_2^2 + \frac{n^k}{k!}\lambda||D_{k+1}\vb||_1, \end{align} where $\vy = [y_1 \dots y_n]^T$ is an n vector of observations, $\lambda$ is a tuning parameter, and $D_{k+1}$ is the discrete difference operator of order $k$. When order $k=0$, $D$ is defined \begin{align}\label{D1} D_{1} = \begin{bmatrix} -1 & 1 & 0 & \dots & 0 & 0\\ 0 & -1 & 1 & \dots & 0 & 0\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & 1\\ \end{bmatrix} \in \mathbb{R}^{(n-1)\times n}. \end{align} In this case, the components of the trend filtering estimate form a piecewise constant structure, with break points corresponding to the nonzero entries of $D_{1}\hat{\vb} = (\hat{b}_2 - \hat{b}_1, \dots, \hat{b}_n - \hat{b}_{n-1})$ \citep{Tibshirani2014}. And when $k\geq 1$, the operator $D_{k+1}$ is defined recursively, \begin{align}\label{D1_2} D_{k+1} = D_{1} \cdot D_{k} \in \mathbb{R}^{(n-k-1)\times n}, \end{align} where the dot product is matrix multiplication. Notice that $D_1$ here in \ref{D1_2} is the $(n-k-1)\times (n-k)$ version of $D_1$ described in \ref{D1}. Now we want to transform the trend filtering problem into a sparse regression problem. Let $\bm{\beta}=D_{k+1}\vb$. Then if $D_{k+1}$ were invertibe, we could write $\vb = (D_{k+1})^{-1}\bm{\beta}$ and the above problem would become \begin{align} \hat{\bm{\beta}} = \underset{\bm{\beta}}{\mathrm{argmin}} \frac{1}{2}||\vy - (D_{k+1})^{-1}\bm{\beta}||_2^2 + \frac{n^k}{k!}\lambda||\bm{\beta}||_1. \end{align} We can consider this a sparse regression with $\ell_1$ regularization problem, where the design matrix is $X_{k+1} = (D_{k+1})^{-1}$. \section{Modification of $D$} As we have seen, the trend filtering problem becomes a sparse regression with $\ell_1$ regularization if we consider the design matrix $X_{k+1} = (D_{k+1})^{-1}$. However, $D_{1} \in \mathbb{R}^{(n-1)\times n}$ is not invertible, so is $D_{k+1}$ for $k=1,2,\dots$ By observation, we complete $D_{1}$ as a square and symmetric matrix \begin{align} \hat{D}_{1} = \begin{bmatrix} -1 & 1 & 0 & \dots & 0 & 0\\ 0 & -1 & 1 & \dots & 0 & 0\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & 1\\ 0 & 0 & 0 & \dots & 0 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}. \end{align} And for $k\geq 1$, we obtain \begin{align} \hat{D}_{k+1} = \hat{D}_{1} \cdot \hat{D}_{k} \in \mathbb{R}^{n\times n}. \end{align} We notice that, by this modification, $\hat{D}_{k+1}$ has $k$ more rows added at the bottom without changing any previous entry. With this modification, we are able to invert $\hat{D}_{k+1}$ and consider the inverse matrix as a design matrix in the sparse regression. \section{Special structure of $\hat{D}^{-1}$} After determining the design matrix $X$ in the sparse regression problem, we could apply \susie algorithm to help us find a possible fit. Here, we denote $X_{k+1} = (\hat{D}_{k+1})^{-1}$, where $k$ is the order of trend filtering. Rather than generating $\hat{D}^{-1}$ and set this as an $X$ input, we exploit the special structure of $\hat{D}^{-1}$ and perform \susie on this specific trend filtering problem with $O(n)$ complexity. We will talk about how to make different computations linear in complexity by utilizing the special structure respectively. \section{Computation of $Xb$} \label{computation-on-Xb} In the trend filtering application, since $X_{k+1} = (\hat{D}_{k+1})^{-1}$, we obtain \begin{align} X_{k+1} \vb & = (\hat{D}_{k+1})^{-1} \vb = (\underbrace{\hat{D}_{1}\dots \hat{D}_{1}}_{k+1})^{-1} \vb \\ &= \underbrace{(\hat{D}_{1})^{-1} \dots (\hat{D}_{1})^{-1}}_{k+1} \vb \\ &= \underbrace{X_1 \dots X_1}_{k+1} \vb. \end{align} We notice that since \begin{align} X_1 = \begin{bmatrix} -1 & -1 & -1 & \dots & -1 & -1\\ 0 & -1 & -1 & \dots & -1 & -1\\ \vdots & \ddots & \\ 0 & 0 & 0 & \dots & -1 & -1\\ 0 & 0 & 0 & \dots & 0 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_1 \vb & = -1 \cdot [b_1+b_2+\dots+b_n, b_2+\dots+b_n, \dots, b_{n-1}+b_n, b_n]^T \\ & = -1 \cdot \text{cumsum}(\text{reverse}(\vb)) . \end{align} Let $f: \mathbb{R}^n \to \mathbb{R}^n$ such that $f(\vx)= -\text{cumsum}(\text{reverse}(\vx))$ for any $\vx \in \mathbb{R}^n$. Then \begin{align} X_{k+1} \vb & = \underbrace{X_1 \dots X_1}_{k+1} \vb = f^{(k+1)}(\vb), \end{align} where $k$ is the order of trend filtering. \section{Computation of $X^Ty$} \label{computation-on-Xty} We consider $X_{k+1}^T\vy$. Here $X_{k+1} = (\hat{D}_{k+1})^{-1}$ in the trend filtering problem, and $\vy$ is an n vector. We have \begin{align} X_{k+1}^T \vy & = ((\hat{D}_{k+1})^{-1})^T \vy = ((\underbrace{\hat{D}_{1}\dots \hat{D}_{1}}_{k+1})^{-1})^T \vy \\ & = \underbrace{((\hat{D}_{1})^{-1})^T \dots ((\hat{D}_{1})^{-1})^T}_{k+1} \vy \\ & = \underbrace{X_1^T \dots X_1^T}_{k+1} \vy. \end{align} Similarly, we observe that since \begin{align} X_1^T = \begin{bmatrix} -1 & 0 & 0 & \dots & 0 & 0\\ -1 & -1 & 0 & \dots & 0 & 0\\ \vdots & \ddots & \\ -1 & -1 & -1 & \dots & -1 & 0\\ -1 & -1 & -1 & \dots & -1 & -1 \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_1^T \vy & = -1 \cdot [y_1, y_1+y_2, \dots, y_1+y_2+\dots+y_n]^T \\ & = -1 \cdot \text{cumsum}(\vy). \end{align} Let $g: \mathbb{R}^n \to \mathbb{R}^n$ such that $g(\vx)= -\text{cumsum}(\vx)$ for any $\vx \in \mathbb{R}^n$. Then \begin{align} X_{k+1}^T \vy & = \underbrace{X_1^T \dots X_1^T}_{k+1} \vy = g^{(k+1)}(\vy), \end{align} where $k$ is the order of trend filtering. \section{Computation on $(X^2)^T 1$; i.e., colSums($X^2$)} \label{computation-on-d} To compute $(X_{k+1}^2)^T \bm{1}$, let's first explore the special structure of $X_{k+1}=(\hat{D}^{(k+1)})^{-1}$ for $k=0,1,2$. \begin{align} X_1 = \begin{bmatrix} -1 & -1 & -1 & -1 & -1 & \dots \\ 0 & -1 & -1 & -1 & -1 & \dots \\ 0 & 0 & -1 & -1 & -1 & \dots \\ 0 & 0 & 0 & -1 & -1 & \dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_2 = \begin{bmatrix} 1 & 2 & 3 & 4 & 5 & 6 & \dots \\ 0 & 1 & 2 & 3 & 4 & 5 & \dots \\ 0 & 0 & 1 & 2 & 3 & 4 &\dots \\ 0 & 0 & 0 & 1 & 2 & 3 &\dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} \begin{align} X_3 = \begin{bmatrix} -1 & -3 & -6 & -10 & -15 & \dots \\ 0 & -1 & -3 & -6 & -10 & \dots \\ 0 & 0 & -1 & -3 & -6 & \dots \\ 0 & 0 & 0 & -1 & -3 & \dots \\ \vdots & \ddots & \\ \end{bmatrix} \in \mathbb{R}^{n\times n}, \end{align} Define a triangular rotate matrix $Q \in \mathbb{R}^{n\times n}$ such that (i) For any $i, j \leq n$, $Q_{ij} = 0$ if $i>j$. (ii) For any $k < n$, $Q_{ab} = Q_{cd}$ if $b-a = d-c = k$. We observe that if $X$ is a triangular rotate matrix, then \begin{align} X^T \bm{1} = \text{cumsum}(X_{1.}). \end{align} Since $X^2$ is still a triangular rotate matrix, we obtain \begin{align} (X^2)^T \bm{1} = \text{cumsum}(X^2_{1.}). \end{align} Since $X_{k+1} = (\hat{D}_{k+1})^{-1}$ is a triangular rotate matrix, \begin{align} (X_{k+1}^2)^T \bm{1} = \text{cumsum}((X_{k+1})_{1.}^2). \end{align} And, obviously, the first row of $X_{k+1}$ is \begin{equation} (X_{k+1})_{1.} = \begin{cases} \bm{-1} & \text{if } k = 0 \\ g^{(k)}(\bm{1}) & \text{if } k > 0. \end{cases} \end{equation} \section{Computation of $\mu$; i.e., column means} \label{computation-on-cm} For each column $j = 1,2,\dots,n$, \begin{equation} \mu_j = E[X_{.j}], \end{equation} and $\bm{\mu} = [\mu_1, \mu_2, \dots, \mu_n]^T \in \mathbb{R}^n$. Hence we get \begin{equation} \bm{\mu} = \frac{1}{n} X_{k+1}^T \bm{1} = \frac{1}{n} \text{cumsum}((X_{k+1})_{1.}), \end{equation} where $(X_{k+1})_{1.}$ is defined above in \ref{computation-on-d}. \section{Computation of $\sigma$; i.e., column standard deviations} \label{computation-on-csd} For each column $j=1,2,\dots, n$, \begin{align} \sigma_j & = \sqrt{E[X_{.j}^2] - E[X_{.j}]^2} \\ & = \sqrt{\frac{1}{n}\sum_{i=1}^{n}X_{ij}^2 - (\frac{1}{n}\sum_{i=1}^{n} X_{ij})^2}. \end{align} Hence, $\bm{\sigma} = [\sigma_1, \sigma_2, \dots, \sigma_n]^T \in \mathbb{R}^n$ becomes \begin{align} \bm{\sigma} & = \sqrt{E[X^2] - E[X]^2} \\ & = \sqrt{\frac{1}{n}\text{colSums}(X^2) - (\frac{1}{n}\text{colSums}(X))^2} \\ & = \sqrt{\frac{1}{n}(X^2)^T \bm{1} + (\frac{1}{n} X^T \bm{1})^2}, \end{align} where the first term involves \ref{computation-on-d} and the second term is computed in \ref{computation-on-cm}. Note that in the algorithm, we set the column standard deviation 1 when the column has variance 0 for computation convenience. \section{Computation on $(\hat{X}^2)^T 1$; i.e., colSums($\hat{X}^2$)} \label{computation-on-std-d} We want to compute colSums($\hat{X}^2$), where $\hat{X}$ is scaled by both column means $\bm{\mu} = [\mu_1, \mu_2, \ldots, \mu_n]^T \in \mathbb{R}^n$ and column standard deviations $\bm{\sigma} = [\sigma_1, \sigma_2, \dots, \sigma_n]^T \in \mathbb{R}^n$. We define $\hat{X} \in \mathbb{R}^{n \times n}$ such that for each $i = 1,2,\ldots,n$ and $j=1,2,\dots,n$, \begin{equation*} \hat{X}_{ij} = (X_{ij} - \mu_j)/\sigma_j, \end{equation*} where $X = X_{k+1} = (\hat{D}_{k+1})^{-1}$ if the order is $k$. \section{Conclusion} Computation details from section \ref{computation-on-Xb} to section \ref{computation-on-std-d} explain how we can benefit from the unique structure of matrices from trend filtering problem. As shown by our formula, we do not need to form any matrix and complete \susie algorithm with $O(n)$ complexity. \bibliography{trendfiltering_derivations} \end{document} susieR/inst/doc/l0_initialization.Rmd0000644000176200001440000000376313350763557017363 0ustar liggesusers--- title: "SuSiE with L0Learn initialization example" author: "Kaiqian Zhang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{L0Learn initialization demo} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#") ``` `susieR` allows for a customized initialization. In this vignette we deomonstrate how to use [`L0Learn`](https://github.com/hazimehh/L0Learn) fit to initialize susieR. ```{r, warning=FALSE} library(susieR) library(L0Learn) ``` ## Simulate data First, we simulate data from the minimal example. ```{r} set.seed(1) n = 1000 p = 1000 beta = rep(0,p) beta[c(1,2,300,400)] = 1 X = matrix(rnorm(n*p),nrow=n,ncol=p) y = X %*% beta + rnorm(n) ``` ## Fit L0Learn We start with fitting a L0-regularized model to the simulated data. ```{r} set.seed(1) L0fit = L0Learn.cvfit(X, y, penalty = "L0") ``` Let's choose the penalty strength parameter that minimizes the cross-validation error. ```{r} lambdaIndex = which.min(L0fit$cvMeans[[1]]) L0coef = as.numeric(coef(L0fit$fit, lambda = L0fit$fit$lambda[[1]][lambdaIndex])) effect.beta = L0coef[which(L0coef!=0)][-1] effect.index = (which(L0coef!=0)-1)[-1] length(effect.beta) effect.beta[1:10] effect.index[1:10] ``` The L0Learn model finds `r length(effect.beta)` effects, which will be used to initialize susie. ## Build an initialization object We create an initialization from `l0learn` coefficients using `susie_init_coef` function, ```{r} set.seed(1) s.init = susie_init_coef(effect.index, effect.beta, p) ``` ## Run susieR with initialization Now, we use `effect.index` and `effect.beta` obtained from L0Learn fit to create an `s.init` object. We then run susie with this initialization. ```{r} susieL0.fit = susie(X,y,s_init=s.init) susieL0.fit$sets$cs ``` ## References 1. Hussein Hazimeh and Rahul Mazumder. (2018). Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms. susieR/inst/doc/susie_refine.Rmd0000644000176200001440000000531214113171573016376 0ustar liggesusers--- title: "Refine SuSiE model" author: "Yuxin Zou" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Refine SuSiE model} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` In this vignette, we demonstrate a procedure that helps SuSiE get out of local optimum. We simulate phenotype using UK Biobank genotypes from 50,000 individuals. There are 1001 SNPs. It is simulated to have exactly 2 non-zero effects at 234, 287. ```{r} library(susieR) library(curl) data_file <- tempfile(fileext = ".RData") data_url <- paste0("https://raw.githubusercontent.com/stephenslab/susieR/", "master/inst/datafiles/FinemappingConvergence1k.RData") curl_download(data_url,data_file) load(data_file) b <- FinemappingConvergence$true_coef susie_plot(FinemappingConvergence$z, y = "z", b=b) ``` The strongest marginal association is a non-effect SNP. Since the sample size is large, we use sufficient statistics ($X^\intercal X, X^\intercal y, y^\intercal y$ and sample size $n$) to fit susie model. It identifies 2 Credible Sets, one of them is false positive. This is because `susieR` get stuck around a local minimum. ```{r} fitted <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n)) susie_plot(fitted, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted),2))) ``` Our refine procedure to get out of local optimum is 1. fit a susie model, $s$ (suppose it has $K$ CSs). 2. for CS in $s$, set SNPs in CS to have prior weight 0, fit susie model --> we have K susie models: $t_1, \cdots, t_K$. 3. for each $k = 1, \cdots, K$, fit susie with initialization at $t_k$ ($\alpha, \mu, \mu^2$) --> $s_k$ 4. if $\max_k \text{elbo}(s_k) > \text{elbo}(s)$, set $s = s_{kmax}$ where $kmax = \arg_k \max \text{elbo}(s_k)$ and go to step 2; if no, break. We fit susie model with above procedure by setting `refine = TRUE`. ```{r} fitted_refine <- with(FinemappingConvergence, susie_suff_stat(XtX = XtX, Xty = Xty, yty = yty, n = n, refine=TRUE)) susie_plot(fitted_refine, y="PIP", b=b, main=paste0("ELBO = ", round(susie_get_objective(fitted_refine),2))) ``` With the refine procedure, it identifies 2 CSs with the true signals, and the achieved evidence lower bound (ELBO) is higher. ## Session information Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results. ```{r} sessionInfo() ``` susieR/inst/doc/finemapping.R0000644000176200001440000000551414143477576015715 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) set.seed(1) ## ----------------------------------------------------------------------------- data(N3finemapping) attach(N3finemapping) ## ----------------------------------------------------------------------------- dim(Y) ## ----------------------------------------------------------------------------- b <- true_coef[,1] plot(b, pch=16, ylab='effect size') ## ----------------------------------------------------------------------------- which(b != 0) ## ----------------------------------------------------------------------------- sumstats <- univariate_regression(X, Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat susie_plot(z_scores, y = "z", b=b) ## ----------------------------------------------------------------------------- fitted <- susie(X, Y[,1], L = 10, verbose = TRUE) ## ----------------------------------------------------------------------------- print(fitted$sets) ## ----------------------------------------------------------------------------- sets <- susie_get_cs(fitted, X = X, coverage = 0.9, min_abs_corr = 0.1) ## ----------------------------------------------------------------------------- print(sets) ## ----------------------------------------------------------------------------- susie_plot(fitted, y="PIP", b=b) ## ----------------------------------------------------------------------------- i <- fitted$sets$cs[[3]] z3 <- cbind(i,z_scores[i],fitted$pip[i]) colnames(z3) <- c('position', 'z-score', 'PIP') z3[order(z3[,2], decreasing = TRUE),] ## ----------------------------------------------------------------------------- fitted = susie(X, Y[,1], L = 10, estimate_residual_variance = TRUE, estimate_prior_variance = FALSE, scaled_prior_variance = 0.2) susie_plot(fitted, y='PIP', b=b) ## ---- eval=FALSE-------------------------------------------------------------- # remove.covariate.effects <- function (X, Z, y) { # # include the intercept term # if (any(Z[,1]!=1)) Z = cbind(1, Z) # A <- forceSymmetric(crossprod(Z)) # SZy <- as.vector(solve(A,c(y %*% Z))) # SZX <- as.matrix(solve(A,t(Z) %*% X)) # y <- y - c(Z %*% SZy) # X <- X - Z %*% SZX # return(list(X = X,y = y,SZy = SZy,SZX = SZX)) # } # # out = remove.covariate.effects(X, Z, Y[,1]) # fitted_adjusted = susie(out$X, out$y, # L = 10) , ## ----------------------------------------------------------------------------- sessionInfo() susieR/inst/doc/susierss_diagnostic.R0000644000176200001440000000514714143477651017500 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ----------------------------------------------------------------------------- library(susieR) library(curl) ## ----------------------------------------------------------------------------- data("N3finemapping") b = N3finemapping$true_coef[,1] sumstats <- univariate_regression(N3finemapping$X, N3finemapping$Y[,1]) z_scores <- sumstats$betahat / sumstats$sebetahat Rin = cor(N3finemapping$X) attr(Rin, "eigen") = eigen(Rin, symmetric = T) susie_plot(z_scores, y = "z", b=b) ## ----------------------------------------------------------------------------- s = estimate_s_rss(z_scores, Rin) s ## ----------------------------------------------------------------------------- condz_in = kriging_rss(z_scores, Rin) condz_in$plot ## ----------------------------------------------------------------------------- fit <- susie_rss(z_scores, Rin) susie_plot(fit,y = "PIP", b=b) ## ----------------------------------------------------------------------------- data_file <- tempfile(fileext = ".RData") data_url <- paste0("https://raw.githubusercontent.com/stephenslab/susieR/", "master/inst/datafiles/SummaryConsistency1k.RData") curl_download(data_url,data_file) load(data_file) zflip = SummaryConsistency$z ld = SummaryConsistency$ldref b = numeric(length(zflip)) b[SummaryConsistency$signal_id] = zflip[SummaryConsistency$signal_id] plot(zflip, pch = 16, col = "#767676", main = "Marginal Associations", xlab="SNP", ylab = "z scores") points(SummaryConsistency$signal_id, zflip[SummaryConsistency$signal_id], col=2, pch=16) points(SummaryConsistency$flip_id, zflip[SummaryConsistency$flip_id], col=7, pch=16) ## ----------------------------------------------------------------------------- fit = susie_rss(zflip, ld) susie_plot(fit, y='PIP', b=b) points(SummaryConsistency$flip_id, fit$pip[SummaryConsistency$flip_id], col=7, pch=16) ## ----------------------------------------------------------------------------- s = estimate_s_rss(zflip, ld) s ## ----------------------------------------------------------------------------- condz = kriging_rss(zflip, ld) condz$plot ## ----------------------------------------------------------------------------- z = zflip z[SummaryConsistency$flip_id] = -z[SummaryConsistency$flip_id] fit = susie_rss(z, ld) susie_plot(fit, y='PIP', b=b) ## ----------------------------------------------------------------------------- sessionInfo() susieR/inst/doc/susierss_diagnostic.Rmd0000644000176200001440000001050614142575215020006 0ustar liggesusers--- title: "Diagnostic for fine-mapping with summary statistics" author: "Yuxin Zou" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Diagnostic for fine-mapping with summary statistics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` This vignette demonstrates diagnostic plot for consistency between summary statistics and reference LD matrix. The `susie_rss` assumes the LD matrix accurately estimate the correlations among SNPs from the original GWAS genotype data. Typically, the LD matrix comes from some public database of genotypes in a suitable reference population. The inaccurate LD information leads to unreliable fine-mapping result. The diagnostic for consistency between summary statistics and refenrence LD matrix is based on the RSS model under the null with regularized LD matrix. $$ \hat{z} | R, s \sim N(0, (1-s)R + s I), 0 SuSiE with L0Learn initialization example

SuSiE with L0Learn initialization example

Kaiqian Zhang

2021-11-12

susieR allows for a customized initialization. In this vignette we deomonstrate how to use L0Learn fit to initialize susieR.

Build an initialization object

We create an initialization from l0learn coefficients using susie_init_coef function,

Run susieR with initialization

Now, we use effect.index and effect.beta obtained from L0Learn fit to create an s.init object. We then run susie with this initialization.

References

  1. Hussein Hazimeh and Rahul Mazumder. (2018). Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms.
susieR/inst/doc/susierss_diagnostic.html0000644000176200001440000075476414143477652020264 0ustar liggesusers Diagnostic for fine-mapping with summary statistics

Diagnostic for fine-mapping with summary statistics

Yuxin Zou

2021-11-12

This vignette demonstrates diagnostic plot for consistency between summary statistics and reference LD matrix.

The susie_rss assumes the LD matrix accurately estimate the correlations among SNPs from the original GWAS genotype data. Typically, the LD matrix comes from some public database of genotypes in a suitable reference population. The inaccurate LD information leads to unreliable fine-mapping result.

The diagnostic for consistency between summary statistics and refenrence LD matrix is based on the RSS model under the null with regularized LD matrix. \[ \hat{z} | R, s \sim N(0, (1-s)R + s I), 0<s<1 \] The parameter s is estimated by maximum likelihood. A larger s means a greater inconsistency between summary statistics and the LD matrix. The expected z score is computed for each SNP, \(E(\hat{z}_j | \hat{z}_{-j})\), and plotted against the observed z scores.

LD information from the original genotype data

We demonstrate the diagnostic plot in a simple case, the LD matrix is estimated from the original genotype data. We use the same simulated data as in fine mapping vignette.

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The estimated s is

The plot for the observed z scores vs the expected z scores is

&nbsp;

 

Summary of SuSiE Credible Sets:

&nbsp;

 

LD information from the reference panel

We use another simulated data where the LD matrix is estimated from a reference panel. There is one signal in the simulated data (red point). There is one SNP with mismatched reference and alternative allele between summary statistics and the reference panel (yellow point).

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Using the data with misaligned allele, SuSiE-RSS identifies a true positive CS containing the true effect SNP; and a false positive CS that incorrectly contains the mismatched SNP.

&nbsp;

 

The estimated s is

In the diagnostic plot, the mismatched SNP shows the largest difference between observed and expected z-scores, and therefore appears furthest away from the diagonal.

&nbsp;

 

After fixing the allele encoding, SuSiE-RSS identifies a single true positive CS containing the true-effect SNP, and the formerly mismatched SNP is (correctly) not included in a CS.

&nbsp;

 

Session information

Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results.

susieR/inst/doc/sparse_susie_eval.R0000644000176200001440000000231314143477606017120 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ## ---- warning=FALSE----------------------------------------------------------- library(Matrix) library(susieR) set.seed(1) ## ----------------------------------------------------------------------------- create_sparsity_mat = function(sparsity, n, p) { nonzero <- round(n*p*(1-sparsity)) nonzero.idx <- sample(n*p, nonzero) mat <- numeric(n*p) mat[nonzero.idx] <- 1 mat <- matrix(mat, nrow=n, ncol=p) return(mat) } ## ----------------------------------------------------------------------------- n <- 1000 p <- 1000 beta <- rep(0,p) beta[c(1,300,400,1000)] <- 10 X.dense <- create_sparsity_mat(0.99,n,p) X.sparse <- as(X.dense,"dgCMatrix") y <- c(X.dense %*% beta + rnorm(n)) ## ----------------------------------------------------------------------------- susie.dense <- susie(X.dense,y) ## ----------------------------------------------------------------------------- susie.sparse <- susie(X.sparse,y) susieR/inst/doc/sparse_susie_eval.html0000644000176200001440000003066714143477606017700 0ustar liggesusers Evaluation of sparse version of SuSiE

Evaluation of sparse version of SuSiE

Kaiqian Zhang

2021-11-12

Overview

In this vignette, we provide line profiles for revised version SuSiE, which allows for a sparse matrix structure. We compare speed performance when the form of the matrix X is dense and sparse.

In this minimal example, we observe that given a large sparse matrix, if it is in the dense form, the speed is around 40% slower than that in a sparse form.

Further step

We encourage people who are insterested in improving SuSiE can get insights from those line profiles provided.

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A simple new approach to variable", "selection in regression, with application to genetic", "fine mapping. Journal of the Royal Statistical Society,", "forthcoming")) susieR/inst/analysis/0000755000176200001440000000000013476534107014336 5ustar liggesuserssusieR/inst/analysis/test_susie_auto.Rmd0000644000176200001440000000167413335050146020217 0ustar liggesusers--- title: "Test susie auto" author: "Matthew Stephens" date: "5/2/2018" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` The goal here is to test the function `susie_auto` which tries to make susie run well even in some tricky situations (eg where $L$ is big, which susie is not well suited to). This is an example Lei Sun showed us from the paper demonstrating that false positives occur early on the Lasso path. Here I change L to 20 instead of 200 to make it run fast to begin with. ```{r} set.seed(777) library(susieR) L <- 20 X <- matrix(rnorm(1010 * 1000), 1010, 1000) beta <- rep(0, 1000) beta[1 : L] <- 100 y <- X %*% beta + rnorm(1010) ``` ```{r} s <- susie_auto(X,y,verbose=TRUE) s$sa2 ``` Now try L=200 ```{r} set.seed(777) L <- 200 X <- matrix(rnorm(1010 * 1000), 1010, 1000) beta <- rep(0, 1000) beta[1 : L] <- 100 y <- X %*% beta + rnorm(1010) s2 <- susie_auto(X,y,verbose=TRUE) s2$sa2 ``` susieR/inst/analysis/testing.Rmd0000644000176200001440000000612113335050146016445 0ustar liggesusers--- title: "test.Rmd" author: "Matthew Stephens" date: "4/14/2018" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` # simulate data This is Lei's example ```{r} set.seed(777) library(susieR) X <- matrix(rnorm(1010 * 1000), 1010, 1000) beta <- rep(0, 1000) beta[1 : 200] <- 100 y <- X %*% beta + rnorm(1010) s = susie(X,y,L=200) plot(coef(s),beta) s$sigma2 # fit <- lm(y ~ X - 1) # mlr.p <- log(summary(fit)$coefficients[, 4]) # mar.p <- c() mar.betahat = c() for (i in 1 : 1000) { fit <- lm(y ~ X[, i] - 1) mar.p[i] <- log(summary(fit)$coefficients[, 4]) mar.betahat[i] <- summary(fit)$coefficients[, 1] } # # pdf("pvalue.pdf", width = 10, height = 5) # par(mfrow = c(1, 2)) # plot(mlr.p, ylab = "log(p-value)", main = "Multiple Linear Regression") # abline(h = log(0.05 / 1000), lty = 2, col = "red") # legend("right", lty = 2, col = "red", "log(0.05/p)") # # plot(mar.p, ylab = "log(p-value)", main = "One-on-One Linear Regression") # abline(h = log(0.05 / 1000), lty = 2, col = "red") ``` Notice that the coefficients are monotonic with betahat. Some shrinkage of zero values is evident, but it is not enough... presumably because sigma2 is way over-estimated. And further we see excessive shrinkage of true signals, presumably because sa2 is too small. ```{r} plot(coef(s),mar.betahat) ``` Here we try fixing $L$ and residual variance to true value. ```{r} strue = susie(X,y,L=200,residual_variance =1,estimate_residual_variance =FALSE) plot(coef(strue),beta) strue$elbo ``` it works!! ```{r} plot(strue$alpha[1,]) plot(strue$alpha[2,]) ``` Try with very small residual variance ```{r} s3 = susie(X,y,L=200, residual_variance = 0.01,estimate_residual_variance = FALSE) plot(coef(s3)) s4 = susie(X,y,s_init = s3) plot(coef(s4)) s4$elbo ``` That is weird it goes away from the solution! Try with estimating prior: ```{r} s5 = susie(X,y,s_init = s3, estimate_prior_variance = TRUE) plot(coef(s5)) s5$elbo sqrt(s5$sa2) sqrt(s4$sa2) ``` much better! Now try too many effects ```{r} s3.300 = susie(X,y,L=300, residual_variance = 0.01,estimate_residual_variance = FALSE) s5.300 = susie(X,y,s_init = s3.300, estimate_prior_variance = TRUE) plot(coef(s3.300)) plot(coef(s5.300)) s3.300$elbo s5.300$elbo sum(s5.300$sa2>0) ``` Now try too many effects but just a very small number of iterations for initial case: ```{r} s3.300.5 = susie(X,y,L=300, residual_variance = 0.01,estimate_residual_variance = FALSE, max_iter = 5) s5.300.5 = susie(X,y,s_init = s3.300.5, estimate_prior_variance = TRUE) plot(coef(s3.300.5)) plot(coef(s5.300.5)) s3.300.5$elbo s5.300.5$elbo plot(colSums(s5.300.5$alpha)) ``` Q: does the initial run with small variance gradually find the smaller effects, or does it get them from the first iteration? Could look at that by doing one iteration at a time. ```{r} s3.300.1 = susie(X,y,L=300, residual_variance = 0.01,estimate_residual_variance = FALSE, max_iter = 1) plot(coef(s3.300.1)) s3.300.2 = susie(X,y,s_init=s3.300.1,estimate_residual_variance = FALSE, max_iter = 1) plot(coef(s3.300.2)) s3.300 = susie(X,y,s_init=s3.300.2,estimate_residual_variance = TRUE) ``` susieR/inst/analysis/optimize.Rmd0000644000176200001440000000227713335050146016640 0ustar liggesusers--- title: "optimize" author: "Matthew Stephens" date: "4/15/2018" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` Diagnose optimization issues with Lei's example ```{r} set.seed(777) devtools::load_all(".") X <- matrix(rnorm(1010 * 1000), 1010, 1000) beta <- rep(0, 1000) beta[1 : 200] <- 100 y <- X %*% beta + rnorm(1010) s = susie(X,y,L=1,estimate_residual_variance = TRUE) Y = y-s$Xr s2 = s$sigma2 x = seq(1,100000,length=100) l = rep(0,100) lg = rep(0,100) for(i in 1:100){ l[i] = loglik(x[i],Y,X,s2) lg[i] = loglik.grad(x[i],Y,X,s2) } plot(x,l) plot(x,lg) # > which.max(l) # [1] 23 # > lg[23] # [1] -2.398905e-07 # > lg[22] # [1] 6.282734e-07 lx = log(x) l2=l lg2=lg for(i in 1:100){ l2[i] = negloglik.logscale(lx[i],Y,X,s2) lg2[i] = negloglik.grad.logscale(lx[i],Y,X,s2) } plot(lx,l2) plot(lx,lg2) y = seq(0,20,length=100) l3=l2 lg3=lg2 for(i in 1:100){ l3[i] = negloglik.logscale(y[i],Y,X,s2) lg3[i] = negloglik.grad.logscale(y[i],Y,X,s2) } plot(y,l3) plot(y,lg3) uniroot(negloglik.grad.logscale,c(-20,20),extendInt = "upX",Y=Y,X=X,s2=s2) ``` So, to summarize, problem seems to be that optim has issues with very flat initial gradient near 0. susieR/inst/code/0000755000176200001440000000000014143477741013427 5ustar liggesuserssusieR/inst/code/gen_original_results.R0000644000176200001440000000411613607476333017772 0ustar liggesusers## results from original susie devtools::install_github("stephenslab/susieR") library(susieR) create_sparsity_mat = function(sparsity, n, p){ nonzero = round(n*p*(1-sparsity)) nonzero.idx = sample(n*p, nonzero) mat = numeric(n*p) mat[nonzero.idx] = 1 mat = matrix(mat, nrow=n, ncol=p) return(mat) } set.seed(1) n = 100 p = 200 beta = rep(0,p) beta[1] = 10 beta[2] = 10 beta[3] = 10 beta[4] = 10 X.dense = create_sparsity_mat(0.99,n,p) y = c(X.dense %*% beta + rnorm(n)) L = 10 residual_variance = 0.8 scaled_prior_variance = 0.2 s = list(alpha=matrix(1/p,nrow=L,ncol=p), mu=matrix(2,nrow=L,ncol=p), mu2=matrix(3,nrow=L,ncol=p), Xr=rep(5,n), KL=rep(1.2,L), sigma2=residual_variance, V=scaled_prior_variance * as.numeric(var(y))) X = susieR:::set_X_attributes(X.dense) Eb = rep(1, p) Eb2 = rep(1, p) s2 = residual_variance V = scaled_prior_variance objective.original.res = susieR::susie_get_objective(s) saveRDS(objective.original.res, 'objective_original_res.rds') Eloglik.original.res = susieR:::Eloglik(X,y,s) saveRDS(Eloglik.original.res, 'Eloglik_original_res.rds') ER2.original.res = susieR:::get_ER2(X,y,s) saveRDS(ER2.original.res, 'ER2_original_res.rds') SER.original.res = susieR:::SER_posterior_e_loglik(X,y,s2,Eb,Eb2) saveRDS(SER.original.res, 'SER_original_res.rds') singleReg.original.res = susieR:::single_effect_regression(y,X,V) saveRDS(singleReg.original.res, 'singleReg_original_res.rds') vbupdate.original.res = susieR:::update_each_effect(X, y, s) saveRDS(vbupdate.original.res, 'vbupdate_original_res.rds') susiefit.original.res = susie(X.dense,y) saveRDS(susiefit.original.res, 'susiefit_original_res.rds') susiefit.original.res2 = susie(X.dense, y, standardize = TRUE, intercept = FALSE) susiefit.original.res3 = susie(X.dense, y, standardize = FALSE, intercept = TRUE) susiefit.original.res4 = susie(X.dense, y, standardize = FALSE, intercept = FALSE) saveRDS(susiefit.original.res2, 'susiefit_original_res2.rds') saveRDS(susiefit.original.res3, 'susiefit_original_res3.rds') saveRDS(susiefit.original.res4, 'susiefit_original_res4.rds') susieR/inst/code/sparse_matrix_strategy.Rmd0000644000176200001440000001203013703336624020665 0ustar liggesusers--- title: "Sparse matrix multiplication strategy" author: "Kaiqian Zhang" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Sparse vs. dense matrix operations} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5, fig.height = 3,fig.align = "center", fig.cap = " ",dpi = 120) ``` ## Set up environment ```{r, warning=FALSE, message=FALSE} library(susieR) library(Matrix) library(microbenchmark) library(ggplot2) set.seed(1) ``` ## Goal Our intention is to use sparse matrix multiplications to help reduce computation time. ## General strategy Given a large sparse matrix `X`, we want to compute some matrix multiplications associated with a scaled $\tilde{X}$. We notice that after scaling, `X` becomes a dense matrix and is not possible for a sparse matrix multiplication. So we construct formulae to apply sparse matrix multiplication first on a standardized `X` since standardization does not affect its sparsity. Then we perform centering to get the same result. ## Types of matrix multiplications There are two types of matrix multiplications we want to investigate: + Compute $\tilde{X}b$, where $\tilde{X}$ is an n by p scaled matrix and $b$ is a p vector. + Compute $\tilde{X}^Ty$, where $\tilde{X}$ is an n by p scaled matrix and $y$ is an n vector. ## Results This strategy has a decent performance when computing both $\tilde{X}b$ and $\tilde{X}^Ty$, compared to simple matrix multiplication `%*%`. ## Strategy formulae details ### Computing $\boldsymbol{\tilde{X}b}$ Suppose we want to compute $\tilde{X}b$, where $\tilde{X}$ is a scaled n by p matrix and $b$ is a p vector. Our goal is to express $\tilde{X}b$ into a term involving unscaled $X$ matrix multiplication to achieve sparse matrix operation. \begin{equation} \begin{aligned} \tilde{X}b &= \sum_{j=1}^{p} \tilde{X}_{\cdot j} b_j \\ &= \sum_{j=1}^{p} \frac{X_{\cdot j}-\mu_j}{\sigma_j}b_j \\ &= \sum_{j=1}^{p}\frac{X_{\cdot j}}{\sigma_j}b_j - \sum_{j=1}^{p} \frac{\mu_j}{\sigma_j}b_j \\ &= X b / \sigma - \mu^Tb/\sigma, \end{aligned} \end{equation} where $\mu$ is a *p*-vector of column means, and $\sigma$ is a *p*-vector of column standard deviations. ### Computing $\boldsymbol{\tilde{X}^Ty}$ Suppose we want to compute $\tilde{X}^Ty$, where $\tilde{X}$ is a scaled n by p matrix and $y$ is an n vector. Similarly, we express $\tilde{X}^Ty$ using unscaled $X$ so that we can perform sparse matrix multiplication. We have the following: \begin{equation} \begin{aligned} \tilde{X}^Ty &= \sum_{i=1}^{n} \tilde{X}_{i.}y_i \\ &= \sum_{i=1}^{n} \frac{X_{i.} - \mu}{\sigma}y_i \\ &= \frac{1}{\sigma}\sum_{i=1}^{n}X_{i.}y_i - \frac{\mu}{\sigma}\sum_{i=1}^{n} y_i \\ &= \frac{1}{\sigma}(X^Ty) - \frac{\mu}{\sigma}y^T 1, \end{aligned} \end{equation} where $\mu$ is a *p*-vector of column means, and $\sigma$ is a *p*-vector of columnwise standard deviations. ## Simulations We simulate an `n = 1000` by `p = 10000` matrix `X` at sparsity $99\%$, i.e. $99\%$ entries are zeros. We compare results between normal matrix computation and our sparse strategy as well as comparing speed using microbenchmark. ```{r} create_sparsity_mat <- function(sparsity, n, p) { nonzero <- round(n*p*(1-sparsity)) nonzero.idx <- sample(n*p, nonzero) mat <- numeric(n*p) mat[nonzero.idx] <- 1 mat <- matrix(mat, nrow=n,ncol=p) return(mat) } n <- 1000 p <- 10000 ``` ```{r} X.dense <- create_sparsity_mat(0.99,n,p) X.sparse <- as(X.dense,"dgCMatrix") X.tilde <- susieR:::set_X_attributes(X.dense) #returns a scaled X if input is a dense matrix X <- susieR:::set_X_attributes(X.sparse) #return an unsacled sparse X if input is a sparse matrix #but computes column means and standard deviations ``` ```{r} b <- rnorm(p) y <- rnorm(n) ``` ### Benchmark for computing $\boldsymbol{\tilde{X}b}$ The final results of two methods when computing $\tilde{X}b$ are very close. ```{r} res1 <- X.tilde %*% b res2 <- susieR:::compute_Xb(X,b) max(abs(res1 - res2)) ``` ```{r} compute_Xb_benchmark <- microbenchmark( dense = (use.normal.Xb <- X.tilde%*%b), sparse = (use.sparse.Xb <- susieR:::compute_Xb(X,b)), times = 20,unit = "s") ``` Our sparse strategy demonstrates an obvious advantage over the normal matrix multiplication in computing $\tilde{X}b$. ```{r} autoplot(compute_Xb_benchmark) ``` ### Benchmark for computing $\boldsymbol{\tilde{X}^Ty}$ The final results of two methods when computing $\tilde{X}^Ty$ are almost the same. ```{r} res3 <- t(X.tilde) %*% y res4 <- susieR:::compute_Xty(X,y) max(abs(res3 - res4)) ``` ```{r} compute_Xty_benchmark = microbenchmark( dense = (use.normal.Xty <- t(X.tilde)%*%y), sparse = (use.sparse.Xty <- susieR:::compute_Xty(X, y)), times = 20,unit = "s") ``` Our sparse strategy evidently has a better performance than the normal method in computing $\tilde{X}^Ty$. ```{r} autoplot(compute_Xty_benchmark) ``` susieR/inst/code/finemap.R0000644000176200001440000000661313350071146015163 0ustar liggesusers#!/usr/bin/env Rscript library(dplyr) library(readr) library(magrittr) #' FINEMAP I/O write_finemap_sumstats <- function(z, LD_file, n, k, prefix) { cfg = list(z=paste0(prefix,".z"), ld=LD_file, snp=paste0(prefix,".snp"), config=paste0(prefix,".config"), k=paste0(prefix,".k"), log=paste0(prefix,".log"), meta=paste0(prefix,".master")) write.table(z,cfg$z,quote=F,col.names=F) if (!is.null(k)) { write.table(t(k),cfg$k,quote=F,col.names=F,row.names=F) write("z;ld;snp;config;k;log;n-ind",file=cfg$meta) write(paste(cfg$z, cfg$ld, cfg$snp, cfg$config, cfg$k, cfg$log, n, sep=";"), file=cfg$meta,append=TRUE) } else { write("z;ld;snp;config;log;n-ind",file=cfg$meta) write(paste(cfg$z, cfg$ld, cfg$snp, cfg$config, cfg$log, n, sep=";"), file=cfg$meta,append=TRUE) } return(cfg) } #' Run FINEMAP version 1.1 #' http://www.christianbenner.com ## FIXME: read the finemapr implementation for data sanity check. ## Can be useful as a general data sanity checker (in previous modules) run_finemap <- function(z, LD_file, n, k, args = "", prefix="data") { cfg = write_finemap_sumstats(z, LD_file, n, k, prefix) cmd = paste("finemap --sss --log", "--in-files", cfg$meta, args) dscrutils::run_cmd(cmd) # read output tables snp = read.table(cfg$snp,header=TRUE,sep=" ") snp$snp = as.character(snp$snp) snp = rank_snp(snp) config = read.table(cfg$config,header=TRUE,sep=" ") # Only keep configurations with cumulative 95% probability # config = within(config, config_prob_cumsum <- cumsum(config_prob)) # config = config[config$config_prob_cumsum <= 0.95,] # extract number of causal ncausal = finemap_extract_ncausal(cfg$log) return(list(snp=snp, set=config, ncausal=ncausal)) } rank_snp <- function(snp) { snp <- arrange(snp, -snp_prob) %>% mutate( rank = seq(1, n()), snp_prob_cumsum = cumsum(snp_prob) / sum(snp_prob)) %>% select(rank, snp, snp_prob, snp_prob_cumsum, snp_log10bf) return(snp) } finemap_extract_ncausal <- function(logfile) { lines <- grep("->", readLines(logfile), value = TRUE) lines <- gsub("\\(|\\)|>", "", lines) splits <- strsplit(lines, "\\s+") tab <- data.frame( ncausal_num = sapply(splits, function(x) as.integer(x[2])), ncausal_prob = sapply(splits, function(x) as.double(x[4]))) tab <- mutate(tab, type = ifelse(duplicated(ncausal_num), "post", "prior")) return(tab) } finemap_mvar <- function(zscore, LD_file, n, k, args, prefix, parallel = FALSE) { if (is.null(dim(zscore))) { zscore = matrix(ncol=1,zscore) } single_core = function(r) run_finemap(zscore[,r], LD_file, n, k, args, paste0(prefix, '_condition_', r)) if (parallel) return(parallel::mclapply(1:ncol(zscore), function(r) single_core(r), mc.cores = min(8, ncol(zscore)))) else return(lapply(1:ncol(zscore), function(r) single_core(r))) } eval(parse(text=commandArgs(T))) dat = readRDS(input) sumstats = dat$sumstats N = nrow(dat$data$X) ld = tempfile(fileext = ".ld") write.table(cor(dat$data$X),ld,quote=F,col.names=F,row.names=F) posterior = finemap_mvar(sumstats[1,,] / sumstats[2,,], ld, N, k=NULL, args, prefix=tempfile(fileext = ".finemap")) saveRDS(posterior, paste0(output, '.rds')) susieR/inst/code/susie_rss_memory.R0000644000176200001440000000163314141046730017150 0ustar liggesusers# export MEM_CHECK_INTERVAL=0.01 # python3 monitor_memory.py Rscript susie_rss_memory.R # # NOTES: # # - Without any improvements: # Size of X: 0.5 GB # max rss_memory: 4.15 GB # # - With some improvements, the initial steps right before the main # loop use 0.86 GB. # # - susie_rss right before the CS and PIP calculations uses 1.6 GB. # # library(susieR) devtools::load_all() # set.seed(1) # p <- 8000 # n <- 1000 # X <- matrix(rnorm(n*p),n,p) # y <- rnorm(n) # ss <- susieR:::univariate_regression(X,y) # z <- ss$betahat/ss$sebetahat # R <- cor(X) # save(list = c("X","y","z","R"),file = "susie_rss_data.RData") load("susie_rss_data.RData") cat("Size of R:\n") print(object.size(R),unit = "GB") cat("Running susie_rss.\n") set.seed(1) out <- susie_rss(z,R,estimate_prior_variance = FALSE,min_abs_corr = 0, check_input = FALSE,refine = FALSE,verbose = TRUE) print(sapply(out$sets$cs,length)) susieR/inst/code/susie_memory.R0000644000176200001440000000131614141046730016257 0ustar liggesusers# export MEM_CHECK_INTERVAL=0.01 # python3 monitor_memory.py Rscript susie_memory.R # # NOTES: # # - Without any improvements: # Size of X: 1 GB # max rss_memory: 4.70 GB # # - With the improvements: # Size of X: 1 GB # max rss_memory: 3.00 GB # # set.seed(1) # p <- 16000 # n <- 8000 # X <- matrix(rnorm(n*p),n,p) # X <- scale(X,center = TRUE,scale = TRUE) # y <- rnorm(n) # save(list = c("X","y"),file = "susie_data.RData") # library(susieR) devtools::load_all() load("susie_data.RData") cat("Size of X:\n") print(object.size(X),unit = "GB") cat("Running susie.\n") set.seed(1) out <- susie(X,y,estimate_prior_variance = FALSE,min_abs_corr = 0, verbose = TRUE) print(sapply(out$sets$cs,length)) susieR/inst/code/monitor_memory.py0000744000176200001440000001201614141046730017045 0ustar liggesusers#!/usr/bin/env python3 # # Copyright (c) 2012 Realz Slaw, 2017 Gao Wang # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import time import psutil import subprocess class ProcessTimer: def __init__(self, command, interval = 1): self.command = command self.execution_state = False self.interval = interval def execute(self): self.max_vms_memory = 0 self.max_rss_memory = 0 self.t0 = time.time() self.t1 = None self.max_t = [self.t0] try: self.p = subprocess.Popen(self.command, shell=False) except FileNotFoundError: self.p = None sys.exit("Invalid command `{}`".format(sys.argv[1])) self.execution_state = True def poll(self): if not self.check_execution_state(): return False self.t1 = time.time() try: pp = psutil.Process(self.p.pid) # Obtain a list of the subprocess and all its descendants. descendants = list(pp.children(recursive=True)) descendants = descendants + [pp] rss_memory = 0 vms_memory = 0 # Calculate and sum up the memory of the subprocess and all its # descendants. for descendant in descendants: try: mem_info = descendant.memory_info() rss_memory += mem_info[0] vms_memory += mem_info[1] except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): # Sometimes a subprocess descendant will have terminated # between the time we obtain a list of descendants, and the # time we actually poll this descendant's memory usage. pass if int(self.max_vms_memory * 1E-8) < int(vms_memory * 1E-8): # Peak memory updated, at ~100-MB resolution. self.max_t = [self.t1] if int(self.max_vms_memory * 1E-8) == int(vms_memory * 1E-8): # Peak memory maintained. self.max_t = [self.max_t[0], self.t1] self.max_vms_memory = max(self.max_vms_memory,vms_memory) self.max_rss_memory = max(self.max_rss_memory,rss_memory) except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): return self.check_execution_state() return self.check_execution_state() def is_running(self): return psutil.pid_exists(self.p.pid) and self.p.poll() == None def check_execution_state(self): if not self.execution_state: return False if self.is_running(): return True self.executation_state = False self.t1 = time.time() return False def close(self,kill=False): if self.p is not None: try: pp = psutil.Process(self.p.pid) if kill: pp.kill() else: pp.terminate() except (psutil.NoSuchProcess, psutil.ZombieProcess, psutil.AccessDenied): pass def takewhile_excluding(iterable, value = ['|', '<', '>']): for it in iterable: if it in value: return yield it if __name__ == '__main__': import sys, os if len(sys.argv) <= 1: sys.exit() interval = float(os.environ['MEM_CHECK_INTERVAL']) if 'MEM_CHECK_INTERVAL' in os.environ else 1 ptimer = ProcessTimer(takewhile_excluding(sys.argv[1:]), interval) try: ptimer.execute() # Poll as often as possible; otherwise the subprocess might # "sneak" in some extra memory usage while you aren't looking. while ptimer.poll(): time.sleep(ptimer.interval) finally: # Make sure that we don't leave the process dangling. ptimer.close() sys.stderr.write('\ntime elapsed: {:.2f}s\n'.format(max(0, ptimer.t1 - ptimer.t0 - ptimer.interval * 0.5))) sys.stderr.write('peak first occurred: {:.2f}s\n'.format(min(ptimer.max_t) - ptimer.t0)) sys.stderr.write('peak last occurred: {:.2f}s\n'.format(max(ptimer.max_t) - ptimer.t0)) sys.stderr.write('max vms_memory: {:.2f}GB\n'.format(ptimer.max_vms_memory * 1.07E-9)) sys.stderr.write('max rss_memory: {:.2f}GB\n'.format(ptimer.max_rss_memory * 1.07E-9)) sys.stderr.write('memory check interval: %ss\n' % ptimer.interval) sys.stderr.write('return code: %s\n' % ptimer.p.returncode) susieR/inst/code/caviar.R0000644000176200001440000000412313335050146015003 0ustar liggesusers#!/usr/bin/env Rscript library(dplyr) library(readr) library(magrittr) #' CAVIAR I/O write_caviar_sumstats <- function(z, prefix) { cfg = list(z=paste0(prefix,".z"), set=paste0(prefix,"_set"), post=paste0(prefix,"_post"), log=paste0(prefix,".log")) write.table(z,cfg$z,quote=F,col.names=F) return(cfg) } #' Run CAVIAR #' https://github.com/fhormoz/caviar run_caviar <- function(z, LD_file, args = "", prefix="data") { cfg = write_caviar_sumstats(z, prefix) cmd = paste("CAVIAR", "-z", cfg$z, "-l", LD_file, "-o", prefix, args) dscrutils::run_cmd(cmd) if(!all(file.exists(cfg$post, cfg$set, cfg$log))) { stop("Cannot find one of the post, set, and log files") } log <- readLines(cfg$log) # read output tables snp <- read.delim(cfg$post) stopifnot(ncol(snp) == 3) names(snp) <- c("snp", "snp_prob_set", "snp_prob") snp$snp <- as.character(snp$snp) snp <- rank_snp(snp) # `set` of snps set <- readLines(cfg$set) set_ordered <- left_join(data_frame(snp = set), snp, by = "snp") %>% arrange(rank) %$% snp return(list(snp=snp, set=set_ordered)) } rank_snp <- function(snp) { snp <- arrange(snp, -snp_prob) %>% mutate( rank = seq(1, n()), snp_prob_cumsum = cumsum(snp_prob) / sum(snp_prob)) %>% select(rank, snp, snp_prob, snp_prob_cumsum, snp_prob_set) return(snp) } finemap_mcaviar <- function(zscore, LD_file, args, prefix) { if (is.null(dim(zscore))) { zscore = matrix(ncol=1,zscore) } return(parallel::mclapply(1:ncol(zscore), function(r) run_caviar(zscore[,r], LD_file, args, paste0(prefix, '_condition_', r)), mc.cores = min(8, ncol(zscore)))) } eval(parse(text=commandArgs(T))) dat = readRDS(input) sumstats = dat$sumstats ld = tempfile(fileext = ".ld") write.table(cor(dat$data$X),ld,quote=F,col.names=F,row.names=F) posterior = finemap_mcaviar(sumstats[1,,] / sumstats[2,,], ld, args, prefix=tempfile(fileext = ".caviar")) saveRDS(posterior, paste0(output, '.rds')) susieR/inst/code/compute_ss_memory.R0000644000176200001440000000124614141046730017312 0ustar liggesusers# export MEM_CHECK_INTERVAL=0.01 # python3 monitor_memory.py Rscript compute_ss_memory.R # # NOTES: # # - Without any improvements: # Size of X: 0.3 GB # max rss_memory: 1.65 GB # # - The original centering and scaling steps require about 1 GB. # # - With the improvements: # Size of X: 0.3 GB # max rss_memory: 0.66 GB # # set.seed(1) # p <- 2000 # n <- 20000 # X <- matrix(rnorm(n*p),n,p) # y <- rnorm(n) # save(list = c("X","y"),file = "compute_ss_data.RData") # library(susieR) devtools::load_all() set.seed(1) load("compute_ss_data.RData") cat("Size of X:\n") print(object.size(X),unit = "GB") cat("Running compute_ss.\n") out <- compute_ss(X,y,standardize = TRUE) susieR/inst/code/dap-g.py0000644000176200001440000000615113365653500014766 0ustar liggesusers#!/usr/bin/env python3 import sys import subprocess import pandas as pd import numpy as np def write_dap_full(x,y,prefix,r): names = np.array([('geno', i+1, f'group{r}') for i in range(x.shape[1])]) with open(f'{prefix}.data', 'w') as f: print(*(['pheno', 'pheno', f'group{r}'] + list(np.array(y).ravel())), file=f) np.savetxt(f, np.hstack((names, x.T)), fmt = '%s', delimiter = ' ') def run_dap_full(prefix, args): cmd = ['dap-g', '-d', f'{prefix}.data', '-o', f'{prefix}.result', '--output_all'] + ' '.join(args).split() subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate() def write_dap_ss(z,prefix): '''z-score vesion of dap input is the same as FINEMAP''' ids = np.array([str(i+1) for i in range(z.shape[0])]) with open(f'{prefix}.z', 'w') as f: np.savetxt(f, np.vstack((ids, z)).T, fmt = '%s', delimiter = ' ') def run_dap_z(ld, prefix, args): cmd = ['dap-g', '-d_z', f'{prefix}.z', '-d_ld', ld, '-o', f'{prefix}.result', '--all'] + ' '.join(args).split() subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate() def extract_dap_output(prefix): out = [x.strip().split() for x in open(f'{prefix}.result').readlines()] pips = [] clusters = [] still_pip = True for line in out: if len(line) == 0: continue if len(line) > 2 and line[2] == 'cluster_pip': still_pip = False continue if still_pip and (not line[0].startswith('((')): continue if still_pip: pips.append([line[1], float(line[2]), float(line[3]), int(line[4])]) else: clusters.append([len(clusters) + 1, float(line[2]), float(line[3])]) pips = pd.DataFrame(pips, columns = ['snp', 'snp_prob', 'snp_log10bf', 'cluster']) clusters = pd.DataFrame(clusters, columns = ['cluster', 'cluster_prob', 'cluster_avg_r2']) clusters = pd.merge(clusters, pips.groupby(['cluster'])['snp'].apply(','.join).reset_index(), on = 'cluster') return {'snp': pips, 'set': clusters} def dap_single(x, y, prefix, r, args): write_dap_full(x,y,prefix,r) run_dap_full(prefix,args) return extract_dap_output(prefix) def dap_single_z(z, ld, prefix, args): write_dap_ss(z,prefix) run_dap_z(ld,prefix,args) return extract_dap_output(prefix) def dap_batch(X, Y, prefix, *args): return dict([(f'V{r+1}', dap_single(X, Y[:,r], f'{prefix}_condition_{r+1}', r+1, args)) for r in range(Y.shape[1])]) def dap_batch_z(z, ld, prefix, *args): return dict([(f'V{r+1}', dap_single_z(z[:,r], ld, f'{prefix}_condition_{r+1}', args)) for r in range(z.shape[1])]) import os from dsc.dsc_io import load_rds, save_rds import tempfile import warnings if not sys.warnoptions: warnings.simplefilter("ignore") input_file = os.path.expanduser(sys.argv[1]) output_file = os.path.expanduser(sys.argv[2]) args = sys.argv[3:] data = load_rds(input_file)['data'] cache = tempfile.NamedTemporaryFile(suffix = '.dap') posterior = dap_batch(data['X'], data['Y'], cache.name, ' '.join(args)) save_rds(posterior, output_file + '.rds') susieR/inst/code/finemap_1p4.R0000644000176200001440000000764514112242336015653 0ustar liggesusers#!/usr/bin/env Rscript library(dplyr) library(readr) library(magrittr) #' FINEMAP I/O write_finemap_sumstats <- function(beta, se, LD_file, n, k, prefix) { cfg = list(z=paste0(prefix,".z"), ld=LD_file, snp=paste0(prefix,".snp"), config=paste0(prefix,".config"), cred=paste0(prefix, ".cred"), k=paste0(prefix,".k"), log=paste0(prefix,".log"), meta=paste0(prefix,".master")) se = replace(se, se == 0, 'nan') z = data.frame(chromosome="chr", position=seq(1, length(beta)), allele1='nan', allele2='nan', maf='nan', beta, se) z = cbind(rsid=z$position, z) write.table(z,cfg$z,quote=F,col.names=T,row.names=F) if (!is.null(k)) { write.table(t(k),cfg$k,quote=F,col.names=F,row.names=F) write("z;ld;snp;config;cred;n_samples;k;log",file=cfg$meta) write(paste(cfg$z, cfg$ld, cfg$snp, cfg$config, cfg$cred, n, cfg$k, cfg$log, sep=";"), file=cfg$meta,append=TRUE) } else { write("z;ld;snp;config;cred;n_samples;log",file=cfg$meta) write(paste(cfg$z, cfg$ld, cfg$snp, cfg$config, cfg$cred, n, cfg$log, sep=";"), file=cfg$meta,append=TRUE) } return(cfg) } #' Run FINEMAP version 1.4 #' http://www.christianbenner.com ## FIXME: read the finemapr implementation for data sanity check. ## Can be useful as a general data sanity checker (in previous modules) run_finemap <- function(beta, se, LD_file, n, k, args = "", prefix="data") { cfg = write_finemap_sumstats(beta, se, LD_file, n, k, prefix) cmd = paste("finemap --sss --log", "--in-files", cfg$meta, args) dscrutils::run_cmd(cmd) cfg$log = paste0(cfg$log, "_sss") # read output tables snp = read.table(cfg$snp,header=TRUE,sep=" ") snp$snp = as.character(snp$rsid) snp = rank_snp(snp) # we add snp-prob for backwards-compatability with code that used this script with FINEMAP v1.1 snp$prob = snp$snp_prob config = read.table(cfg$config,header=TRUE,sep=" ") # Only keep configurations with cumulative 95% probability # config = within(config, config_prob_cumsum <- cumsum(config_prob)) # config = config[config$config_prob_cumsum <= 0.95,] # extract number of causal ncausal = finemap_extract_ncausal(cfg$log) return(list(snp=snp, set=config, ncausal=ncausal)) } rank_snp <- function(snp) { snp <- arrange(snp, -prob) %>% mutate( rank = seq(1, n()), prob_cumsum = cumsum(prob) / sum(prob)) %>% select(rank, snp, prob, prob_cumsum, log10bf) return(snp) } finemap_extract_ncausal <- function(logfile) { lines <- grep("->", readLines(logfile), value = TRUE) lines <- gsub("\\(|\\)|>", "", lines) splits <- strsplit(lines, "\\s+") tab <- data.frame( ncausal_num = sapply(splits, function(x) as.integer(x[2])), ncausal_prob = sapply(splits, function(x) as.double(x[4]))) tab <- mutate(tab, type = ifelse(duplicated(ncausal_num), "post", "prior")) return(tab) } finemap_mvar <- function(beta, se, LD_file, n, k, args, prefix, parallel = FALSE) { if (is.null(dim(beta))) { beta = matrix(ncol=1,beta) } if (is.null(dim(se))) { se = matrix(ncol=1,se) } single_core = function(r) run_finemap(beta[,r], se[,r], LD_file, n, k, args, prefix=paste0(prefix, '_condition_', r)) if (parallel) return(parallel::mclapply(1:ncol(beta), function(r) single_core(r), mc.cores = min(8, ncol(beta)))) else return(lapply(1:ncol(beta), function(r) single_core(r))) } eval(parse(text=commandArgs(T))) dat = readRDS(input) sumstats = dat$sumstats N = nrow(dat$data$X) ld = tempfile(fileext = ".ld") ld_mat = cor(dat$data$X) ld_mat[is.na(ld_mat)] = 'nan' write.table(ld_mat,ld,quote=F,col.names=F,row.names=F) posterior = finemap_mvar(sumstats[1,,], sumstats[2,,], ld, N, k=NULL, args, prefix=tempfile(fileext = ".finemap")) saveRDS(posterior, paste0(output, '.rds'))