sva/DESCRIPTION0000644000175200017520000000457714710321436014111 0ustar00biocbuildbiocbuildPackage: sva Title: Surrogate Variable Analysis Version: 3.54.0 Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization RoxygenNote: 7.0.2 git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_20 git_last_commit: 2e460b8 git_last_commit_date: 2024-10-29 Repository: Bioconductor 3.20 Date/Publication: 2024-10-29 NeedsCompilation: yes Packaged: 2024-10-30 03:08:14 UTC; biocbuild sva/MD50000644000175200017520000000702414710321436012701 0ustar00biocbuildbiocbuild46e82b2d91117386fc64b2d7171927c0 *DESCRIPTION c07885762a7717267140c88cc2f55d6f *NAMESPACE e8b11d5011af52e5ebf319c3acb0bec9 *R/ComBat.R 90a2713746f4ab84d5c355912e43cd10 *R/ComBat_seq.R 07b665bcc25b084836bcb92c0c575875 *R/empirical.controls.R 469ece46c1757e635dd2c04da557c3f9 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importFrom(edgeR,estimateGLMTagwiseDisp) importFrom(edgeR,getOffset) importFrom(edgeR,glmFit) importFrom(edgeR,glmFit.default) importFrom(graphics,lines) importFrom(graphics,par) importFrom(limma,lmFit) importFrom(stats,cor) importFrom(stats,density) importFrom(stats,dnbinom) importFrom(stats,dnorm) importFrom(stats,lm) importFrom(stats,model.matrix) importFrom(stats,pf) importFrom(stats,pnbinom) importFrom(stats,ppoints) importFrom(stats,prcomp) importFrom(stats,predict) importFrom(stats,qgamma) importFrom(stats,qnbinom) importFrom(stats,qnorm) importFrom(stats,qqline) importFrom(stats,qqnorm) importFrom(stats,qqplot) importFrom(stats,smooth.spline) importFrom(stats,var) importFrom(utils,capture.output) importFrom(utils,read.delim) useDynLib(sva, .registration = TRUE) sva/R/0000755000175200017520000000000014710217751012573 5ustar00biocbuildbiocbuildsva/R/ComBat.R0000644000175200017520000002764114710217751014075 0ustar00biocbuildbiocbuild#' Adjust for batch effects using an empirical Bayes framework #' #' ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology #' described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for #' batch effects. Users are returned an expression matrix that has been corrected for batch effects. The input #' data are assumed to be cleaned and normalized before batch effect removal. #' #' @param dat Genomic measure matrix (dimensions probe x sample) - for example, expression matrix #' @param batch {Batch covariate (only one batch allowed)} #' @param mod Model matrix for outcome of interest and other covariates besides batch #' @param par.prior (Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used #' @param prior.plots (Optional) TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric #' @param mean.only (Optional) FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment) #' @param ref.batch (Optional) NULL If given, will use the selected batch as a reference for batch adjustment. #' @param BPPARAM (Optional) BiocParallelParam for parallel operation #' #' @return data A probe x sample genomic measure matrix, adjusted for batch effects. #' #' @importFrom graphics lines par #' @importFrom stats cor density dnorm model.matrix pf ppoints prcomp predict #' qgamma qnorm qqline qqnorm qqplot smooth.spline var #' @importFrom utils read.delim #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:50,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' batch = pheno$batch #' mod = model.matrix(~as.factor(cancer), data=pheno) #' #' # parametric adjustment #' combat_edata1 = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE) #' #' # non-parametric adjustment, mean-only version #' combat_edata2 = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=FALSE, mean.only=TRUE) #' #' # reference-batch version, with covariates #' combat_edata3 = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, ref.batch=3) #' #' @export #' ComBat <- function(dat, batch, mod = NULL, par.prior = TRUE, prior.plots = FALSE, mean.only = FALSE, ref.batch = NULL, BPPARAM = bpparam("SerialParam")) { if(length(dim(batch))>1){ stop("This version of ComBat only allows one batch variable") } ## to be updated soon! ## coerce dat into a matrix dat <- as.matrix(dat) ## find genes with zero variance in any of the batches batch <- as.factor(batch) zero.rows.lst <- lapply(levels(batch), function(batch_level){ if(sum(batch==batch_level)>1){ return(which(apply(dat[, batch==batch_level], 1, function(x){var(x)==0}))) }else{ return(which(rep(1,3)==2)) } }) zero.rows <- Reduce(union, zero.rows.lst) keep.rows <- setdiff(1:nrow(dat), zero.rows) if (length(zero.rows) > 0) { cat(sprintf("Found %d genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.\n", length(zero.rows))) # keep a copy of the original data matrix and remove zero var rows dat.orig <- dat dat <- dat[keep.rows, ] } ## make batch a factor and make a set of indicators for batch if(any(table(batch)==1)){mean.only=TRUE} if(mean.only==TRUE){ message("Using the 'mean only' version of ComBat") } batchmod <- model.matrix(~-1+batch) if (!is.null(ref.batch)){ ## check for reference batch, check value, and make appropriate changes if (!(ref.batch%in%levels(batch))) { stop("reference level ref.batch is not one of the levels of the batch variable") } message("Using batch =",ref.batch, "as a reference batch (this batch won't change)") ref <- which(levels(as.factor(batch))==ref.batch) # find the reference batchmod[,ref] <- 1 } else { ref <- NULL } message("Found", nlevels(batch), "batches") ## A few other characteristics on the batches n.batch <- nlevels(batch) batches <- list() for (i in 1:n.batch) { batches[[i]] <- which(batch == levels(batch)[i]) } # list of samples in each batch n.batches <- sapply(batches, length) if(any(n.batches==1)){ mean.only=TRUE message("Note: one batch has only one sample, setting mean.only=TRUE") } n.array <- sum(n.batches) ## combine batch variable and covariates design <- cbind(batchmod,mod) ## check for intercept in covariates, and drop if present check <- apply(design, 2, function(x) all(x == 1)) if(!is.null(ref)){ check[ref] <- FALSE } ## except don't throw away the reference batch indicator design <- as.matrix(design[,!check]) ## Number of covariates or covariate levels message("Adjusting for", ncol(design)-ncol(batchmod), 'covariate(s) or covariate level(s)') ## Check if the design is confounded if(qr(design)$rank < ncol(design)) { ## if(ncol(design)<=(n.batch)){stop("Batch variables are redundant! Remove one or more of the batch variables so they are no longer confounded")} if(ncol(design)==(n.batch+1)) { stop("The covariate is confounded with batch! Remove the covariate and rerun ComBat") } if(ncol(design)>(n.batch+1)) { if((qr(design[,-c(1:n.batch)])$rank 0) { dat.orig[keep.rows, ] <- bayesdata bayesdata <- dat.orig } return(bayesdata) } sva/R/ComBat_seq.R0000644000175200017520000002307614710217751014743 0ustar00biocbuildbiocbuild#' Adjust for batch effects using an empirical Bayes framework in RNA-seq raw counts #' #' ComBat_seq is an improved model from ComBat using negative binomial regression, #' which specifically targets RNA-Seq count data. #' #' @param counts Raw count matrix from genomic studies (dimensions gene x sample) #' @param batch Vector / factor for batch #' @param group Vector / factor for biological condition of interest #' @param covar_mod Model matrix for multiple covariates to include in linear model (signals from these variables are kept in data after adjustment) #' @param full_mod Boolean, if TRUE include condition of interest in model #' @param shrink Boolean, whether to apply shrinkage on parameter estimation #' @param shrink.disp Boolean, whether to apply shrinkage on dispersion #' @param gene.subset.n Number of genes to use in empirical Bayes estimation, only useful when shrink = TRUE #' #' @return data A gene x sample count matrix, adjusted for batch effects. #' #' @importFrom edgeR DGEList estimateGLMCommonDisp estimateGLMTagwiseDisp glmFit glmFit.default getOffset #' @importFrom stats dnbinom lm pnbinom qnbinom #' @importFrom utils capture.output #' #' @examples #' #' count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) #' batch <- c(rep(1, 4), rep(2, 4)) #' group <- rep(c(0,1), 4) #' #' # include condition (group variable) #' adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group, full_mod=TRUE) #' #' # do not include condition #' adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, full_mod=FALSE) #' #' @export #' ComBat_seq <- function(counts, batch, group=NULL, covar_mod=NULL, full_mod=TRUE, shrink=FALSE, shrink.disp=FALSE, gene.subset.n=NULL){ ######## Preparation ######## ## Does not support 1 sample per batch yet batch <- as.factor(batch) if(any(table(batch)<=1)){ stop("ComBat-seq doesn't support 1 sample per batch yet") } ## Remove genes with only 0 counts in any batch keep_lst <- lapply(levels(batch), function(b){ which(apply(counts[, batch==b], 1, function(x){!all(x==0)})) }) keep <- Reduce(intersect, keep_lst) rm <- setdiff(1:nrow(counts), keep) countsOri <- counts counts <- counts[keep, ] # require bioconductor 3.7, edgeR 3.22.1 dge_obj <- DGEList(counts=counts) ## Prepare characteristics on batches n_batch <- nlevels(batch) # number of batches batches_ind <- lapply(1:n_batch, function(i){which(batch==levels(batch)[i])}) # list of samples in each batch n_batches <- sapply(batches_ind, length) #if(any(n_batches==1)){mean_only=TRUE; cat("Note: one batch has only one sample, setting mean.only=TRUE\n")} n_sample <- sum(n_batches) cat("Found",n_batch,'batches\n') ## Make design matrix # batch batchmod <- model.matrix(~-1+batch) # colnames: levels(batch) # covariate group <- as.factor(group) if(full_mod & nlevels(group)>1){ cat("Using full model in ComBat-seq.\n") mod <- model.matrix(~group) }else{ cat("Using null model in ComBat-seq.\n") mod <- model.matrix(~1, data=as.data.frame(t(counts))) } # drop intercept in covariate model if(!is.null(covar_mod)){ if(is.data.frame(covar_mod)){ covar_mod <- do.call(cbind, lapply(1:ncol(covar_mod), function(i){model.matrix(~covar_mod[,i])})) } covar_mod <- covar_mod[, !apply(covar_mod, 2, function(x){all(x==1)})] } # bind with biological condition of interest mod <- cbind(mod, covar_mod) # combine design <- cbind(batchmod, mod) ## Check for intercept in covariates, and drop if present check <- apply(design, 2, function(x) all(x == 1)) #if(!is.null(ref)){check[ref]=FALSE} ## except don't throw away the reference batch indicator design <- as.matrix(design[,!check]) cat("Adjusting for",ncol(design)-ncol(batchmod),'covariate(s) or covariate level(s)\n') ## Check if the design is confounded if(qr(design)$rank(n_batch+1)){ if((qr(design[,-c(1:n_batch)])$rank1){ sgn[j] = sign(cor(svd.wx$v[1:ndb,j],sv$sv[1:ndb,j])) } else if(sv$n.sv==1){ sgn[j] = sign(cor(svd.wx$v[1:ndb,j],sv$sv[1:ndb])) } } newV = newV*sgn newV = t(newV) newV = scale(newV)/sqrt(dim(newV)[1]) newV = t(newV) }else if(method=="exact"){ newV = matrix(nrow=nnew,ncol=n.sv) for(i in 1:nnew){ tmp = cbind(dbdat,newdat[,i]) tmpd = (1-sv$pprob.b)*sv$pprob.gam*tmp ss = svd(t(scale(t(tmpd),scale=FALSE))) sgn = rep(NA,sv$n.sv) for(j in 1:sv$n.sv){ # This code should continue working with drop=FALSE since # matrices are also vectors. Remove the else once there's no # more concern about backward-compatibility with sva objects # from previous versions. if(sv$n.sv>1){ sgn[j] = sign(cor(ss$v[1:ndb,j],sv$sv[1:ndb,j])) } else if(sv$n.sv==1){ sgn[j] = sign(cor(ss$v[1:ndb,j],sv$sv[1:ndb])) } } newV[i,]<-ss$v[(ndb+1),1:sv$n.sv]*sgn } newV = t(newV) } adjusted = newdat - gammahat %*% newV newV = t(newV) } return(list(db=db,new=adjusted,newsv = newV)) } sva/R/helper.R0000644000175200017520000001022714710217751014177 0ustar00biocbuildbiocbuildlibrary(BiocParallel) sva.class2Model <- function(classes) { return(model.matrix(~factor(classes))) } modefunc <- function(x) { return(as.numeric(names(sort(-table(x)))[1])) } mono <- function(lfdr){ .Call("monotone", as.numeric(lfdr), PACKAGE="sva") } edge.lfdr <- function(p, trunc=TRUE, monotone=TRUE, transf=c("probit", "logit"), adj=1.5, eps=10^-8, lambda=0.8, ...) { pi0 <- mean(p >= lambda)/(1 - lambda) pi0 <- min(pi0, 1) n <- length(p) transf <- match.arg(transf) if(transf=="probit") { p <- pmax(p, eps) p <- pmin(p, 1-eps) x <- qnorm(p) myd <- density(x, adjust=adj) mys <- smooth.spline(x=myd$x, y=myd$y) y <- predict(mys, x)$y lfdr <- pi0*dnorm(x)/y } if(transf=="logit") { x <- log((p+eps)/(1-p+eps)) myd <- density(x, adjust=adj) mys <- smooth.spline(x=myd$x, y=myd$y) y <- predict(mys, x)$y dx <- exp(x) / (1+exp(x))^2 lfdr <- pi0 * dx/y } if(trunc) { lfdr[lfdr > 1] <- 1 } if(monotone) { lfdr <- lfdr[order(p)] lfdr <- mono(lfdr) lfdr <- lfdr[rank(p)] } return(lfdr) } # Trims the data of extra columns, note your array names cannot be named 'X' or start with 'X.' trim.dat <- function(dat){ tmp <- strsplit(colnames(dat),'\\.') tr <- NULL for (i in 1:length(tmp)) { tr <- c(tr, tmp[[i]][1] != 'X') } tr } # Following four find empirical hyper-prior values aprior <- function(gamma.hat) { m <- mean(gamma.hat) s2 <- var(gamma.hat) (2*s2 + m^2) / s2 } bprior <- function(gamma.hat){ m <- mean(gamma.hat) s2 <- var(gamma.hat) (m*s2 + m^3) / s2 } postmean <- function(g.hat,g.bar,n,d.star,t2){ (t2*n*g.hat + d.star*g.bar) / (t2*n + d.star) } postvar <- function(sum2,n,a,b){ (.5*sum2 + b) / (n/2 + a - 1) } # Inverse gamma distribution density function. (Note: does not do any bounds checking on arguments) dinvgamma <- function (x, shape, rate = 1/scale, scale = 1) { # PDF taken from https://en.wikipedia.org/wiki/Inverse-gamma_distribution # Note: alpha = shape, beta = rate stopifnot(shape > 0) stopifnot(rate > 0) ifelse(x <= 0, 0, ((rate ^ shape) / gamma(shape)) * x ^ (-shape - 1) * exp(-rate/x)) } # Pass in entire data set, the design matrix for the entire data, the batch means, the batch variances, priors (m, t2, a, b), columns of the data matrix for the batch. Uses the EM to find the parametric batch adjustments it.sol <- function(sdat,g.hat,d.hat,g.bar,t2,a,b,conv=.0001){ n <- rowSums(!is.na(sdat)) g.old <- g.hat d.old <- d.hat change <- 1 count <- 0 while(change>conv){ g.new <- postmean(g.hat, g.bar, n, d.old, t2) sum2 <- rowSums((sdat - g.new %*% t(rep(1,ncol(sdat))))^2, na.rm=TRUE) d.new <- postvar(sum2, n, a, b) change <- max(abs(g.new-g.old) / g.old, abs(d.new-d.old) / d.old) g.old <- g.new d.old <- d.new count <- count+1 } ## cat("This batch took", count, "iterations until convergence\n") adjust <- rbind(g.new, d.new) rownames(adjust) <- c("g.star","d.star") adjust } ## likelihood function used below L <- function(x,g.hat,d.hat){ prod(dnorm(x, g.hat, sqrt(d.hat))) } ## Monte Carlo integration functions int.eprior <- function(sdat, g.hat, d.hat){ g.star <- d.star <- NULL r <- nrow(sdat) for(i in 1:r){ g <- g.hat[-i] d <- d.hat[-i] x <- sdat[i,!is.na(sdat[i,])] n <- length(x) j <- numeric(n)+1 dat <- matrix(as.numeric(x), length(g), n, byrow=TRUE) resid2 <- (dat-g)^2 sum2 <- resid2 %*% j LH <- 1/(2*pi*d)^(n/2)*exp(-sum2/(2*d)) LH[LH=="NaN"]=0 g.star <- c(g.star, sum(g*LH)/sum(LH)) d.star <- c(d.star, sum(d*LH)/sum(LH)) ## if(i%%1000==0){cat(i,'\n')} } adjust <- rbind(g.star,d.star) rownames(adjust) <- c("g.star","d.star") adjust } ## fits the L/S model in the presence of missing data values Beta.NA <- function(y,X){ des <- X[!is.na(y),] y1 <- y[!is.na(y)] B <- solve(crossprod(des), crossprod(des, y1)) B } sva/R/helper_seq.R0000644000175200017520000000550014710217751015045 0ustar00biocbuildbiocbuild#### Expand a vector into matrix (columns as the original vector) vec2mat <- function(vec, n_times){ return(matrix(rep(vec, n_times), ncol=n_times, byrow=FALSE)) } #### Monte Carlo integration functions monte_carlo_int_NB <- function(dat, mu, gamma, phi, gene.subset.n){ weights <- pos_res <- list() for(i in 1:nrow(dat)){ m <- mu[-i,!is.na(dat[i,])] x <- dat[i,!is.na(dat[i,])] gamma_sub <- gamma[-i] phi_sub <- phi[-i] # take a subset of genes to do integration - save time if(!is.null(gene.subset.n) & is.numeric(gene.subset.n) & length(gene.subset.n)==1){ if(i==1){cat(sprintf("Using %s random genes for Monte Carlo integration\n", gene.subset.n))} mcint_ind <- sample(1:(nrow(dat)-1), gene.subset.n, replace=FALSE) m <- m[mcint_ind, ]; gamma_sub <- gamma_sub[mcint_ind]; phi_sub <- phi_sub[mcint_ind] G_sub <- gene.subset.n }else{ if(i==1){cat("Using all genes for Monte Carlo integration; the function runs very slow for large number of genes\n")} G_sub <- nrow(dat)-1 } #LH <- sapply(1:G_sub, function(j){sum(log2(dnbinom(x, mu=m[j,], size=1/phi_sub[j])+1))}) LH <- sapply(1:G_sub, function(j){prod(dnbinom(x, mu=m[j,], size=1/phi_sub[j]))}) LH[is.nan(LH)]=0; if(sum(LH)==0 | is.na(sum(LH))){ pos_res[[i]] <- c(gamma.star=as.numeric(gamma[i]), phi.star=as.numeric(phi[i])) }else{ pos_res[[i]] <- c(gamma.star=sum(gamma_sub*LH)/sum(LH), phi.star=sum(phi_sub*LH)/sum(LH)) } weights[[i]] <- as.matrix(LH/sum(LH)) } pos_res <- do.call(rbind, pos_res) weights <- do.call(cbind, weights) res <- list(gamma_star=pos_res[, "gamma.star"], phi_star=pos_res[, "phi.star"], weights=weights) return(res) } #### Match quantiles match_quantiles <- function(counts_sub, old_mu, old_phi, new_mu, new_phi){ new_counts_sub <- matrix(NA, nrow=nrow(counts_sub), ncol=ncol(counts_sub)) for(a in 1:nrow(counts_sub)){ for(b in 1:ncol(counts_sub)){ if(counts_sub[a, b] <= 1){ new_counts_sub[a,b] <- counts_sub[a, b] }else{ tmp_p <- pnbinom(counts_sub[a, b]-1, mu=old_mu[a, b], size=1/old_phi[a]) if(abs(tmp_p-1)<1e-4){ new_counts_sub[a,b] <- counts_sub[a, b] # for outlier count, if p==1, will return Inf values -> use original count instead }else{ new_counts_sub[a,b] <- 1+qnbinom(tmp_p, mu=new_mu[a, b], size=1/new_phi[a]) } } } } return(new_counts_sub) } mapDisp <- function(old_mu, new_mu, old_phi, divider){ new_phi <- matrix(NA, nrow=nrow(old_mu), ncol=ncol(old_mu)) for(a in 1:nrow(old_mu)){ for(b in 1:ncol(old_mu)){ old_var <- old_mu[a, b] + old_mu[a, b]^2 * old_phi[a, b] new_var <- old_var / (divider[a, b]^2) new_phi[a, b] <- (new_var - new_mu[a, b]) / (new_mu[a, b]^2) } } return(new_phi) } sva/R/irwsva.build.R0000644000175200017520000000473314710217751015336 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables by estimating empirical control probes #' #' This function is the implementation of the iteratively re-weighted least squares #' approach for estimating surrogate variables. As a buy product, this function #' produces estimates of the probability of being an empirical control. See the function #' \code{\link{empirical.controls}} for a direct estimate of the empirical controls. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param mod The model matrix being used to fit the data #' @param mod0 The null model being compared when fitting the data #' @param n.sv The number of surogate variables to estimate #' @param B The number of iterations of the irwsva algorithm to perform #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:5000,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' res <- irwsva.build(edata, mod, mod0 = NULL,n.sv,B=5) #' #' @export #' irwsva.build <- function(dat, mod, mod0 = NULL,n.sv,B=5) { n <- ncol(dat) m <- nrow(dat) if(is.null(mod0)){mod0 <- mod[,1]} Id <- diag(n) resid <- dat %*% (Id - mod %*% solve(t(mod) %*% mod) %*% t(mod)) uu <- eigen(t(resid)%*%resid) vv <- uu$vectors ndf <- n - dim(mod)[2] pprob <- rep(1,m) one <- rep(1,n) Id <- diag(n) df1 <- dim(mod)[2] + n.sv df0 <- dim(mod0)[2] + n.sv rm(resid) cat(paste("Iteration (out of", B,"):")) for(i in 1:B){ mod.b <- cbind(mod,uu$vectors[,1:n.sv]) mod0.b <- cbind(mod0,uu$vectors[,1:n.sv]) ptmp <- f.pvalue(dat,mod.b,mod0.b) pprob.b <- (1-edge.lfdr(ptmp)) mod.gam <- cbind(mod0,uu$vectors[,1:n.sv]) mod0.gam <- cbind(mod0) ptmp <- f.pvalue(dat,mod.gam,mod0.gam) pprob.gam <- (1-edge.lfdr(ptmp)) pprob <- pprob.gam*(1-pprob.b) dats <- dat*pprob dats <- dats - rowMeans(dats) uu <- eigen(t(dats)%*%dats) cat(paste(i," ")) } sv = svd(dats)$v[,1:n.sv, drop=FALSE] retval <- list(sv=sv,pprob.gam = pprob.gam, pprob.b=pprob.b,n.sv=n.sv) return(retval) } sva/R/num.sv.R0000644000175200017520000000647014710217751014153 0ustar00biocbuildbiocbuild#' A function for calculating the number of surrogate variables to estimate in a model #' #' This function estimates the number of surrogate variables that should be included #' in a differential expression model. The default approach is based on a permutation #' procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides #' an interface to the asymptotic approach proposed by Leek 2011 Biometrics. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param mod The model matrix being used to fit the data #' @param method One of "be" or "leek" as described in the details section #' @param vfilter You may choose to filter to the vfilter most variable rows before performing the analysis #' @param B The number of permutaitons to use if method = "be" #' @param seed Set a seed when using the permutation approach #' #' @return n.sv The number of surrogate variables to use in the sva software #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:5000,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' #' @export #' num.sv <- function(dat, mod,method=c("be","leek"),vfilter=NULL,B=20,seed=NULL) { if(!is.null(vfilter)){ if(vfilter < 100 | vfilter > dim(dat)[1]){ stop(paste("The number of genes used in the analysis must be between 100 and",dim(dat)[1],"\n")) } tmpv = rowVars(dat) ind = which(rank(-tmpv) < vfilter) dat = dat[ind,] } method <- match.arg(method) if(method=="be"){ if(!is.null(seed)){set.seed(seed)} warn <- NULL n <- ncol(dat) m <- nrow(dat) H <- mod %*% solve(t(mod) %*% mod) %*% t(mod) res <- dat - t(H %*% t(dat)) uu <- svd(res) ndf <- min(m,n) - ceiling(sum(diag(H))) dstat <- uu$d[1:ndf]^2/sum(uu$d[1:ndf]^2) dstat0 <- matrix(0,nrow=B,ncol=ndf) for(i in 1:B){ res0 <- t(apply(res, 1, sample, replace=FALSE)) res0 <- res0 - t(H %*% t(res0)) uu0 <- svd(res0) dstat0[i,] <- uu0$d[1:ndf]^2/sum(uu0$d[1:ndf]^2) } psv <- rep(1,n) for(i in 1:ndf){ psv[i] <- mean(dstat0[,i] >= dstat[i]) } for(i in 2:ndf){ psv[i] <- max(psv[(i-1)],psv[i]) } nsv <- sum(psv <= 0.10) return(as.numeric(list(n.sv = nsv))) }else{ dat <- as.matrix(dat) dims <- dim(dat) a <- seq(0,2,length=100) n <- floor(dims[1]/10) rhat <- matrix(0,nrow=100,ncol=10) P <- (diag(dims[2])-mod %*% solve(t(mod) %*% mod) %*% t(mod)) for(j in 1:10){ dats <- dat[1:(j*n),] ee <- eigen(t(dats) %*% dats) sigbar <- ee$values[dims[2]]/(j*n) R <- dats %*% P wm <- (1/(j*n))*t(R) %*% R - P*sigbar ee <- eigen(wm) v <- c(rep(TRUE, 100), rep(FALSE, dims[2])) v <- v[order(c(a*(j*n)^(-1/3)*dims[2],ee$values), decreasing = TRUE)] u <- 1:length(v) w <- 1:100 rhat[,j] <- rev((u[v==TRUE]-w)) } ss <- rowVars(rhat) bumpstart <- which.max(ss > (2*ss[1])) start <- which.max(c(rep(1e5,bumpstart),ss[(bumpstart+1):100]) < 0.5*ss[1]) finish <- which.max(ss*c(rep(0,start),rep(1,100-start)) > ss[1]) if(finish==1){finish <- 100} n.sv <- modefunc(rhat[start:finish,10]) return(n.sv) print(method) } } sva/R/psva.R0000644000175200017520000000355514710217751013677 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables with the two step approach of Leek and Storey 2007 #' #' This function is the implementation of the two step approach for estimating surrogate #' variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily #' included for backwards compatibility. Newer versions of the sva algorithm are available #' through \code{\link{sva}}, \code{\link{svaseq}}, with low level functionality available #' through \code{\link{irwsva.build}} and \code{\link{ssva}}. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param batch A factor variable giving the known batch levels #' @param ... Other arguments to the \code{\link{sva}} function. #' #' @return psva.D Data with batch effect removed but biological heterogeneity preserved #' #' @importFrom limma lmFit #' #' @examples #' #' #' #' library(bladderbatch) #' library(limma) #' data(bladderdata) #' dat <- bladderEset[1:50,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' batch = pheno$batch #' batch.fac = as.factor(batch) #' #' psva_data <- psva(edata,batch.fac) #' #' @author Elana J. Fertig #' #' @export #' psva <- function(dat, batch, ...) { # convert input class / categorical variables to a standard model matrix if (class(batch)=='factor' | class(batch)=='character') { mod <- sva.class2Model(batch) } else { stop('Invalid batch type for psva (require factor or character):', class(batch)) } # find SV's psva.SV <- sva(dat=dat, mod=mod, ...) colnames(psva.SV$sv) <- paste('sv',1:ncol(psva.SV$sv)) # fit data psva.fit <- lmFit(dat, cbind(mod,psva.SV$sv)) # batch corrected data psva.D <- sweep(psva.fit$coefficients[,paste('sv',1:ncol(psva.SV$sv))]%*% t(psva.SV$sv), 1, psva.fit$coefficients[,"(Intercept)"], FUN="+") return(psva.D) }sva/R/qsva.R0000644000175200017520000000211214710217751013664 0ustar00biocbuildbiocbuild#' A function for computing quality surrogate variables (qSVs) #' #' This function computes quality surrogate variables (qSVs) #' from the library-size- and read-length-normalized #' degradation matrix for subsequent RNA quality correction #' #' @param degradationMatrix the normalized degradation matrix, region by sample #' @param mod (Optional) statistical model used in DE analysis #' #' @return the qSV adjustment variables #' #' @examples #' #' ## Find files #' bwPath <- system.file('extdata', 'bwtool', package = 'sva') #' #' ## Read the data #' degCovAdj = read.degradation.matrix( #' covFiles = list.files(bwPath,full.names=TRUE), #' sampleNames = list.files(bwPath), readLength = 76, #' totalMapped = rep(100e6,5),type="bwtool") #' #' ## Input data #' head(degCovAdj) #' #' ## Results #' qsva(degCovAdj) #' #' @export qsva <- function(degradationMatrix, mod = matrix(1, ncol=1, nrow = ncol(degradationMatrix))) { # do PCA degPca = prcomp(t(log2(degradationMatrix+1))) ## how many PCs? k = num.sv(log2(degradationMatrix+1), mod) # return qSVS degPca$x[, seq_len(k)] } sva/R/read.degradation.matrix.R0000644000175200017520000000653114710217751017421 0ustar00biocbuildbiocbuild#' A function for reading in coverage data from degradation-susceptible regions #' #' This function reads in degradation regions to form #' a library-size- and read-length-normalized #' degradation matrix for subsequent RNA quality correction #' #' @param covFiles coverage file(s) for degradation regions #' @param sampleNames sample names; creates column names of degradation matrix #' @param totalMapped how many reads per sample (library size normalization) #' @param readLength read length in base pairs (read length normalization) #' @param normFactor common library size to normalize to; 80M reads as default #' @param type whether input are individual `bwtool` output, `region_matrix` run on individual samples, or `region_matrix` run on all samples together #' @param BPPARAM (Optional) BiocParallelParam for parallel operation #' #' @return the normalized degradation matrix, region by sample #' #' @import BiocParallel #' #' @examples #' # bwtool #' bwPath = system.file('extdata', 'bwtool', package = 'sva') #' degCovAdj = read.degradation.matrix( #' covFiles = list.files(bwPath,full.names=TRUE), #' sampleNames = list.files(bwPath), readLength = 76, #' totalMapped = rep(100e6,5),type="bwtool") #' head(degCovAdj) #' #' # region_matrix: each sample #' r1Path = system.file('extdata', 'region_matrix_one', package = 'sva') #' degCovAdj1 = read.degradation.matrix( #' covFiles = list.files(r1Path,full.names=TRUE), #' sampleNames = list.files(r1Path), readLength = 76, #' totalMapped = rep(100e6,5),type="region_matrix_single") #' head(degCovAdj1) #' #' r2Path = system.file('extdata', 'region_matrix_all', package = 'sva') #' degCovAdj2 = read.degradation.matrix( #' covFiles = list.files(r2Path,full.names=TRUE), #' sampleNames = list.files(r1Path), readLength = 76, #' totalMapped = rep(100e6,5),type="region_matrix_all") #' head(degCovAdj2) #' #' @export read.degradation.matrix <- function(covFiles, sampleNames, totalMapped, readLength= 100, normFactor = 80e6, type = c("bwtool","region_matrix_single","region_matrix_all"), BPPARAM = bpparam()) { type <- match.arg(type) if(length(covFiles) != length(sampleNames) & type %in% c("bwtool", "region_matrix_single")) stop("Must provide one coverage file and sample name per sample for 'bwtool' or 'region_matrix_single' types") ## read in data if(type == "bwtool") { # bwtool output degCov = bplapply(covFiles, read.delim, as.is=TRUE, colClasses = c(rep("NULL",9), "numeric"),header=TRUE,BPPARAM=BPPARAM) degCovMat =do.call("cbind",degCov) colnames(degCovMat) = sampleNames } else if(type == "region_matrix_single") { # region w/ manifest degCov = bplapply(covFiles, read.delim, as.is=TRUE,header=TRUE,row.names=1,BPPARAM=BPPARAM) degCov = lapply(degCov,as.numeric) degCovMat =do.call("cbind",degCov) colnames(degCovMat) = sampleNames } else if(type == "region_matrix_all") { # region w/ individual file degCov = read.delim(covFiles, as.is=TRUE,row.names=1) degCovMat = t(as.matrix(degCov)) colnames(degCovMat) = sampleNames } else stop("type must be 'bwtool','region_matrix_single','region_matrix_all'") ## normalize for library size and read length degCovMat = degCovMat/readLength # read length bg = matrix(rep(totalMapped/normFactor), ncol = ncol(degCovMat), nrow = nrow(degCovMat), byrow=TRUE) degCovAdj = degCovMat/bg # return return(degCovAdj) }sva/R/ssva.R0000644000175200017520000000445514710217751013702 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables using a supervised approach #' #' This function implements a supervised surrogate variable analysis approach #' where genes/probes known to be affected by artifacts but not by the biological #' variables of interest are assumed to be known in advance. This supervised sva #' approach can be called through the \code{\link{sva}} and \code{\link{svaseq}} functions #' by specifying controls. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param controls A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control. #' @param n.sv The number of surogate variables to estimate #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity (exactly equal to controls for ssva) #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod (always null for ssva) #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:5000,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' set.seed(1234) #' controls <- runif(nrow(edata)) #' ssva_res <- ssva(edata,controls,n.sv) #' #' @export #' #' ssva <- function(dat,controls,n.sv){ if(is.null(n.sv)){stop("ssva error: You must specify the number of surrogate variables")} if(dim(dat)[1] != length(controls)){stop("ssva error: You must specify a control vector the same length as the number of genes.")} if(any(controls > 1) | any(controls < 0)){stop("ssva error: Control probabilities must be between 0 and 1.")} if (n.sv == 0) { warning("Returning zero surrogate variables as requested") return(list(sv=matrix(nrow=ncol(dat), ncol=0), pprob.gam = controls, pprob.b=NULL, n.sv=0)) } dats <- dat*controls allZero = rowMeans(dats==0) == 1 dats = dats[!allZero,] ss = svd((dats - rowMeans(dats))) sv = ss$v[,1:n.sv, drop=FALSE] return(list(sv=sv,pprob.gam = controls, pprob.b=NULL,n.sv=n.sv)) } sva/R/sva-package.R0000644000175200017520000000443014710217751015101 0ustar00biocbuildbiocbuild#' sva: a package for removing artifacts from microarray and sequencing data #' #' sva has functionality to estimate and remove artifacts from high dimensional data #' the \code{\link{sva}} function can be used to estimate artifacts from microarray data #' the \code{\link{svaseq}} function can be used to estimate artifacts from count-based #' RNA-sequencing (and other sequencing) data. The \code{\link{ComBat}} function can be #' used to remove known batch effecs from microarray data. The \code{\link{fsva}} function #' can be used to remove batch effects for prediction problems. #' #' #' A vignette is available by typing \code{browseVignettes("sva")} in the R prompt. #' #' @references For the package: Leek JT, Johnson WE, Parker HS, Jaffe AE, and Storey JD. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics DOI:10.1093/bioinformatics/bts034 #' @references For sva: Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences , 105: 18718-18723. #' @references For sva: Leek JT and Storey JD. (2007) Capturing heterogeneity in gene expression studies by `Surrogate Variable Analysis'. PLoS Genetics, 3: e161. #' @references For Combat: Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8 (1), 118-127 #' @references For svaseq: Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD #' @references For fsva: Parker HS, Bravo HC, Leek JT (2013) Removing batch effects for prediction problems with frozen surrogate variable analysis arXiv:1301.3947 #' @references For psva: Parker HS, Leek JT, Favorov AV, Considine M, Xia X, Chavan S, Chung CH, Fertig EJ (2014) Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction Bioinformatics doi: 10.1093/bioinformatics/btu375 #' #' @docType package #' @author Jeffrey T. Leek, W. Evan Johnson, Hilary S. Parker, Andrew E. Jaffe, John D. Storey, Yuqing Zhang #' @name sva #' #' @import genefilter #' @import mgcv #' @rawNamespace import(matrixStats, except = c(rowSds, rowVars)) #' #' @useDynLib sva, .registration = TRUE NULL sva/R/sva.R0000644000175200017520000000740714710217751013517 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables by estimating empirical control probes #' #' This function is the implementation of the iteratively re-weighted least squares #' approach for estimating surrogate variables. As a by product, this function #' produces estimates of the probability of being an empirical control. See the function #' \code{\link{empirical.controls}} for a direct estimate of the empirical controls. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param mod The model matrix being used to fit the data #' @param mod0 The null model being compared when fitting the data #' @param n.sv The number of surogate variables to estimate #' @param controls A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control. #' @param method For empirical estimation of control probes use "irw". If control probes are known use "supervised" #' @param vfilter You may choose to filter to the vfilter most variable rows before performing the analysis. vfilter must be NULL if method is "supervised" #' @param B The number of iterations of the irwsva algorithm to perform #' @param numSVmethod If n.sv is NULL, sva will attempt to estimate the number of needed surrogate variables. This should not be adapted by the user unless they are an expert. #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:5000,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' mod0 = model.matrix(~1,data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' svobj = sva(edata,mod,mod0,n.sv=n.sv) #' #' @export #' sva <- function(dat, mod, mod0 = NULL,n.sv=NULL,controls=NULL,method=c("irw","two-step","supervised"), vfilter=NULL,B=5, numSVmethod = "be") { method <- match.arg(method) if(!is.null(controls) & !is.null(vfilter)){stop("sva error: if controls is provided vfilter must be NULL.\n")} if((method=="supervised") & is.null(controls)){stop("sva error: for a supervised analysis you must provide a vector of controls.\n")} if(!is.null(controls) & (method!="supervised")){method = "supervised"; cat("sva warning: controls provided so supervised sva is being performed.\n")} if(!is.null(vfilter)){ if(vfilter < 100 | vfilter > dim(dat)[1]){ stop(paste("sva error: the number of genes used in the analysis must be between 100 and",dim(dat)[1],"\n")) } tmpv = rowVars(dat) ind = which(rank(-tmpv) <= vfilter) dat = dat[ind,] } if (!is.null(n.sv) && n.sv == 0) { warning("Returning zero surrogate variables as requested") return(list(sv=matrix(nrow=ncol(dat), ncol=0), pprob.gam = rep(0, nrow(dat)), pprob.b=NULL, n.sv=0)) } if(is.null(n.sv)){ n.sv = num.sv(dat,mod,method=numSVmethod,vfilter=vfilter) } if(n.sv > 0){ cat(paste("Number of significant surrogate variables is: ",n.sv,"\n")) if(method=="two-step"){ return(twostepsva.build(dat=dat, mod=mod,n.sv=n.sv)) } if(method=="irw"){ return(irwsva.build(dat=dat, mod=mod, mod0 = mod0,n.sv=n.sv,B=B)) } if(method=="supervised"){ return(ssva(dat,controls,n.sv)) } }else{ cat("No significant surrogate variables\n"); return(list(sv=matrix(nrow=ncol(dat), ncol=0), pprob.gam = rep(0, nrow(dat)), pprob.b=NULL, n.sv=0)) } } sva/R/sva.check.R0000644000175200017520000000321514710217751014564 0ustar00biocbuildbiocbuild#' A function for post-hoc checking of an sva object to check for #' degenerate cases. #' #' This function is designed to check for degenerate cases in the #' sva fit and fix the sva object where possible. #' #' \code{\link{empirical.controls}} for a direct estimate of the empirical controls. #' #' @param svaobj The transformed data matrix with the variables in rows and samples in columns #' @param dat The data set that was used to build the surrogate variables #' @param mod The model matrix being used to fit the data #' @param mod0 The null model matrix being used to fit the data #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' #dat <- bladderEset #' dat <- bladderEset[1:5000,] #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' mod0 = model.matrix(~1,data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' svobj = sva(edata,mod,mod0,n.sv=n.sv) #' svacheckobj = sva.check(svobj,edata,mod,mod0) #' #' @export #' #' sva.check <- function(svaobj,dat,mod,mod0){ if(mean(svaobj$pprob.gam) > 0.95){ message("Nearly all genes identified as batch associated, estimates of batch may be compromised. Please check your data carefully before using sva. Reverting to 2-step sva algorithm.") svaobj = sva(dat,mod,mod0,method="two-step") } return(svaobj) } sva/R/sva_network.R0000644000175200017520000000217414710217751015264 0ustar00biocbuildbiocbuild#' A function to adjust gene expression data before network inference #' #' This function corrects a gene expression matrix prior to network inference by #' returning the residuals after regressing out the top principal components. #' The number of principal components to remove can be determined using a #' permutation-based approach using the "num.sv" function with method = "be" #' @param dat The uncorrected normalized gene expression data matrix with samples in rows and genes in columns #' @param n.pc The number of principal components to remove #' @return dat.adjusted Cleaned gene expression data matrix with the top prinicpal components removed #' #' @examples #' library(bladderbatch) #' data(bladderdata) #' dat <- bladderEset[1:5000,] #' #' edata = exprs(dat) #' mod = matrix(1, nrow = dim(dat)[2], ncol = 1) #' #' n.pc = num.sv(edata, mod, method="be") #' dat.adjusted = sva_network(t(edata), n.pc) #' #' @export #' sva_network=function(dat, n.pc){ ss<-svd(dat - colMeans(dat)) dat.adjusted=dat for (i in seq_len(dim(dat)[2])) { dat.adjusted[,i]<- lm(dat[,i] ~ ss$u[,1:n.pc])$residuals } return(dat.adjusted) } sva/R/svaseq.R0000644000175200017520000001003514710217751014217 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables for count based RNA-seq data. #' #' This function is the implementation of the iteratively re-weighted least squares #' approach for estimating surrogate variables. As a by product, this function #' produces estimates of the probability of being an empirical control. This function first #' applies a moderated log transform as described in Leek 2014 before calculating the surrogate #' variables. See the function \code{\link{empirical.controls}} for a direct estimate of the empirical controls. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param mod The model matrix being used to fit the data #' @param mod0 The null model being compared when fitting the data #' @param n.sv The number of surogate variables to estimate #' @param controls A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control. #' @param method For empirical estimation of control probes use "irw". If control probes are known use "supervised" #' @param vfilter You may choose to filter to the vfilter most variable rows before performing the analysis. vfilter must be NULL if method is "supervised" #' @param B The number of iterations of the irwsva algorithm to perform #' @param numSVmethod If n.sv is NULL, sva will attempt to estimate the number of needed surrogate variables. This should not be adapted by the user unless they are an expert. #' @param constant The function takes log(dat + constant) before performing sva. By default constant = 1, all values of dat + constant should be positive. #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(zebrafishRNASeq) #' data(zfGenes) #' filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2) #' filtered = zfGenes[filter,] #' genes = rownames(filtered)[grep("^ENS", rownames(filtered))] #' controls = grepl("^ERCC", rownames(filtered)) #' group = as.factor(rep(c("Ctl", "Trt"), each=3)) #' dat0 = as.matrix(filtered) #' #' mod1 = model.matrix(~group) #' mod0 = cbind(mod1[,1]) #' svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv #' plot(svseq,pch=19,col="blue") #' #' @export #' svaseq <- function(dat, mod, mod0 = NULL,n.sv=NULL,controls=NULL,method=c("irw","two-step","supervised"), vfilter=NULL,B=5, numSVmethod = "be",constant = 1) { method <- match.arg(method) if(!is.null(controls) & !is.null(vfilter)){stop("sva error: if controls is provided vfilter must be NULL.\n")} if((method=="supervised") & is.null(controls)){stop("sva error: for a supervised analysis you must provide a vector of controls.\n")} if(!is.null(controls) & (method!="supervised")){method = "supervised"; cat("sva warning: controls provided so supervised sva is being performed.\n")} if(any(dat < 0)){stop("svaseq error: counts must be zero or greater")} dat = log(dat + constant) if(!is.null(vfilter)){ if(vfilter < 100 | vfilter > dim(dat)[1]){ stop(paste("sva error: the number of genes used in the analysis must be between 100 and",dim(dat)[1],"\n")) } tmpv = rowVars(dat) ind = which(rank(-tmpv) < vfilter) dat = dat[ind,] } if(is.null(n.sv)){ n.sv = num.sv(dat=dat,mod=mod,method=numSVmethod,vfilter=vfilter) } if(n.sv > 0){ cat(paste("Number of significant surrogate variables is: ",n.sv,"\n")) if(method=="two-step"){ return(twostepsva.build(dat=dat, mod=mod,n.sv=n.sv)) } if(method=="irw"){ return(irwsva.build(dat=dat, mod=mod, mod0 = mod0,n.sv=n.sv,B=B)) } if(method=="supervised"){ return(ssva(dat,controls,n.sv=n.sv)) } }else{ cat("No significant surrogate variables\n"); return(list(sv=0,pprob.gam=0,pprob.b=0,n.sv=0)) } } sva/R/twostepsva.build.R0000644000175200017520000000537714710217751016247 0ustar00biocbuildbiocbuild#' A function for estimating surrogate variables with the two step approach of Leek and Storey 2007 #' #' This function is the implementation of the two step approach for estimating surrogate #' variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily #' included for backwards compatibility. Newer versions of the sva algorithm are available #' through \code{\link{sva}}, \code{\link{svaseq}}, with low level functionality available #' through \code{\link{irwsva.build}} and \code{\link{ssva}}. #' #' @param dat The transformed data matrix with the variables in rows and samples in columns #' @param mod The model matrix being used to fit the data #' @param n.sv The number of surogate variables to estimate #' #' @return sv The estimated surrogate variables, one in each column #' @return pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity #' @return pprob.b A vector of the posterior probabilities each gene is affected by mod (this is always null for the two-step approach) #' @return n.sv The number of significant surrogate variables #' #' @examples #' library(bladderbatch) #' library(limma) #' data(bladderdata) #' dat <- bladderEset #' #' pheno = pData(dat) #' edata = exprs(dat) #' mod = model.matrix(~as.factor(cancer), data=pheno) #' #' n.sv = num.sv(edata,mod,method="leek") #' svatwostep <- twostepsva.build(edata,mod,n.sv) #' #' @export #' twostepsva.build <- function(dat, mod, n.sv){ n <- ncol(dat) m <- nrow(dat) H <- mod %*% solve(t(mod) %*% mod) %*% t(mod) res <- dat - t(H %*% t(dat)) uu <- svd(res) ndf <- n - ceiling(sum(diag(H))) dstat <- uu$d[1:ndf]^2/sum(uu$d[1:ndf]^2) res.sv <- uu$v[,1:n.sv, drop=FALSE] use.var <- matrix(rep(FALSE, n.sv*m), ncol=n.sv) pp <- matrix(rep(FALSE, n.sv*m), ncol=n.sv) for(i in 1:n.sv) { mod <- cbind(rep(1,n),res.sv[,i]) mod0 <- cbind(rep(1,n)) pp[,i] <-f.pvalue(dat,mod,mod0) use.var[,i] <- edge.lfdr(pp[,i]) < 0.10 } for(i in ncol(use.var):1) { if(sum(use.var[,i]) < n) { use.var <- as.matrix(use.var[,-i]) n.sv <- n.sv - 1 if(n.sv <= 0){break} } } if(n.sv >0){ sv <- matrix(0,nrow=n,ncol=n.sv) dat <- t(scale(t(dat),scale=FALSE)) for(i in 1:n.sv) { uu <- svd(dat[use.var[,i],]) maxcor <- 0 for(j in 1:(n-1)) { if(abs(cor(uu$v[,j], res.sv[,i])) > maxcor) { maxcor <- abs(cor(uu$v[,j], res.sv[,i])) sv[,i] <- uu$v[,j] } } } pprob.gam <- use.var %*% rep(1,n.sv) > 0 retval <- list(sv=sv,pprob.gam=pprob.gam,pprob.b=NULL,n.sv=n.sv) return(retval) } else{ sv <- rep(0,n) ind <- rep(0,m) n.sv <- 0 retval <- list(sv=sv,pprob.gam=rep(0,m),pprob.b = NULL,n.sv=n.sv) return(retval) } } sva/build/0000755000175200017520000000000014710321436013465 5ustar00biocbuildbiocbuildsva/build/vignette.rds0000644000175200017520000000035614710321436016030 0ustar00biocbuildbiocbuild‹mQK‚0-P?ILô\@î`Bpab qá¶ÂI°`[Ewž\ŒT'™væõÍ·»!Ä$”Ĵд¦xôQ'¨¡ÄÆ{ /Ì y©Ác„]uV¹HYö/¤ˆ îÕ™°â´ß(Ôü&qv©'ô¡WðC‹·Êl \¸¤LsîÆL±6<\/æNë왊.$ DJ¾ëO­äp·2mͧßphfÐö½MÕDZ6~ИF¨Ïð“¿3¿-òÒkwàTÿsÇ㉢/*ʘÔ5ª¦öñUß/z¤'Dásva/inst/0000755000175200017520000000000014710321436013343 5ustar00biocbuildbiocbuildsva/inst/doc/0000755000175200017520000000000014710321436014110 5ustar00biocbuildbiocbuildsva/inst/doc/sva.R0000644000175200017520000001641014710321436015026 0ustar00biocbuildbiocbuild### R code from vignette source 'sva.Rnw' ################################################### ### code chunk number 1: sva.Rnw:5-6 ################################################### options(width=65) ################################################### ### code chunk number 2: style-Sweave ################################################### BiocStyle::latex() ################################################### ### code chunk number 3: input ################################################### library(sva) library(bladderbatch) data(bladderdata) library(pamr) library(limma) ################################################### ### code chunk number 4: input ################################################### pheno = pData(bladderEset) ################################################### ### code chunk number 5: input ################################################### edata = exprs(bladderEset) ################################################### ### code chunk number 6: input ################################################### mod = model.matrix(~as.factor(cancer), data=pheno) ################################################### ### code chunk number 7: input ################################################### mod0 = model.matrix(~1,data=pheno) ################################################### ### code chunk number 8: input ################################################### n.sv = num.sv(edata,mod,method="leek") n.sv ################################################### ### code chunk number 9: input ################################################### svobj = sva(edata,mod,mod0,n.sv=n.sv) ################################################### ### code chunk number 10: input ################################################### pValues = f.pvalue(edata,mod,mod0) qValues = p.adjust(pValues,method="BH") ################################################### ### code chunk number 11: input ################################################### modSv = cbind(mod,svobj$sv) mod0Sv = cbind(mod0,svobj$sv) pValuesSv = f.pvalue(edata,modSv,mod0Sv) qValuesSv = p.adjust(pValuesSv,method="BH") ################################################### ### code chunk number 12: input ################################################### fit = lmFit(edata,modSv) ################################################### ### code chunk number 13: input ################################################### contrast.matrix <- cbind("C1"=c(-1,1,0,rep(0,svobj$n.sv)),"C2"=c(0,-1,1,rep(0,svobj$n.sv)),"C3"=c(-1,0,1,rep(0,svobj$n.sv))) fitContrasts = contrasts.fit(fit,contrast.matrix) ################################################### ### code chunk number 14: input ################################################### eb = eBayes(fitContrasts) topTableF(eb, adjust="BH") ################################################### ### code chunk number 15: input ################################################### batch = pheno$batch ################################################### ### code chunk number 16: input ################################################### modcombat = model.matrix(~1, data=pheno) ################################################### ### code chunk number 17: input ################################################### combat_edata = ComBat(dat=edata, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE) ################################################### ### code chunk number 18: input ################################################### pValuesComBat = f.pvalue(combat_edata,mod,mod0) qValuesComBat = p.adjust(pValuesComBat,method="BH") ################################################### ### code chunk number 19: input ################################################### count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) batch <- c(rep(1, 4), rep(2, 4)) adjusted <- ComBat_seq(count_matrix, batch=batch, group=NULL) ################################################### ### code chunk number 20: input ################################################### group <- rep(c(0,1), 4) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group) ################################################### ### code chunk number 21: input ################################################### cov1 <- rep(c(0,1), 4) cov2 <- c(0,0,1,1,0,0,1,1) covar_mat <- cbind(cov1, cov2) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, covar_mod=covar_mat) ################################################### ### code chunk number 22: input ################################################### modBatch = model.matrix(~as.factor(cancer) + as.factor(batch),data=pheno) mod0Batch = model.matrix(~as.factor(batch),data=pheno) pValuesBatch = f.pvalue(edata,modBatch,mod0Batch) qValuesBatch = p.adjust(pValuesBatch,method="BH") ################################################### ### code chunk number 23: input2 ################################################### n.sv = num.sv(edata,mod,vfilter=2000,method="leek") svobj = sva(edata,mod,mod0,n.sv=n.sv,vfilter=2000) ################################################### ### code chunk number 24: input ################################################### set.seed(12354) trainIndicator = sample(1:57,size=30,replace=FALSE) testIndicator = (1:57)[-trainIndicator] trainData = edata[,trainIndicator] testData = edata[,testIndicator] trainPheno = pheno[trainIndicator,] testPheno = pheno[testIndicator,] ################################################### ### code chunk number 25: input ################################################### mydata = list(x=trainData,y=trainPheno$cancer) mytrain = pamr.train(mydata) table(pamr.predict(mytrain,testData,threshold=2),testPheno$cancer) ################################################### ### code chunk number 26: input ################################################### trainMod = model.matrix(~cancer,data=trainPheno) trainMod0 = model.matrix(~1,data=trainPheno) trainSv = sva(trainData,trainMod,trainMod0) ################################################### ### code chunk number 27: input ################################################### fsvaobj = fsva(trainData,trainMod,trainSv,testData) mydataSv = list(x=fsvaobj$db,y=trainPheno$cancer) mytrainSv = pamr.train(mydataSv) table(pamr.predict(mytrainSv,fsvaobj$new,threshold=1),testPheno$cancer) ################################################### ### code chunk number 28: input ################################################### library(zebrafishRNASeq) data(zfGenes) filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2) filtered = zfGenes[filter,] genes = rownames(filtered)[grep("^ENS", rownames(filtered))] controls = grepl("^ERCC", rownames(filtered)) group = as.factor(rep(c("Ctl", "Trt"), each=3)) dat0 = as.matrix(filtered) ################################################### ### code chunk number 29: input3 ################################################### ## Set null and alternative models (ignore batch) mod1 = model.matrix(~group) mod0 = cbind(mod1[,1]) svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv plot(svseq,pch=19,col="blue") ################################################### ### code chunk number 30: input4 ################################################### sup_svseq = svaseq(dat0,mod1,mod0,controls=controls,n.sv=1)$sv plot(sup_svseq, svseq,pch=19,col="blue") sva/inst/doc/sva.Rnw0000644000175200017520000006402514710217751015404 0ustar00biocbuildbiocbuild% \VignetteIndexEntry{sva tutorial} % \VignetteKeywords{Gene expression data, RNA-seq, batch effects} % \VignettePackage{sva} \documentclass[12pt]{article} <>= options(width=65) @ <>= BiocStyle::latex() @ \SweaveOpts{eps=FALSE,echo=TRUE} \begin{document} \SweaveOpts{concordance=TRUE} \title{The SVA package for removing batch effects and other unwanted variation in high-throughput experiments} \author{Jeffrey Leek$^1$*, W. Evan Johnson$^2$, Andrew Jaffe$^1$, Hilary Parker$^1$, John Storey$^3$ \\ $^1$Johns Hopkins Bloomberg School of Public Health \\ $^2$Boston University\\ $^3$Princeton University\\ *email: \texttt{jleek@jhsph.edu}} \date{Modified: October 24, 2011 Compiled: \today} \maketitle \tableofcontents \section{Overview} The \Rpackage{sva} package contains functions for removing batch effects and other unwanted variation in high-throughput experiments. Specifically, the \Rpackage{sva} package contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The \Rpackage{sva} package can be used to remove artifacts in two ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments and (2) directly removing known batch effects using ComBat \cite{johnson:2007aa}. Leek et. al (2010) define batch effects as follows: \begin{quote} Batch effects are sub-groups of measurements that have qualitatively different behaviour across conditions and are unrelated to the biological or scientific variables in a study. For example, batch effects may occur if a subset of experiments was run on Monday and another set on Tuesday, if two technicians were responsible for different subsets of the experiments, or if two different lots of reagents, chips or instruments were used. \end{quote} The \Rpackage{sva} package includes the popular ComBat \cite{johnson:2007aa} function for directly modeling batch effects when they are known. There are also potentially a large number of environmental and biological variables that are unmeasured and may have a large impact on measurements from high-throughput biological experiments. For these cases the \Rfunction{sva} function may be more appropriate for removing these artifacts. It is also possible to use the \Rfunction{sva} function with the \Rfunction{ComBat} function to remove both known batch effects and other potential latent sources of variation. Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility (see \cite{leek:storey:2007,leek:storey:2008,leek:2010aa} for more detailed information). This document provides a tutorial for using the \Rpackage{sva} package. The tutorial includes information on (1) how to estimate the number of latent sources of variation, (2) how to apply the\Rpackage{sva} package to estimate latent variables such as batch effects, (3) how to directly remove known batch effects using the \Rfunction{ComBat} function, (4) how to perform differential expression analysis using surrogate variables either directly or with the\Rpackage{limma} package, and (4) how to apply ``frozen'' \Rfunction{sva} to improve prediction and clustering. As with any R package, detailed information on functions, along with their arguments and values, can be obtained in the help files. For instance, to view the help file for the function \Rfunction{sva} within R, type \texttt{?sva}. The analyses performed in this experiment are based on gene expression measurements from a bladder cancer study \cite{dyrskjot:2004aa}. The data can be loaded from the \Rpackage{bladderbatch} data package. The relevant packages for the Vignette can be loaded with the code: <>= library(sva) library(bladderbatch) data(bladderdata) library(pamr) library(limma) @ \section{Setting up the data} The first step in using the \Rpackage{sva} package is to properly format the data and create appropriate model matrices. The data should be a matrix with features (genes, transcripts, voxels) in the rows and samples in the columns. This is the typical genes by samples matrix found in gene expression analyses. The \Rpackage{sva} package assumes there are two types of variables that are being considered: (1) adjustment variables and (2) variables of interest. For example, in a gene expression study the variable of interest might an indicator of cancer versus control. The adjustment variables could be the age of the patients, the sex of the patients, and a variable like the date the arrays were processed. Two model matrices must be made: the ``full model'' and the ``null model''. The null model is a model matrix that includes terms for all of the adjustment variables but not the variables of interest. The full model includes terms for both the adjustment variables and the variables of interest. The assumption is that you will be trying to analyze the association between the variables of interest and gene expression, adjusting for the adjustment variables. The model matrices can be created using the \Rfunction{model.matrix}. \section{Setting up the data from an ExpressionSet} For the bladder cancer study, the variable of interest is cancer status. To begin we will assume no adjustment variables. The bladder data are stored in an expression set - a Bioconductor object used for storing gene expression data. The variables are stored in the phenotype data slot and can be obtained as follows: <>= pheno = pData(bladderEset) @ The expression data can be obtained from the expression slot of the expression set. <>= edata = exprs(bladderEset) @ Next we create the full model matrix - including both the adjustment variables and the variable of interest (cancer status). In this case we only have the variable of interest. Since cancer status has multiple levels, we treat it as a factor variable. <>= mod = model.matrix(~as.factor(cancer), data=pheno) @ The null model contains only the adjustment variables. Since we are not adjusting for any other variables in this analysis, only an intercept is included in the model. <>= mod0 = model.matrix(~1,data=pheno) @ Now that the model matrices have been created, we can apply the \Rfunction{sva} function to estimate batch and other artifacts. \section{Applying the \Rfunction{sva} function to estimate batch and other artifacts} The \Rfunction{sva} function performs two different steps. First it identifies the number of latent factors that need to be estimated. If the \Rfunction{sva} function is called without the \texttt{n.sv} argument specified, the number of factors will be estimated for you. The number of factors can also be estimated using the \Rfunction{num.sv}. <>= n.sv = num.sv(edata,mod,method="leek") n.sv @ Next we apply the \Rfunction{sva} function to estimate the surrogate variables: <>= svobj = sva(edata,mod,mod0,n.sv=n.sv) @ The \Rfunction{sva} function returns a list with four components, \texttt{sv}, \texttt{pprob.gam}, \texttt{pprob.b}, \texttt{n.sv}. \texttt{sv} is a matrix whose columns correspond to the estimated surrogate variables. They can be used in downstream analyses as described below. \texttt{pprob.gam} is the posterior probability that each gene is associated with one or more latent variables \cite{leek:2008aa}. \texttt{pprob.b} is the posterior probability that each gene is associated with the variables of interest \cite{leek:2008aa}. \texttt{n.sv} is the number of surrogate variables estimated by the \Rfunction{sva}. \section{Adjusting for surrogate variables using the \Rfunction{f.pvalue} function} The \Rfunction{f.pvalue} function can be used to calculate parametric F-test p-values for each row of a data matrix. In the case of the bladder study, this would correspond to calculating a parametric F-test p-value for each of the 22,283 rows of the matrix. The F-test compares the models \texttt{mod} and \texttt{mod0}. They must be nested models, so all of the variables in \texttt{mod0} must appear in \texttt{mod}. First we can calculate the F-test p-values for differential expression with respect to cancer status, without adjusting for surrogate variables, adjust them for multiple testing, and calculate the number that are significant with a Q-value less than 0.05. <>= pValues = f.pvalue(edata,mod,mod0) qValues = p.adjust(pValues,method="BH") @ Note that nearly 70\% of the genes are strongly differentially expressed at an FDR of less than 5\% between groups. This number seems artificially high, even for a strong phenotype like cancer. Now we can perform the same analysis, but adjusting for surrogate variables. The first step is to include the surrogate variables in both the null and full models. The reason is that we want to adjust for the surrogate variables, so we treat them as adjustment variables that must be included in both models. Then P-values and Q-values can be computed as before. <>= modSv = cbind(mod,svobj$sv) mod0Sv = cbind(mod0,svobj$sv) pValuesSv = f.pvalue(edata,modSv,mod0Sv) qValuesSv = p.adjust(pValuesSv,method="BH") @ Now these are the adjusted P-values and Q-values accounting for surrogate variables. \section{Adjusting for surrogate variables using the \Rpackage{limma} package} The \Rpackage{limma} package is one of the most commonly used packages for differential expression analysis. The \Rpackage{sva} package can easily be used in conjunction with the \Rpackage{limma} package to perform adjusted differential expression analysis. The first step in this process is to fit the linear model with the surrogate variables included. <>= fit = lmFit(edata,modSv) @ From here, you can use the \Rpackage{limma} functions to perform the usual analyses. As an example, suppose we wanted to calculate differential expression with respect to cancer. To do that we first compute the contrasts between the pairs of cancer/normal terms. We do not include the surrogate variables in the contrasts, since they are only being used to adjust the analysis. <>= contrast.matrix <- cbind("C1"=c(-1,1,0,rep(0,svobj$n.sv)),"C2"=c(0,-1,1,rep(0,svobj$n.sv)),"C3"=c(-1,0,1,rep(0,svobj$n.sv))) fitContrasts = contrasts.fit(fit,contrast.matrix) @ The next step is to calculate the test statistics using the \Rfunction{eBayes} function: <>= eb = eBayes(fitContrasts) topTableF(eb, adjust="BH") @ \section{Applying the \Rfunction{ComBat} function to adjust for known batches} The \Rfunction{ComBat} function adjusts for known batches using an empirical Bayesian framework \cite{johnson:2007aa}. In order to use the function, you must have a known batch variable in your dataset. <>= batch = pheno$batch @ Just as with \Rfunction{sva}, we then need to create a model matrix for the adjustment variables, including the variable of interest. Note that you do not include batch in creating this model matrix - it will be included later in the \Rfunction{ComBat} function. In this case there are no other adjustment variables so we simply fit an intercept term. <>= modcombat = model.matrix(~1, data=pheno) @ Note that adjustment variables will be treated as given to the \Rfunction{ComBat} function. This means if you are trying to adjust for a categorical variable with p different levels, you will need to give \Rfunction{ComBat} p-1 indicator variables for this covariate. We recommend using the \Rfunction{model.matrix} function to set these up. For continuous adjustment variables, just give a vector in the containing the covariate values in a single column of the model matrix. We now apply the \Rfunction{ComBat} function to the data, using parametric empirical Bayesian adjustments. <>= combat_edata = ComBat(dat=edata, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE) @ This returns an expression matrix, with the same dimensions as your original dataset. This new expression matrix has been adjusted for batch. Significance analysis can then be performed directly on the adjusted data using the model matrix and null model matrix as described before: <>= pValuesComBat = f.pvalue(combat_edata,mod,mod0) qValuesComBat = p.adjust(pValuesComBat,method="BH") @ These P-values and Q-values now account for the known batch effects included in the batch variable. There are a few additional options for the \Rfunction{ComBat} function. By default, it performs parametric empirical Bayesian adjustments. If you would like to use nonparametric empirical Bayesian adjustments, use the \texttt{par.prior=FALSE} option (this will take longer). Additionally, use the \texttt{prior.plots=TRUE} option to give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric estimate. For example, you might chose to use the parametric Bayesian adjustments for your data, but then can check the plots to ensure that the estimates were reasonable. Also, we have now added the \texttt{mean.only=TRUE} option, that only adjusts the mean of the batch effects across batches (default adjusts the mean and variance). This option is recommended for cases where milder batch effects are expected (so no need to adjust the variance), or in cases where the variances are expected to be different across batches due to the biology. For example, suppose a researcher wanted to project a knock-down genomic signature to be projected into the TCGA data. In this case, the knockdowns samples may be very similar to each other (low variance) whereas the signature will be at varying levels in the TCGA patient data. Thus the variances may be very different between the two batches (signature perturbation samples vs TCGA), so only adjusting the mean of the batch effect across the samples might be desired in this case. Finally, we have now added a \texttt{ref.batch} parameter, which allows users to select one batch as a reference to which other batches will be adjusted. Specifically, the means and variances of the non-reference batches will be adjusted to make the mean/variance of the reference batch. This is a useful feature for cases where one batch is larger or better quality. In addition, this will be useful in biomarker situations where the researcher wants to fix the traning set/model and then adjust test sets to the reference/training batch. This avoids test-set bias in such studies. When using the \texttt{mean.only=TRUE} or the \texttt{ref.batch} options, please cite \cite{zhang2018alternative}. \section{\Rfunction{ComBat-Seq} for batch adjustment on RNA-Seq count data} ComBat-Seq is an improved model based on the ComBat framework, which specifically targets RNA-Seq count data. It uses a negative binomial regression to model the count matrix, and estimate parameters representing the batch effects. Then it provides adjusted data by mapping the original data to an expected distribution if there were no batch effects. The adjusted data preserve the integer nature of count matrix. Like ComBat, it requires known a batch variable. <>= count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) batch <- c(rep(1, 4), rep(2, 4)) adjusted <- ComBat_seq(count_matrix, batch=batch, group=NULL) @ In ComBat-Seq, user may specify biological covariates, whose signals will be preserved in the adjusted data. If the user would like to specify one biological variable, they may use the \texttt{group} parameter <>= group <- rep(c(0,1), 4) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group) @ If users wish to specify multiple biological variables, they may pass them as a matrix or data frame to the \texttt{covar\_mod} parameter <>= cov1 <- rep(c(0,1), 4) cov2 <- c(0,0,1,1,0,0,1,1) covar_mat <- cbind(cov1, cov2) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, covar_mod=covar_mat) @ \section{Removing known batch effects with a linear model} Direct adjustment for batch effects can also be performed using the \Rfunction{f.pvalue} function. In the bladder cancer example, one of the known variables is a batch variable. This variable can be included as an adjustment variable in both \texttt{mod} and \texttt{mod0}. Then the \Rfunction{f.pvalue} function can be used to detect differential expression. This approach is a simplified version of \Rfunction{ComBat}. <>= modBatch = model.matrix(~as.factor(cancer) + as.factor(batch),data=pheno) mod0Batch = model.matrix(~as.factor(batch),data=pheno) pValuesBatch = f.pvalue(edata,modBatch,mod0Batch) qValuesBatch = p.adjust(pValuesBatch,method="BH") @ \section{Surrogate variables versus direct adjustment} The goal of the \Rfunction{sva} is to remove all unwanted sources of variation while protecting the contrasts due to the primary variables included in \texttt{mod}. This leads to the identification of features that are consistently different between groups, removing all common sources of latent variation. In some cases, the latent variables may be important sources of biological variability. If the goal of the analysis is to identify heterogeneity in one or more subgroups, the \Rfunction{sva} function may not be appropriate. For example, suppose that it is expected that cancer samples represent two distinct, but unknown subgroups. If these subgroups have a large impact on expression, then one or more of the estimated surrogate variables may be very highly correlated with subgroup. In contrast, direct adjustment only removes the effect of known batch variables. All sources of latent biological variation will remain in the data using this approach. In other words, if the samples were obtained in different environments, this effect will remain in the data. If important sources of heterogeneity (from different environments, lab effects, etc.) are not accounted for, this may lead to increased false positives. \section{Variance filtering to speed computations when the number of features is large ($m >100,000$)} When the number of features is very large ($m > 100,000$) both the \Rfunction{num.sv} and \Rfunction{sva} functions may be slow, since multiple singular value decompositions of the entire data matrix must be computed. Both functions include a variance filtering term, \texttt{vfilter}, which may be used to speed up the calculation. \texttt{vfilter} must be an integer between 100 and the total number of features $m$. The features are ranked from most variable to least variable by standard deviation. Computations will only be performed on the \texttt{vfilter} most variable features. This can improve computational time, but caution should be exercised, since the surrogate variables will only be estimated on a subset of the matrix. Running the functions with fewer than 1,000 features is not recommended. <>= n.sv = num.sv(edata,mod,vfilter=2000,method="leek") svobj = sva(edata,mod,mod0,n.sv=n.sv,vfilter=2000) @ \section{Applying the \Rfunction{fsva} function to remove batch effects for prediction} The surrogate variable analysis functions have been developed for population-level analyses such as differential expression analysis in microarrays. In some cases, the goal of an analysis is prediction. In this case, data sets are generally composed a training set and a test set. For each sample in the training set, the outcome/class is known, but latent sources of variability are unknown. For the samples in the test set, neither the outcome/class or the latent sources of variability are known. ``Frozen'' surrogate variable analysis can be used to remove latent variation in the test data set. To illustrate these functions, the bladder data can be separated into a training and test set. <>= set.seed(12354) trainIndicator = sample(1:57,size=30,replace=FALSE) testIndicator = (1:57)[-trainIndicator] trainData = edata[,trainIndicator] testData = edata[,testIndicator] trainPheno = pheno[trainIndicator,] testPheno = pheno[testIndicator,] @ Using these data sets, the \Rpackage{pamr} package can be used to train a predictive model on the training data, as well as test that prediction on a test data set. <>= mydata = list(x=trainData,y=trainPheno$cancer) mytrain = pamr.train(mydata) table(pamr.predict(mytrain,testData,threshold=2),testPheno$cancer) @ Next, the \Rfunction{sva} function can be used to calculate surrogate variables for the training set. <>= trainMod = model.matrix(~cancer,data=trainPheno) trainMod0 = model.matrix(~1,data=trainPheno) trainSv = sva(trainData,trainMod,trainMod0) @ The \Rfunction{fsva} function can be used to adjust both the training data and the test data. The training data is adjusted using the calculated surrogate variables. The testing data is adjusted using the ``frozen'' surrogate variable algorithm. The output of the \Rfunction{fsva} function is an adjusted training set and an adjusted test set. These can be used to train and test a second, more accurate, prediction function. <>= fsvaobj = fsva(trainData,trainMod,trainSv,testData) mydataSv = list(x=fsvaobj$db,y=trainPheno$cancer) mytrainSv = pamr.train(mydataSv) table(pamr.predict(mytrainSv,fsvaobj$new,threshold=1),testPheno$cancer) @ \section{sva for sequencing (svaseq)} In our original work we used the identify function for data measured on an approximately symmetric and continuous scale. For sequencing data, which are often represented as counts, a more suitable model may involve the use of a moderated log function \cite{frazee2014differential,bullard2010evaluation}. For example in Step 1 of the algorithm we may first transform the gene expression measurements by applying the function $log(g_{ij} + c)$ for a small positive constant. In the analyses that follow we will set $c = 1$. First we set up the data by filtering low count genes and identify potential control genes. The group variable in this case consists of two different treatments. <>= library(zebrafishRNASeq) data(zfGenes) filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2) filtered = zfGenes[filter,] genes = rownames(filtered)[grep("^ENS", rownames(filtered))] controls = grepl("^ERCC", rownames(filtered)) group = as.factor(rep(c("Ctl", "Trt"), each=3)) dat0 = as.matrix(filtered) @ Now we can apply svaseq to estimate the latent factor. In this case, we set $n.sv=1$ because the number of samples is small ($n = 6$) but in general svaseq can be used to estimate the number of latent factors. <>= ## Set null and alternative models (ignore batch) mod1 = model.matrix(~group) mod0 = cbind(mod1[,1]) svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv plot(svseq,pch=19,col="blue") @ \section{Supervised sva} In our original work we introduced an algorithm for estimating the genes affected only by unknown artifacts empirically \cite{leek:2007aa, leek:2008aa}. Subsequently, Gagnon-Bartsch and colleagues \cite{gagnon2012using} used our surrogate variable model but made the important point that for some technologies or experiments control probes can be used to identify the set of genes only affected by artifacts. Supervised sva uses known control probes to estimate the surrogate variables. You can use supervised sva with the standard sva function. Here we show an example of how to perform supervised sva with the svaseq function. <>= sup_svseq = svaseq(dat0,mod1,mod0,controls=controls,n.sv=1)$sv plot(sup_svseq, svseq,pch=19,col="blue") @ Here we passed the controls argument, which is a vector of values between 0 and 1, representing the probability that a gene is affected by batch but not affected by the group variable. Since we have known negative control genes in this example, we simply set controls[i] = TRUE for all control genes and controls[i] = FALSE for all non-controls. \section{What to cite} The sva package includes multiple different methods created by different faculty and students. It would really help them out if you would cite their work when you use this software. To cite the overall sva package cite: \begin{itemize} \item Leek JT, Johnson WE, Parker HS, Jaffe AE, and Storey JD. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics DOI:10.1093/bioinformatics/bts034 \end{itemize} For sva please cite: \begin{itemize} \item Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences , 105: 18718-18723. \item Leek JT and Storey JD. (2007) Capturing heterogeneity in gene expression studies by `Surrogate Variable Analysis'. PLoS Genetics, 3: e161. \end{itemize} For combat please cite: \begin{itemize} \item Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8 (1), 118-127 \end{itemize} For mean-only or reference-batch combat please cite: \begin{itemize} \item Zhang, Y., Jenkins, D. F., Manimaran, S., Johnson, W. E. (2018). Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC bioinformatics, 19 (1), 262. \end{itemize} For svaseq please cite: \begin{itemize} \item Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD \end{itemize} For supervised sva please cite: \begin{itemize} \item Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD \item Gagnon-Bartsch JA, Speed TP (2012) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13:539-52. \end{itemize} For fsva please cite: \begin{itemize} \item Parker HS, Bravo HC, Leek JT (2013) Removing batch effects for prediction problems with frozen surrogate variable analysis arXiv:1301.3947 \end{itemize} For psva please cite: \begin{itemize} \item Parker HS, Leek JT, Favorov AV, Considine M, Xia X, Chavan S, Chung CH, Fertig EJ (2014) Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction Bioinformatics doi: 10.1093/bioinformatics/btu375 \end{itemize} \bibliography{sva} \end{document} sva/inst/doc/sva.pdf0000644000175200017520000072061414710321435015405 0ustar00biocbuildbiocbuild%PDF-1.5 %ÐÔÅØ 64 0 obj << /Length 718 >> stream concordance:sva.tex:sva.Rnw:1 3 1 1 5 1 0 1 2 4 1 1 0 28 1 1 2 1 0 4 1 3 0 1 2 10 1 1 2 4 0 2 2 4 0 1 2 3 1 1 2 4 0 1 2 1 1 1 2 4 0 1 2 6 1 1 2 1 0 1 1 6 0 1 2 2 1 1 2 8 0 1 2 6 1 1 2 1 0 1 1 3 0 1 2 2 1 1 2 1 0 1 1 1 2 1 1 3 0 1 2 6 1 1 2 4 0 1 2 2 1 1 2 1 0 1 1 3 0 1 2 2 1 1 2 1 0 1 1 27 0 1 2 5 1 1 2 4 0 1 2 2 1 1 2 4 0 1 2 4 1 1 2 4 0 1 2 2 1 1 2 1 0 1 1 3 0 1 2 14 1 1 3 2 0 1 1 1 2 12 0 1 2 2 1 1 2 1 0 1 2 13 0 1 2 2 1 1 2 1 0 2 1 1 2 13 0 1 2 5 1 1 2 1 0 3 1 3 0 1 2 14 1 1 2 1 0 1 1 7 0 1 2 7 1 1 2 1 0 2 1 1 2 1 1 1 2 1 1 3 0 1 2 2 1 1 2 1 0 1 1 5 0 1 1 9 0 1 2 2 1 1 2 1 0 2 1 7 0 1 2 3 1 1 2 1 0 2 1 5 0 1 1 9 0 1 2 6 1 1 2 1 0 7 1 3 0 1 2 2 1 1 3 2 0 1 1 1 2 6 0 1 1 3 0 1 2 5 1 1 2 7 0 1 1 3 0 1 2 58 1 endstream endobj 83 0 obj << /Length 1431 /Filter /FlateDecode >> stream xÚíZKsÛ6¾ûWàHe"ïGN3I3žvâÖn{H{ %Hd,‘*IÙɿd‘#·®å÷è"€° ,w¿»"ASDФßbbåˆ`I¤ÑÐjE´F _3ô뮫OÞ|Ô 1µÑN$؆”XjNÇèKrš¹ÁQ–œü>0$y.éhÀHrž(I¦ƒ!üÆi¿¬ÂE忥¿¾È‹i¸s–6#¿&üuz»2 :MÐå&?æFMf§Å8tÊ&sQ沸L‹ÆÅ />­ò´ÉËb›Ì¼€‰Ú$Y>͆MVµÛ[N³Å² n@eòmáª|î P 2Ö–}óQHD5fT o Á°dh(,Ö<ÚçÈýI8¯Üw&Dò“sç^Ä›\#J±•°VZŽ%WHa&tXHô®…¹W°1íÕk¬lò >\¤þDB&GeVÔáÐ}mTrÌÝTÇ®SG;ꢶwŸr—AãQêOè¶ê~W ½©ÿ¨†\kl”ECj0³*Œ}Êgé€Ó¤“r¥“ã‡GÜÞ9÷¿àÛ¶BVŒ·{17Û‹YÙ«ñæô=•œ4¥WéŸéVE¹©‡‡iR"­&m(s l˜€ŽÁDÑÎŽ¸ÙñǽSaΣÀöÉÂ^˜J>•‹s/pãxãpVz¿MÊù´­û&•Ÿ1 ã'£,N˜ùЄIè/Ïfù( vé¬É¶í<î‡L‰é8Ïíû°¬›²ò+ò WÕy3Åßo¨‡ßLÏq•#÷/ª®„ ,Dô«Wnžæ³·!À1“ÄÔo[ÈÜ>ä˜2ÔmÚ%[îÃü§P@‚(¢œcÂ)€®Fó?`V`cA˜C“ƒ_n¹½€èY,á­¯‚Nf@%ø¿¶Á _gQ?|ÍêE†ÝxÙÃ;Á–ª5ý’j­Á‚G?ƒAŽs€áÆoCalòÜlÔ”gí˜ÇjSL¼!Å¥áÖûr¾ÈgWËzk`ªÉÝíÆw`ÁJ1ü/‚9 3‰`þ¾fé“Àê6L¹Ò¦°4| n)}ß*û (ƒí<Í:Oo—;ÈíÛ:\šØ6j­ÄÒŠp¼õùÀÞ$ðÆsä/ç({Û>ûAÜJ¨@»’ôÑkãPÞ¤ØÆ[ÁŽøuâš&$ð*¾\„¶isèŒÓ&½G0£ð"Ï­Ü£Ù~ðYÙv…üéàÃ*”:Á ÚÞ¤*ç¡—ÆŒÿ÷Eåê:/ q( m3½gˆ"|«céD<ÇZY·ãXâëÝb1û¾®Ky 2céó-eRØN’×Ù5-„,Ò5Äd¬‚®²BF1õ†˜z•þÿ½­rBȧ…0mRè5jcA¥Á”Æ‚L}± ÑöŸãXÓu–0Y£¶<,]†ÖÕM>OwU̶ýÚtž0„D”jlyô‚²A’&Ÿ´¢Ñ*o»—(—2iù‚|Ïé{Ã?ç¬H>ròÅ;N»Ü$ï‚›Æ_—u|í‘ðnãñ­ýO.êeU•ÓœÒ&mýÇ#!•IžžùúÏÌÕaê²^‹x4~ã–`#€à¨‚ R­NJ°¿‚ã–zƒJ‹­ŽÅ¶ ^\¤³¥»C–óÿ”ø‹ÈJ¼Çv{.ÚCâÞð{.zD.RwËEBŠ+.Rnr‘»Ž‹üÔÈE~Zä"8Ù<‡>¼âGT›åóy§õPNʸè¢îúƒ„©Ûï>þ÷†áÀ«ž(ðÆ=ô‹+PQF0'ú! 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28476 43800 11680 6726 71462 29328 1559 21117 1254 1438 2760 11862 61712 164821 16887 5756 5498 10600 4542 3194 32198 1749 9744 1575 48132 4784 18377 12087 6240 23128 5771 7331 2481 2179 2838 4155 7939 9162 11820 11653 676 723 2050 3578 2960 4536 11398 2042 2509 5455 6408 64224 4113 298708 2775 5495 12363 13752 8865 25397 11038 7415 2319 4106 6301 11665 2327 7773 567 5344 7776 6632 6548 2760 11739 46912 9083 13613 8057 5722 6711 499232 1115235 6216 5297 1360 12740 30441 1748 7224 18205 3162 67346 887 10330 14144 2029 3865 73331 14971 1866 3575 158264 23992 29072 31803 17112 670 31671 6901 23051 15361 25115 21518 20812 8416 8872 6229 1564 5725 723563 3343 3623 6673 10750 445 5836 9075 6776 7604 3502 11423 485 6194 16832 23415 7427 454 32815 35901 23327 12648 1451 9676 7223 1322 4785 3831 3072 1431 217020 3082 1026 3266 1022 701 9023 19109 47886 2251 7122 11095 8933 8285 6207 4720 5205 4083 48571 304 4862 738 10714 8083 2209 41674 2726 10246 1471 4109 5971 97790 69965 15601 6587 76242 1810 174 3243 370240 4685 90324 19911 1428 2487 92200 62619 49524 15873 10638 27117 5274 5038 3921 3274 30018 4537 9414 6941 7435 727 16597 2808 11982 15101 10785 48845 25393 13474 24647 34688 30722 7247 1671 1180 35205 4914 4643 7917 24677 953 25877 7035 20974 14500 20265 18716 14242 16174 17348 6748 29407 3044 3753 5624 2164 5243 13586 1340 6923 9713 3834 4960 5488 1642 426 28083 17435 9542 16687 3925 6481 13603 3145 6700 5934 8936 5106 180388 79934 40459 4129 93139 91820 49240 76039 355571 354762 220495 191221 127564 82079 135485 127292 17621 1686 976 81196 70895 9079 15187 4060 26065 11617 10074 9769 4853 17170 8580 19148 12646 1872 627 13520 2352 12323 162242 333 2161 4947 2013 588 1424 2451 2351 66278 1038 2394821 97237 237921 5780 2461 20447 3358 12582 1276 4831 46893 215690 43705 6647 21935 13877 25741 16070 37183 2181993 10465 108114 2292 6994 12464 9200 2682 1341 2068 12728 20580 7728 25107 29864 27854 39839 45884 34934 14619 48896 32792 49115 1600 62652 7119 14269 22920 3398 6039 2976 10132 18054 1621 1609 1418 50538 295 4545 818 63254 8065 5270 3186 10590 8934 5856 20209 1279746 4934 22004 202241 92413 2120 2404 12309 6746 101103 5647 7921 25631 21073 31283 28125 35042 12274 1236 2062 4020 3903 6104 9942 4045 791 9628 15541 12530 7447 7946 198058 16797 472052 10032 3653 35713 21874 37830 30699 14392 4549 3103 22033 17233 75647 2856 8970 8382 38492 45074 2568 391 917 3719 2260 6003 57159 5368 8227 15312 150966 1289 4594 3056 9479 6122 5399 4628 3729 4628 4975 1854 4250 86379 23203 62574 54690 8401 1361 9002 2990 3146 7814 15761 7767 10494 3654 10872 14604 32563 3534 17582 16350 2263 2274 35967 6044 56656 2727 2570 337271 41195 901 3477 5824 1964 5327 2688 6494 4464 0 1034 2171 16273 42050 56426 6798 1230 8601 5275 3406 9023 11011 1984 1179 822 1330 3319 3962 6565 10707 685 583 7171 5197 65351 2274 411528 3798 6048 4354 7516 1635 290021 1072 37769 24037 25794 5972 167044 4417 1528 6558 15749 8314 17655 4106 1549 3647 3763 3134 11270 3429 756 65116 28445 44257 4430 4687 7165 3151 3676 21092 2380 9441 2261 31379 22769 4411 947 4013 1874 1743 1398 5972 11400 8591 163242 12322 5961 8508 57438 18204 3631 2691 2500 7377 47713 33976 1543 48374 29770 1113 673 7095 77830 56840 42460 45932 28276 1281010 8593 31276 20040 41778 3445 25225 16425 36131 24482 14827 3677 50742 231187 3921 2098 3440 6093 4024 3488 69210 1611 1544 29212 903 1457 47582 2524 927 30473 1154 20761 12078 25419 16055 1539 6189 6115 32702 50029 22448 557 11909 4371 8426 7273 24460 14824 3528 4770 3247 7315 5472 8955 7304 117560 1614 980 207072 3614 83656 51788 758 1671 8167 4679 5061 14635 27718 105727 13902 1745 3169 619 27300 22949 1997 196319 6888 76871 14625 30314 3892 5762 86264 379255 774823 51714 17851 71314 4512 5624 18129 31170 17784 16598 81382 1693 2217 1605 1738 1824 25114 5735 7987 6754 28151 44412 49687 41316 90765 49239 47012 72388 111802 724559 2083 1445 1302 456 sva/man/0000755000175200017520000000000014710217751013145 5ustar00biocbuildbiocbuildsva/man/ComBat.Rd0000644000175200017520000000436114710217751014605 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ComBat.R \name{ComBat} \alias{ComBat} \title{Adjust for batch effects using an empirical Bayes framework} \usage{ ComBat( dat, batch, mod = NULL, par.prior = TRUE, prior.plots = FALSE, mean.only = FALSE, ref.batch = NULL, BPPARAM = bpparam("SerialParam") ) } \arguments{ \item{dat}{Genomic measure matrix (dimensions probe x sample) - for example, expression matrix} \item{batch}{{Batch covariate (only one batch allowed)}} \item{mod}{Model matrix for outcome of interest and other covariates besides batch} \item{par.prior}{(Optional) TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used} \item{prior.plots}{(Optional) TRUE give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric} \item{mean.only}{(Optional) FALSE If TRUE ComBat only corrects the mean of the batch effect (no scale adjustment)} \item{ref.batch}{(Optional) NULL If given, will use the selected batch as a reference for batch adjustment.} \item{BPPARAM}{(Optional) BiocParallelParam for parallel operation} } \value{ data A probe x sample genomic measure matrix, adjusted for batch effects. } \description{ ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects. Users are returned an expression matrix that has been corrected for batch effects. The input data are assumed to be cleaned and normalized before batch effect removal. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:50,] pheno = pData(dat) edata = exprs(dat) batch = pheno$batch mod = model.matrix(~as.factor(cancer), data=pheno) # parametric adjustment combat_edata1 = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE) # non-parametric adjustment, mean-only version combat_edata2 = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=FALSE, mean.only=TRUE) # reference-batch version, with covariates combat_edata3 = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, ref.batch=3) } sva/man/ComBat_seq.Rd0000644000175200017520000000317214710217751015454 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ComBat_seq.R \name{ComBat_seq} \alias{ComBat_seq} \title{Adjust for batch effects using an empirical Bayes framework in RNA-seq raw counts} \usage{ ComBat_seq( counts, batch, group = NULL, covar_mod = NULL, full_mod = TRUE, shrink = FALSE, shrink.disp = FALSE, gene.subset.n = NULL ) } \arguments{ \item{counts}{Raw count matrix from genomic studies (dimensions gene x sample)} \item{batch}{Vector / factor for batch} \item{group}{Vector / factor for biological condition of interest} \item{covar_mod}{Model matrix for multiple covariates to include in linear model (signals from these variables are kept in data after adjustment)} \item{full_mod}{Boolean, if TRUE include condition of interest in model} \item{shrink}{Boolean, whether to apply shrinkage on parameter estimation} \item{shrink.disp}{Boolean, whether to apply shrinkage on dispersion} \item{gene.subset.n}{Number of genes to use in empirical Bayes estimation, only useful when shrink = TRUE} } \value{ data A gene x sample count matrix, adjusted for batch effects. } \description{ ComBat_seq is an improved model from ComBat using negative binomial regression, which specifically targets RNA-Seq count data. } \examples{ count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) batch <- c(rep(1, 4), rep(2, 4)) group <- rep(c(0,1), 4) # include condition (group variable) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group, full_mod=TRUE) # do not include condition adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, full_mod=FALSE) } sva/man/empirical.controls.Rd0000644000175200017520000000252314710217751017245 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/empirical.controls.R \name{empirical.controls} \alias{empirical.controls} \title{A function for estimating the probability that each gene is an empirical control} \usage{ empirical.controls( dat, mod, mod0 = NULL, n.sv, B = 5, type = c("norm", "counts") ) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} \item{n.sv}{The number of surogate variables to estimate} \item{B}{The number of iterations of the irwsva algorithm to perform} \item{type}{If type is norm then standard irwsva is applied, if type is counts, then the moderated log transform is applied first} } \value{ pcontrol A vector of probabilites that each gene is a control. } \description{ This function uses the iteratively reweighted surrogate variable analysis approach to estimate the probability that each gene is an empirical control. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.sv = num.sv(edata,mod,method="leek") pcontrol <- empirical.controls(edata,mod,mod0=NULL,n.sv=n.sv,type="norm") } sva/man/f.pvalue.Rd0000644000175200017520000000211314710217751015151 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/f.pvalue.R \name{f.pvalue} \alias{f.pvalue} \title{A function for quickly calculating f statistic p-values for use in sva} \usage{ f.pvalue(dat, mod, mod0) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} } \value{ p A vector of F-statistic p-values one for each row of dat. } \description{ This function does simple linear algebra to calculate f-statistics for each row of a data matrix comparing the nested models defined by the design matrices for the alternative (mod) and and null (mod0) cases. The columns of mod0 must be a subset of the columns of mod. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:50,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1,data=pheno) pValues = f.pvalue(edata,mod,mod0) qValues = p.adjust(pValues,method="BH") } sva/man/fstats.Rd0000644000175200017520000000201414710217751014735 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fstats.R \name{fstats} \alias{fstats} \title{A function for quickly calculating f statistics for use in sva} \usage{ fstats(dat, mod, mod0) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} } \value{ fstats A vector of F-statistics one for each row of dat. } \description{ This function does simple linear algebra to calculate f-statistics for each row of a data matrix comparing the nested models defined by the design matrices for the alternative (mod) and and null (mod0) cases. The columns of mod0 must be a subset of the columns of mod. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:50,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1,data=pheno) fs <- fstats(edata, mod, mod0) } sva/man/fsva.Rd0000644000175200017520000000467114710217751014403 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fsva.R \name{fsva} \alias{fsva} \title{A function for performing frozen surrogate variable analysis as proposed in Parker, Corrada Bravo and Leek 2013} \usage{ fsva(dbdat, mod, sv, newdat = NULL, method = c("fast", "exact")) } \arguments{ \item{dbdat}{A m genes by n arrays matrix of expression data from the database/training data} \item{mod}{The model matrix for the terms included in the analysis for the training data} \item{sv}{The surrogate variable object created by running sva on dbdat using mod.} \item{newdat}{(optional) A set of test samples to be adjusted using the training database} \item{method}{If method ="fast" then the SVD is calculated using an online approach, this may introduce slight bias. If method="exact" the exact SVD is calculated, but will be slower} } \value{ db An adjusted version of the training database where the effect of batch/expression heterogeneity has been removed new An adjusted version of the new samples, adjusted one at a time using the fsva methodology. newsv Surrogate variables for the new samples } \description{ This function performs frozen surrogate variable analysis as described in Parker, Corrada Bravo and Leek 2013. The approach uses a training database to create surrogate variables which are then used to remove batch effects both from the training database and a new data set for prediction purposes. For inferential analysis see \code{\link{sva}}, \code{\link{svaseq}}, with low level functionality available through \code{\link{irwsva.build}} and \code{\link{ssva}}. } \examples{ library(bladderbatch) library(pamr) data(bladderdata) dat <- bladderEset[1:50,] pheno = pData(dat) edata = exprs(dat) set.seed(1234) trainIndicator = sample(1:57,size=30,replace=FALSE) testIndicator = (1:57)[-trainIndicator] trainData = edata[,trainIndicator] testData = edata[,testIndicator] trainPheno = pheno[trainIndicator,] testPheno = pheno[testIndicator,] mydata = list(x=trainData,y=trainPheno$cancer) mytrain = pamr.train(mydata) table(pamr.predict(mytrain,testData,threshold=2),testPheno$cancer) trainMod = model.matrix(~cancer,data=trainPheno) trainMod0 = model.matrix(~1,data=trainPheno) trainSv = sva(trainData,trainMod,trainMod0) fsvaobj = fsva(trainData,trainMod,trainSv,testData) mydataSv = list(x=fsvaobj$db,y=trainPheno$cancer) mytrainSv = pamr.train(mydataSv) table(pamr.predict(mytrainSv,fsvaobj$new,threshold=1),testPheno$cancer) } sva/man/irwsva.build.Rd0000644000175200017520000000300414710217751016042 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/irwsva.build.R \name{irwsva.build} \alias{irwsva.build} \title{A function for estimating surrogate variables by estimating empirical control probes} \usage{ irwsva.build(dat, mod, mod0 = NULL, n.sv, B = 5) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} \item{n.sv}{The number of surogate variables to estimate} \item{B}{The number of iterations of the irwsva algorithm to perform} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity pprob.b A vector of the posterior probabilities each gene is affected by mod n.sv The number of significant surrogate variables } \description{ This function is the implementation of the iteratively re-weighted least squares approach for estimating surrogate variables. As a buy product, this function produces estimates of the probability of being an empirical control. See the function \code{\link{empirical.controls}} for a direct estimate of the empirical controls. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.sv = num.sv(edata,mod,method="leek") res <- irwsva.build(edata, mod, mod0 = NULL,n.sv,B=5) } sva/man/num.sv.Rd0000644000175200017520000000255714710217751014673 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/num.sv.R \name{num.sv} \alias{num.sv} \title{A function for calculating the number of surrogate variables to estimate in a model} \usage{ num.sv(dat, mod, method = c("be", "leek"), vfilter = NULL, B = 20, seed = NULL) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{method}{One of "be" or "leek" as described in the details section} \item{vfilter}{You may choose to filter to the vfilter most variable rows before performing the analysis} \item{B}{The number of permutaitons to use if method = "be"} \item{seed}{Set a seed when using the permutation approach} } \value{ n.sv The number of surrogate variables to use in the sva software } \description{ This function estimates the number of surrogate variables that should be included in a differential expression model. The default approach is based on a permutation procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides an interface to the asymptotic approach proposed by Leek 2011 Biometrics. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.sv = num.sv(edata,mod,method="leek") } sva/man/psva.Rd0000644000175200017520000000232414710217751014406 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/psva.R \name{psva} \alias{psva} \title{A function for estimating surrogate variables with the two step approach of Leek and Storey 2007} \usage{ psva(dat, batch, ...) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{batch}{A factor variable giving the known batch levels} \item{...}{Other arguments to the \code{\link{sva}} function.} } \value{ psva.D Data with batch effect removed but biological heterogeneity preserved } \description{ This function is the implementation of the two step approach for estimating surrogate variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily included for backwards compatibility. Newer versions of the sva algorithm are available through \code{\link{sva}}, \code{\link{svaseq}}, with low level functionality available through \code{\link{irwsva.build}} and \code{\link{ssva}}. } \examples{ library(bladderbatch) library(limma) data(bladderdata) dat <- bladderEset[1:50,] pheno = pData(dat) edata = exprs(dat) batch = pheno$batch batch.fac = as.factor(batch) psva_data <- psva(edata,batch.fac) } \author{ Elana J. Fertig } sva/man/qsva.Rd0000644000175200017520000000175014710217751014411 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/qsva.R \name{qsva} \alias{qsva} \title{A function for computing quality surrogate variables (qSVs)} \usage{ qsva( degradationMatrix, mod = matrix(1, ncol = 1, nrow = ncol(degradationMatrix)) ) } \arguments{ \item{degradationMatrix}{the normalized degradation matrix, region by sample} \item{mod}{(Optional) statistical model used in DE analysis} } \value{ the qSV adjustment variables } \description{ This function computes quality surrogate variables (qSVs) from the library-size- and read-length-normalized degradation matrix for subsequent RNA quality correction } \examples{ ## Find files bwPath <- system.file('extdata', 'bwtool', package = 'sva') ## Read the data degCovAdj = read.degradation.matrix( covFiles = list.files(bwPath,full.names=TRUE), sampleNames = list.files(bwPath), readLength = 76, totalMapped = rep(100e6,5),type="bwtool") ## Input data head(degCovAdj) ## Results qsva(degCovAdj) } sva/man/read.degradation.matrix.Rd0000644000175200017520000000416714710217751020142 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/read.degradation.matrix.R \name{read.degradation.matrix} \alias{read.degradation.matrix} \title{A function for reading in coverage data from degradation-susceptible regions} \usage{ read.degradation.matrix( covFiles, sampleNames, totalMapped, readLength = 100, normFactor = 8e+07, type = c("bwtool", "region_matrix_single", "region_matrix_all"), BPPARAM = bpparam() ) } \arguments{ \item{covFiles}{coverage file(s) for degradation regions} \item{sampleNames}{sample names; creates column names of degradation matrix} \item{totalMapped}{how many reads per sample (library size normalization)} \item{readLength}{read length in base pairs (read length normalization)} \item{normFactor}{common library size to normalize to; 80M reads as default} \item{type}{whether input are individual `bwtool` output, `region_matrix` run on individual samples, or `region_matrix` run on all samples together} \item{BPPARAM}{(Optional) BiocParallelParam for parallel operation} } \value{ the normalized degradation matrix, region by sample } \description{ This function reads in degradation regions to form a library-size- and read-length-normalized degradation matrix for subsequent RNA quality correction } \examples{ # bwtool bwPath = system.file('extdata', 'bwtool', package = 'sva') degCovAdj = read.degradation.matrix( covFiles = list.files(bwPath,full.names=TRUE), sampleNames = list.files(bwPath), readLength = 76, totalMapped = rep(100e6,5),type="bwtool") head(degCovAdj) # region_matrix: each sample r1Path = system.file('extdata', 'region_matrix_one', package = 'sva') degCovAdj1 = read.degradation.matrix( covFiles = list.files(r1Path,full.names=TRUE), sampleNames = list.files(r1Path), readLength = 76, totalMapped = rep(100e6,5),type="region_matrix_single") head(degCovAdj1) r2Path = system.file('extdata', 'region_matrix_all', package = 'sva') degCovAdj2 = read.degradation.matrix( covFiles = list.files(r2Path,full.names=TRUE), sampleNames = list.files(r1Path), readLength = 76, totalMapped = rep(100e6,5),type="region_matrix_all") head(degCovAdj2) } sva/man/ssva.Rd0000644000175200017520000000311014710217751014403 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ssva.R \name{ssva} \alias{ssva} \title{A function for estimating surrogate variables using a supervised approach} \usage{ ssva(dat, controls, n.sv) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{controls}{A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control.} \item{n.sv}{The number of surogate variables to estimate} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity (exactly equal to controls for ssva) pprob.b A vector of the posterior probabilities each gene is affected by mod (always null for ssva) n.sv The number of significant surrogate variables } \description{ This function implements a supervised surrogate variable analysis approach where genes/probes known to be affected by artifacts but not by the biological variables of interest are assumed to be known in advance. This supervised sva approach can be called through the \code{\link{sva}} and \code{\link{svaseq}} functions by specifying controls. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.sv = num.sv(edata,mod,method="leek") set.seed(1234) controls <- runif(nrow(edata)) ssva_res <- ssva(edata,controls,n.sv) } sva/man/sva.Rd0000644000175200017520000001026314710217751014227 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sva-package.R, R/sva.R \docType{package} \name{sva} \alias{sva} \title{sva: a package for removing artifacts from microarray and sequencing data} \usage{ sva( dat, mod, mod0 = NULL, n.sv = NULL, controls = NULL, method = c("irw", "two-step", "supervised"), vfilter = NULL, B = 5, numSVmethod = "be" ) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} \item{n.sv}{The number of surogate variables to estimate} \item{controls}{A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control.} \item{method}{For empirical estimation of control probes use "irw". If control probes are known use "supervised"} \item{vfilter}{You may choose to filter to the vfilter most variable rows before performing the analysis. vfilter must be NULL if method is "supervised"} \item{B}{The number of iterations of the irwsva algorithm to perform} \item{numSVmethod}{If n.sv is NULL, sva will attempt to estimate the number of needed surrogate variables. This should not be adapted by the user unless they are an expert.} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity pprob.b A vector of the posterior probabilities each gene is affected by mod n.sv The number of significant surrogate variables } \description{ sva has functionality to estimate and remove artifacts from high dimensional data the \code{\link{sva}} function can be used to estimate artifacts from microarray data the \code{\link{svaseq}} function can be used to estimate artifacts from count-based RNA-sequencing (and other sequencing) data. The \code{\link{ComBat}} function can be used to remove known batch effecs from microarray data. The \code{\link{fsva}} function can be used to remove batch effects for prediction problems. This function is the implementation of the iteratively re-weighted least squares approach for estimating surrogate variables. As a by product, this function produces estimates of the probability of being an empirical control. See the function \code{\link{empirical.controls}} for a direct estimate of the empirical controls. } \details{ A vignette is available by typing \code{browseVignettes("sva")} in the R prompt. } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1,data=pheno) n.sv = num.sv(edata,mod,method="leek") svobj = sva(edata,mod,mod0,n.sv=n.sv) } \references{ For the package: Leek JT, Johnson WE, Parker HS, Jaffe AE, and Storey JD. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics DOI:10.1093/bioinformatics/bts034 For sva: Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences , 105: 18718-18723. For sva: Leek JT and Storey JD. (2007) Capturing heterogeneity in gene expression studies by `Surrogate Variable Analysis'. PLoS Genetics, 3: e161. For Combat: Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8 (1), 118-127 For svaseq: Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD For fsva: Parker HS, Bravo HC, Leek JT (2013) Removing batch effects for prediction problems with frozen surrogate variable analysis arXiv:1301.3947 For psva: Parker HS, Leek JT, Favorov AV, Considine M, Xia X, Chavan S, Chung CH, Fertig EJ (2014) Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction Bioinformatics doi: 10.1093/bioinformatics/btu375 } \author{ Jeffrey T. Leek, W. Evan Johnson, Hilary S. Parker, Andrew E. Jaffe, John D. Storey, Yuqing Zhang } sva/man/sva.check.Rd0000644000175200017520000000261114710217751015301 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sva.check.R \name{sva.check} \alias{sva.check} \title{A function for post-hoc checking of an sva object to check for degenerate cases.} \usage{ sva.check(svaobj, dat, mod, mod0) } \arguments{ \item{svaobj}{The transformed data matrix with the variables in rows and samples in columns} \item{dat}{The data set that was used to build the surrogate variables} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model matrix being used to fit the data} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity pprob.b A vector of the posterior probabilities each gene is affected by mod n.sv The number of significant surrogate variables } \description{ This function is designed to check for degenerate cases in the sva fit and fix the sva object where possible. } \details{ \code{\link{empirical.controls}} for a direct estimate of the empirical controls. } \examples{ library(bladderbatch) data(bladderdata) #dat <- bladderEset dat <- bladderEset[1:5000,] pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1,data=pheno) n.sv = num.sv(edata,mod,method="leek") svobj = sva(edata,mod,mod0,n.sv=n.sv) svacheckobj = sva.check(svobj,edata,mod,mod0) } sva/man/sva_network.Rd0000644000175200017520000000205514710217751016000 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sva_network.R \name{sva_network} \alias{sva_network} \title{A function to adjust gene expression data before network inference} \usage{ sva_network(dat, n.pc) } \arguments{ \item{dat}{The uncorrected normalized gene expression data matrix with samples in rows and genes in columns} \item{n.pc}{The number of principal components to remove} } \value{ dat.adjusted Cleaned gene expression data matrix with the top prinicpal components removed } \description{ This function corrects a gene expression matrix prior to network inference by returning the residuals after regressing out the top principal components. The number of principal components to remove can be determined using a permutation-based approach using the "num.sv" function with method = "be" } \examples{ library(bladderbatch) data(bladderdata) dat <- bladderEset[1:5000,] edata = exprs(dat) mod = matrix(1, nrow = dim(dat)[2], ncol = 1) n.pc = num.sv(edata, mod, method="be") dat.adjusted = sva_network(t(edata), n.pc) } sva/man/svaseq.Rd0000644000175200017520000000531014710217751014735 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/svaseq.R \name{svaseq} \alias{svaseq} \title{A function for estimating surrogate variables for count based RNA-seq data.} \usage{ svaseq( dat, mod, mod0 = NULL, n.sv = NULL, controls = NULL, method = c("irw", "two-step", "supervised"), vfilter = NULL, B = 5, numSVmethod = "be", constant = 1 ) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{mod0}{The null model being compared when fitting the data} \item{n.sv}{The number of surogate variables to estimate} \item{controls}{A vector of probabilities (between 0 and 1, inclusive) that each gene is a control. A value of 1 means the gene is certainly a control and a value of 0 means the gene is certainly not a control.} \item{method}{For empirical estimation of control probes use "irw". If control probes are known use "supervised"} \item{vfilter}{You may choose to filter to the vfilter most variable rows before performing the analysis. vfilter must be NULL if method is "supervised"} \item{B}{The number of iterations of the irwsva algorithm to perform} \item{numSVmethod}{If n.sv is NULL, sva will attempt to estimate the number of needed surrogate variables. This should not be adapted by the user unless they are an expert.} \item{constant}{The function takes log(dat + constant) before performing sva. By default constant = 1, all values of dat + constant should be positive.} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity pprob.b A vector of the posterior probabilities each gene is affected by mod n.sv The number of significant surrogate variables } \description{ This function is the implementation of the iteratively re-weighted least squares approach for estimating surrogate variables. As a by product, this function produces estimates of the probability of being an empirical control. This function first applies a moderated log transform as described in Leek 2014 before calculating the surrogate variables. See the function \code{\link{empirical.controls}} for a direct estimate of the empirical controls. } \examples{ library(zebrafishRNASeq) data(zfGenes) filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2) filtered = zfGenes[filter,] genes = rownames(filtered)[grep("^ENS", rownames(filtered))] controls = grepl("^ERCC", rownames(filtered)) group = as.factor(rep(c("Ctl", "Trt"), each=3)) dat0 = as.matrix(filtered) mod1 = model.matrix(~group) mod0 = cbind(mod1[,1]) svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv plot(svseq,pch=19,col="blue") } sva/man/twostepsva.build.Rd0000644000175200017520000000300114710217751016743 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/twostepsva.build.R \name{twostepsva.build} \alias{twostepsva.build} \title{A function for estimating surrogate variables with the two step approach of Leek and Storey 2007} \usage{ twostepsva.build(dat, mod, n.sv) } \arguments{ \item{dat}{The transformed data matrix with the variables in rows and samples in columns} \item{mod}{The model matrix being used to fit the data} \item{n.sv}{The number of surogate variables to estimate} } \value{ sv The estimated surrogate variables, one in each column pprob.gam: A vector of the posterior probabilities each gene is affected by heterogeneity pprob.b A vector of the posterior probabilities each gene is affected by mod (this is always null for the two-step approach) n.sv The number of significant surrogate variables } \description{ This function is the implementation of the two step approach for estimating surrogate variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily included for backwards compatibility. Newer versions of the sva algorithm are available through \code{\link{sva}}, \code{\link{svaseq}}, with low level functionality available through \code{\link{irwsva.build}} and \code{\link{ssva}}. } \examples{ library(bladderbatch) library(limma) data(bladderdata) dat <- bladderEset pheno = pData(dat) edata = exprs(dat) mod = model.matrix(~as.factor(cancer), data=pheno) n.sv = num.sv(edata,mod,method="leek") svatwostep <- twostepsva.build(edata,mod,n.sv) } sva/src/0000755000175200017520000000000014710321436013155 5ustar00biocbuildbiocbuildsva/src/sva.c0000644000175200017520000000130414710217751014114 0ustar00biocbuildbiocbuild#include #include #include #include SEXP monotone(SEXP lfdr){ int i,n; double *vec, *out; vec = REAL(lfdr); n = length(lfdr); SEXP Rlfdr; PROTECT(Rlfdr = allocVector(REALSXP,n)); out = REAL(Rlfdr); out[0] = vec[0]; for (i = 1; i < n; i++){ if(vec[i] < out[(i-1)]){ out[i] = out[(i-1)]; }else{ out[i] = vec[i]; } } UNPROTECT(1); return(Rlfdr); } static const R_CallMethodDef callMethods[] = { {"monotone", (DL_FUNC) &monotone, 1}, NULL }; void R_init_sva(DllInfo *info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); } void R_unload_sva(DllInfo *info) { (void) info; } sva/tests/0000755000175200017520000000000014710217751013534 5ustar00biocbuildbiocbuildsva/tests/testthat/0000755000175200017520000000000014710217751015374 5ustar00biocbuildbiocbuildsva/tests/testthat.R0000644000175200017520000000006214710217751015515 0ustar00biocbuildbiocbuildlibrary(testthat) library(sva) test_check("sva") sva/tests/testthat/test_combat_bladderbatch.R0000644000175200017520000000745414710217751022514 0ustar00biocbuildbiocbuild# Tests the ComBat output in the bladder batch dataset library(sva) library(bladderbatch) data(bladderdata) library(testthat) context("ComBat test on bladder batch data") test_that("check ComBat output with several different parameters on bladder cancer data",{ # get expression data, phenotype, and batch pheno = pData(bladderEset) edata = exprs(bladderEset) batch = pheno$batch # set up full and reduced models for testing mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1, data=pheno) #run ComBat without covariates: combat_edata = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<=.05),12468) #run ComBat without covariates (using the NULL model): combat_edata = ComBat(dat=edata, batch=batch, mod=mod0, par.prior=TRUE, prior.plots=FALSE) pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) expect_equal(pValuesComBat_null,pValuesComBat) #Check to see if the prior.plots option works combat_edata = ComBat(dat=edata, batch=batch, prior.plots=TRUE) pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) expect_equal(pValuesComBat_null,pValuesComBat) #run ComBat without covariates non-parametric (small dataset): combat_edata = ComBat(dat=edata[1:100,], batch=batch, par.prior=FALSE, prior.plots=FALSE) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<=.05),78) #run ComBat with covariates: combat_edata = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<.05),18507) #run ComBat with covariates, mean.only=TRUE: combat_edata = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, mean.only=TRUE) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<.05),17115) ######## Check reference version on bladder data in all situations above #run ComBat without covariates: combat_edata = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE, ref.batch=1) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<=.05),11614) #run ComBat without covariates (using the NULL model m0 and same batch): #combat_edata = ComBat(dat=edata, batch=batch, mod=mod0, # par.prior=TRUE, prior.plots=FALSE, # ref.batch=1) #pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) #expect_equal(pValuesComBat_null,pValuesComBat) #Check to see if the prior.plots option works #combat_edata = ComBat(dat=edata, batch=batch, prior.plots=TRUE, # ref.batch=1) #pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) #expect_equal(pValuesComBat_null,pValuesComBat) #run ComBat without covariates non-parametric (small dataset): #combat_edata = ComBat(dat=edata[1:100,], batch=batch, # par.prior=FALSE, prior.plots=FALSE, # ref.batch=1) #pValuesComBat = f.pvalue(combat_edata,mod,mod0) #expect_equal(sum(pValuesComBat<=.05),73) #run ComBat with covariates: #combat_edata = ComBat(dat=edata, batch=batch, # mod=mod, par.prior=TRUE, # prior.plots=FALSE, ref.batch=2) #pValuesComBat = f.pvalue(combat_edata,mod,mod0) #expect_equal(sum(pValuesComBat<.05),19122) #run ComBat with covariates, mean.only=TRUE: combat_edata = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, mean.only=TRUE, ref.batch=3) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<.05),18153) }) sva/tests/testthat/test_combat_bladderbatch_parallel.R0000644000175200017520000001033414710217751024357 0ustar00biocbuildbiocbuild# Tests the ComBat output in the bladder batch dataset library(sva) library(BiocParallel) library(bladderbatch) data(bladderdata) library(testthat) context("ComBat test on bladder batch data") test_that("check ComBat output with several different parameters on bladder cancer data",{ # get expression data, phenotype, and batch pheno = pData(bladderEset) edata = exprs(bladderEset) batch = pheno$batch # Set up parallel params serial <- SerialParam() par <- MulticoreParam(workers=2) # set up full and reduced models for testing mod = model.matrix(~as.factor(cancer), data=pheno) mod0 = model.matrix(~1, data=pheno) #run ComBat without covariates: combat_edata_serial = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE, BPPARAM = serial) combat_edata_parallel = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE, BPPARAM = par) expect_identical(combat_edata_serial, combat_edata_parallel) #run ComBat without covariates (using the NULL model): combat_edata_serial = ComBat(dat=edata, batch=batch, mod=mod0, par.prior=TRUE, prior.plots=FALSE, BPPARAM=serial) combat_edata_parallel = ComBat(dat=edata, batch=batch, mod=mod0, par.prior=TRUE, prior.plots=FALSE, BPPARAM=par) expect_identical(combat_edata_serial, combat_edata_parallel) #run ComBat without covariates non-parametric (small dataset): combat_edata_serial = ComBat(dat=edata[1:100,], batch=batch, par.prior=FALSE, prior.plots=FALSE, BPPARAM=serial) combat_edata_parallel = ComBat(dat=edata[1:100,], batch=batch, par.prior=FALSE, prior.plots=FALSE, BPPARAM=par) expect_identical(combat_edata_serial, combat_edata_parallel) #run ComBat with covariates: combat_edata_serial = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, BPPARAM=serial) combat_edata_parallel = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, BPPARAM=par) expect_identical(combat_edata_serial, combat_edata_parallel) #run ComBat with covariates, mean.only=TRUE: combat_edata_serial = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, mean.only=TRUE, BPPARAM=serial) combat_edata_parallel = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, mean.only=TRUE, BPPARAM=par) expect_identical(combat_edata_serial, combat_edata_parallel) ######## Check reference version on bladder data in all situations above #run ComBat without covariates: combat_edata = ComBat(dat=edata, batch=batch, mod=NULL, par.prior=TRUE, prior.plots=FALSE, ref.batch=1) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<=.05),11614) #run ComBat without covariates (using the NULL model m0 and same batch): #combat_edata = ComBat(dat=edata, batch=batch, mod=mod0, # par.prior=TRUE, prior.plots=FALSE, # ref.batch=1) #pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) #expect_equal(pValuesComBat_null,pValuesComBat) #Check to see if the prior.plots option works #combat_edata = ComBat(dat=edata, batch=batch, prior.plots=TRUE, # ref.batch=1) #pValuesComBat_null = f.pvalue(combat_edata,mod,mod0) #expect_equal(pValuesComBat_null,pValuesComBat) #run ComBat without covariates non-parametric (small dataset): #combat_edata = ComBat(dat=edata[1:100,], batch=batch, # par.prior=FALSE, prior.plots=FALSE, # ref.batch=1) #pValuesComBat = f.pvalue(combat_edata,mod,mod0) #expect_equal(sum(pValuesComBat<=.05),73) #run ComBat with covariates: #combat_edata = ComBat(dat=edata, batch=batch, # mod=mod, par.prior=TRUE, # prior.plots=FALSE, ref.batch=2) #pValuesComBat = f.pvalue(combat_edata,mod,mod0) #expect_equal(sum(pValuesComBat<.05),19122) #run ComBat with covariates, mean.only=TRUE: combat_edata = ComBat(dat=edata, batch=batch, mod=mod, par.prior=TRUE, prior.plots=FALSE, mean.only=TRUE, ref.batch=3) pValuesComBat = f.pvalue(combat_edata,mod,mod0) expect_equal(sum(pValuesComBat<.05),18153) }) sva/tests/testthat/test_combat_errors.R0000644000175200017520000000311114710217751021413 0ustar00biocbuildbiocbuildlibrary(sva) library(testthat) context("ComBat Error Check") test_that("Check ComBat errors for too many batch variables and confounded designs",{ # set up number of batches (nb), total sample size (n), and batch/treatment vaariables nb <- 2 ns <- 2 # one of nb or ns needs to be even (see treat variable) n <- ns*nb batch <- as.factor(rep(1:nb, each=ns)) treat <- rep(c(0,1), n/2) #generate random data X <- cbind(model.matrix(~-1+batch), treat) beta <- c(3*(1:nb-1), 2) sim <- 100 y <- matrix(rnorm(n*sim,X%*%beta),nrow=sim, byrow=TRUE) ## user gives multiple batch variables expect_error(ComBat(y,cbind(batch, batch)),"This version of ComBat only allows one batch variable") ## test confounded errors: expect_error(ComBat(y,batch, batch), "The covariate is confounded with batch! Remove the covariate and rerun ComBat") expect_error(ComBat(y,batch, cbind(treat,batch)), "At least one covariate is confounded with batch! Please remove confounded covariates and rerun ComBat") expect_error(ComBat(y,batch, cbind(treat,treat)), "The covariates are confounded! Please remove one or more of the covariates so the design is not confounded") ## test ref batch input error expect_error(ComBat(y,batch, ref.batch="yellow"), "reference level ref.batch is not one of the levels of the batch variable") #expect_error(ComBat(y,batch, ref.batch=100), "reference level ref.batch is not one of the levels of the batch variable") #expect_error(ComBat(y,batch, ref.batch=rep(0,3)), "reference level ref.batch is not one of the levels of the batch variable") }) sva/tests/testthat/test_combat_sim.R0000644000175200017520000001472414710217751020703 0ustar00biocbuildbiocbuildlibrary(sva) library(testthat) context("ComBat test on simulated data") ### Run ComBat on a simple simulated dataset test_that("check ComBat output with several different parameters on simulated genes",{ # generate simulated data nb <- 3 # number of batches nw <- 5 # samples within batch n <- nw*nb # total number of sampes batch <- rep(1:nb, each = nw) # batch variable for ComBat set.seed(0) treat <- rbinom(n,1,c(.5,.5)) # treatment variable--generated at random beta <- 1:(nb+1) # batch effects + treatment effect (last one) X <- model.matrix(~-1+as.factor(batch)+treat) genes <- 100 set.seed(0) y <- matrix(rnorm(n*genes,X%*%beta),nrow=genes, byrow=TRUE) # generate data ### Test ComBat with different parameters: # without covariates batch_only <- ComBat(y,batch) gene1_batch_only <- c(6.5275687,1.9434116,3.3016615,6.5353399,5.8317968,-0.3314552,3.9485709,4.5368716,4.8050618,7.0422784,4.2176928,2.6528653,2.3037212,7.1688336,3.1533711) expect_equal(as.numeric(batch_only[1,]),gene1_batch_only) # without covariates non-parametric batch_only_non <- ComBat(y,batch,par.prior=FALSE) gene1_batch_only_non <- c(6.5041159,1.9798940,3.3203856,6.5117856,5.8174409,-0.2101846,3.9735703,4.5486383,4.8107961,6.9976908,4.1925793,2.6502177,2.3060862,7.1013511,3.1435378) expect_equal(as.numeric(batch_only_non[1,]),gene1_batch_only_non) # with covariates batch_treat <- ComBat(y,batch,treat,par.prior=TRUE) gene1_batch_treat <- c(6.9550540,1.3750580,3.0128757,6.9644248,6.1160719,0.8808531,5.5188802,6.0339045,6.2686899,8.2272474,2.8499106,1.3062056,0.9617743,5.8187037,1.7999553) expect_equal(as.numeric(batch_treat[1,]),gene1_batch_treat) # with covariates non-parametric batch_treat_non <- ComBat(y,batch,treat,par.prior=FALSE) gene1_batch_treat_non <- c(6.9621810,1.4557761, 2.9477604, 6.9707174, 6.1979029, 0.9018672, 5.5466543, 6.0315322, 6.2525748, 8.0964907, 2.7563817, 1.3122575, 0.9900446, 5.8403030, 1.7741566) expect_equal(as.numeric(batch_treat_non[1,]),gene1_batch_treat_non) # with covariates missing data ## introduce missing values 10% set.seed(1) m <- matrix(rbinom(n*genes,1,.9),nrow=genes, byrow=TRUE) ymis <- y ymis[m==0] <- NA batch_treat_mis <- ComBat(ymis,batch,treat) gene1_batch_treat_mis <- c(7.0249052, 1.4302766, 3.0979753, NA, 6.1706164, 0.7848869, NA, 5.9650280, 6.2100799, 8.2542787, 2.8907924, 1.3319739, 0.9841704, 5.8422657, 1.8305576) expect_equal(as.numeric(batch_treat_mis[1,]),gene1_batch_treat_mis) # mean only ComBat batch_treat_mean_only <- ComBat(y,batch,treat,mean.only=TRUE) gene1_batch_treat_mean_only <- c(7.2030495, 1.6138619, 3.2698945, 7.2125245, 6.3547367, 0.5918231, 5.2032061, 5.8370527, 6.1260060, 8.5364265, 2.7343902, 1.1717875, 0.8231397, 5.6813352, 1.6715816) expect_equal(as.numeric(batch_treat_mean_only[1,]),gene1_batch_treat_mean_only) # mean only ComBat non parametric batch_treat_mean_only_non <- ComBat(y,batch,treat,par.prior=FALSE, mean.only=TRUE) gene1_batch_treat_mean_only_non <- c(6.9822181, 1.3930305, 3.0490631, 6.9916932, 6.1339053, 0.7107136, 5.3220966, 5.9559432, 6.2448965, 8.6553171, 2.8420714, 1.2794687, 0.9308209, 5.7890163, 1.7792628) expect_equal(as.numeric(batch_treat_mean_only_non[1,]),gene1_batch_treat_mean_only_non) # detect a single sample in a batch--automatically use mean only ComBat y_sub <- y[,-c(2:5)]; treat_sub <- treat[-c(2:5)]; batch_sub <- batch[-c(2:5)] batch_treat_mean_only <- ComBat(y_sub,batch_sub,treat_sub,mean.only=FALSE) gene1_batch_treat_mean_only <- c(7.141462, 1.001405, 5.612788, 6.246634, 6.535587, 8.946008, 2.961106, 1.398503, 1.049856, 5.908051, 1.898298) expect_equal(round(as.numeric(batch_treat_mean_only[1,]),6),gene1_batch_treat_mean_only) ############## check if ref batch is changed in all situations above # stats of batch info batch <- as.factor(batch) n.batch <- nlevels(batch) batches <- list() for (i in 1:n.batch){ batches[[i]] <- which(batch == levels(batch)[i]) } # list of samples in each batch n.batches <- sapply(batches, length) # without covariates #batch_only <- ComBat(y,batch, ref.batch=3) #refdat_prev <- y[,batches[[3]]] #refdat_adjust <- batch_only[,batches[[3]]] #expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) # without covariates non-parametric #batch_only_non <- ComBat(y,batch,par.prior=FALSE,ref.batch=2) #refdat_prev <- y[,batches[[2]]] #refdat_adjust <- batch_only_non[,batches[[2]]] #expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) # with covariates batch_treat <- ComBat(y,batch,par.prior=FALSE,ref.batch=1) refdat_prev <- y[,batches[[1]]] refdat_adjust <- batch_treat[,batches[[1]]] expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) # with covariates non-parametric #batch_treat_non <- ComBat(y,batch,treat,par.prior=FALSE,ref.batch=1) #refdat_prev <- y[,batches[[1]]] #refdat_adjust <- batch_treat_non[,batches[[1]]] #expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) # with covariates missing data ## introduce missing values 10% #set.seed(1) #m <- matrix(rbinom(n*genes,1,.9),nrow=genes, byrow=TRUE) #ymis <- y #ymis[m==0] <- NA #batch_treat_mis <- ComBat(ymis,batch,treat,ref.batch=2) #refdat_prev <- ymis[,batches[[2]]] #refdat_adjust <- batch_treat_mis[,batches[[2]]] #expect_equal(sum((refdat_prev==refdat_adjust)[m[,batches[[2]]]!=0]), # dim(refdat_prev)[1]*dim(refdat_prev)[2]-sum(m[,batches[[2]]]==0)) # mean only ComBat #batch_treat_mean_only <- ComBat(y,batch,treat,mean.only=TRUE,ref.batch=1) #refdat_prev <- y[,batches[[1]]] #refdat_adjust <- batch_treat[,batches[[1]]] #expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) # detect a single sample in a batch--automatically use mean only ComBat y_sub <- y[,-c(2:5)]; treat_sub <- treat[-c(2:5)]; batch_sub <- batch[-c(2:5)] n.batch <- nlevels(batch_sub) batches_sub <- list() for (i in 1:n.batch){ batches_sub[[i]] <- which(batch_sub == levels(batch_sub)[i]) } # list of samples in each batch n.batches_sub <- sapply(batches_sub, length) batch_treat_mean_only <- ComBat(y_sub,batch_sub,treat_sub,mean.only=FALSE, ref.batch=2) refdat_prev <- y_sub[,batches_sub[[2]]] refdat_adjust <- batch_treat_mean_only[,batches_sub[[2]]] expect_equal(sum(refdat_prev==refdat_adjust),dim(refdat_prev)[1]*dim(refdat_prev)[2]) }) sva/tests/testthat/test_combatseq.R0000644000175200017520000000146214710217751020537 0ustar00biocbuildbiocbuildlibrary(sva) library(testthat) context("ComBat-Seq tests") ### Run ComBat on a simple simulated dataset test_that("check ComBat-Seq output with several different parameters on simulated genes",{ count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) batch <- c(rep(1, 4), rep(2, 4)) group <- rep(c(0,1), 4) ### Test choice of parameter mod_choice <- capture.output(ComBat_seq(count_matrix, batch, group=rep(1, 8), full_mod=TRUE))[2] expect_equal(mod_choice, "Using null model in ComBat-seq.") ### Test error message expect_error(ComBat_seq(count_matrix, batch, group=batch), "The covariate is confounded with batch! Remove the covariate and rerun ComBat-Seq") expect_error(ComBat_seq(count_matrix, batch=c(1,rep(2,7))), "ComBat-seq doesn't support 1 sample per batch yet") }) sva/vignettes/0000755000175200017520000000000014710321436014376 5ustar00biocbuildbiocbuildsva/vignettes/sva.Rnw0000644000175200017520000006402514710217751015672 0ustar00biocbuildbiocbuild% \VignetteIndexEntry{sva tutorial} % \VignetteKeywords{Gene expression data, RNA-seq, batch effects} % \VignettePackage{sva} \documentclass[12pt]{article} <>= options(width=65) @ <>= BiocStyle::latex() @ \SweaveOpts{eps=FALSE,echo=TRUE} \begin{document} \SweaveOpts{concordance=TRUE} \title{The SVA package for removing batch effects and other unwanted variation in high-throughput experiments} \author{Jeffrey Leek$^1$*, W. Evan Johnson$^2$, Andrew Jaffe$^1$, Hilary Parker$^1$, John Storey$^3$ \\ $^1$Johns Hopkins Bloomberg School of Public Health \\ $^2$Boston University\\ $^3$Princeton University\\ *email: \texttt{jleek@jhsph.edu}} \date{Modified: October 24, 2011 Compiled: \today} \maketitle \tableofcontents \section{Overview} The \Rpackage{sva} package contains functions for removing batch effects and other unwanted variation in high-throughput experiments. Specifically, the \Rpackage{sva} package contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The \Rpackage{sva} package can be used to remove artifacts in two ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments and (2) directly removing known batch effects using ComBat \cite{johnson:2007aa}. Leek et. al (2010) define batch effects as follows: \begin{quote} Batch effects are sub-groups of measurements that have qualitatively different behaviour across conditions and are unrelated to the biological or scientific variables in a study. For example, batch effects may occur if a subset of experiments was run on Monday and another set on Tuesday, if two technicians were responsible for different subsets of the experiments, or if two different lots of reagents, chips or instruments were used. \end{quote} The \Rpackage{sva} package includes the popular ComBat \cite{johnson:2007aa} function for directly modeling batch effects when they are known. There are also potentially a large number of environmental and biological variables that are unmeasured and may have a large impact on measurements from high-throughput biological experiments. For these cases the \Rfunction{sva} function may be more appropriate for removing these artifacts. It is also possible to use the \Rfunction{sva} function with the \Rfunction{ComBat} function to remove both known batch effects and other potential latent sources of variation. Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility (see \cite{leek:storey:2007,leek:storey:2008,leek:2010aa} for more detailed information). This document provides a tutorial for using the \Rpackage{sva} package. The tutorial includes information on (1) how to estimate the number of latent sources of variation, (2) how to apply the\Rpackage{sva} package to estimate latent variables such as batch effects, (3) how to directly remove known batch effects using the \Rfunction{ComBat} function, (4) how to perform differential expression analysis using surrogate variables either directly or with the\Rpackage{limma} package, and (4) how to apply ``frozen'' \Rfunction{sva} to improve prediction and clustering. As with any R package, detailed information on functions, along with their arguments and values, can be obtained in the help files. For instance, to view the help file for the function \Rfunction{sva} within R, type \texttt{?sva}. The analyses performed in this experiment are based on gene expression measurements from a bladder cancer study \cite{dyrskjot:2004aa}. The data can be loaded from the \Rpackage{bladderbatch} data package. The relevant packages for the Vignette can be loaded with the code: <>= library(sva) library(bladderbatch) data(bladderdata) library(pamr) library(limma) @ \section{Setting up the data} The first step in using the \Rpackage{sva} package is to properly format the data and create appropriate model matrices. The data should be a matrix with features (genes, transcripts, voxels) in the rows and samples in the columns. This is the typical genes by samples matrix found in gene expression analyses. The \Rpackage{sva} package assumes there are two types of variables that are being considered: (1) adjustment variables and (2) variables of interest. For example, in a gene expression study the variable of interest might an indicator of cancer versus control. The adjustment variables could be the age of the patients, the sex of the patients, and a variable like the date the arrays were processed. Two model matrices must be made: the ``full model'' and the ``null model''. The null model is a model matrix that includes terms for all of the adjustment variables but not the variables of interest. The full model includes terms for both the adjustment variables and the variables of interest. The assumption is that you will be trying to analyze the association between the variables of interest and gene expression, adjusting for the adjustment variables. The model matrices can be created using the \Rfunction{model.matrix}. \section{Setting up the data from an ExpressionSet} For the bladder cancer study, the variable of interest is cancer status. To begin we will assume no adjustment variables. The bladder data are stored in an expression set - a Bioconductor object used for storing gene expression data. The variables are stored in the phenotype data slot and can be obtained as follows: <>= pheno = pData(bladderEset) @ The expression data can be obtained from the expression slot of the expression set. <>= edata = exprs(bladderEset) @ Next we create the full model matrix - including both the adjustment variables and the variable of interest (cancer status). In this case we only have the variable of interest. Since cancer status has multiple levels, we treat it as a factor variable. <>= mod = model.matrix(~as.factor(cancer), data=pheno) @ The null model contains only the adjustment variables. Since we are not adjusting for any other variables in this analysis, only an intercept is included in the model. <>= mod0 = model.matrix(~1,data=pheno) @ Now that the model matrices have been created, we can apply the \Rfunction{sva} function to estimate batch and other artifacts. \section{Applying the \Rfunction{sva} function to estimate batch and other artifacts} The \Rfunction{sva} function performs two different steps. First it identifies the number of latent factors that need to be estimated. If the \Rfunction{sva} function is called without the \texttt{n.sv} argument specified, the number of factors will be estimated for you. The number of factors can also be estimated using the \Rfunction{num.sv}. <>= n.sv = num.sv(edata,mod,method="leek") n.sv @ Next we apply the \Rfunction{sva} function to estimate the surrogate variables: <>= svobj = sva(edata,mod,mod0,n.sv=n.sv) @ The \Rfunction{sva} function returns a list with four components, \texttt{sv}, \texttt{pprob.gam}, \texttt{pprob.b}, \texttt{n.sv}. \texttt{sv} is a matrix whose columns correspond to the estimated surrogate variables. They can be used in downstream analyses as described below. \texttt{pprob.gam} is the posterior probability that each gene is associated with one or more latent variables \cite{leek:2008aa}. \texttt{pprob.b} is the posterior probability that each gene is associated with the variables of interest \cite{leek:2008aa}. \texttt{n.sv} is the number of surrogate variables estimated by the \Rfunction{sva}. \section{Adjusting for surrogate variables using the \Rfunction{f.pvalue} function} The \Rfunction{f.pvalue} function can be used to calculate parametric F-test p-values for each row of a data matrix. In the case of the bladder study, this would correspond to calculating a parametric F-test p-value for each of the 22,283 rows of the matrix. The F-test compares the models \texttt{mod} and \texttt{mod0}. They must be nested models, so all of the variables in \texttt{mod0} must appear in \texttt{mod}. First we can calculate the F-test p-values for differential expression with respect to cancer status, without adjusting for surrogate variables, adjust them for multiple testing, and calculate the number that are significant with a Q-value less than 0.05. <>= pValues = f.pvalue(edata,mod,mod0) qValues = p.adjust(pValues,method="BH") @ Note that nearly 70\% of the genes are strongly differentially expressed at an FDR of less than 5\% between groups. This number seems artificially high, even for a strong phenotype like cancer. Now we can perform the same analysis, but adjusting for surrogate variables. The first step is to include the surrogate variables in both the null and full models. The reason is that we want to adjust for the surrogate variables, so we treat them as adjustment variables that must be included in both models. Then P-values and Q-values can be computed as before. <>= modSv = cbind(mod,svobj$sv) mod0Sv = cbind(mod0,svobj$sv) pValuesSv = f.pvalue(edata,modSv,mod0Sv) qValuesSv = p.adjust(pValuesSv,method="BH") @ Now these are the adjusted P-values and Q-values accounting for surrogate variables. \section{Adjusting for surrogate variables using the \Rpackage{limma} package} The \Rpackage{limma} package is one of the most commonly used packages for differential expression analysis. The \Rpackage{sva} package can easily be used in conjunction with the \Rpackage{limma} package to perform adjusted differential expression analysis. The first step in this process is to fit the linear model with the surrogate variables included. <>= fit = lmFit(edata,modSv) @ From here, you can use the \Rpackage{limma} functions to perform the usual analyses. As an example, suppose we wanted to calculate differential expression with respect to cancer. To do that we first compute the contrasts between the pairs of cancer/normal terms. We do not include the surrogate variables in the contrasts, since they are only being used to adjust the analysis. <>= contrast.matrix <- cbind("C1"=c(-1,1,0,rep(0,svobj$n.sv)),"C2"=c(0,-1,1,rep(0,svobj$n.sv)),"C3"=c(-1,0,1,rep(0,svobj$n.sv))) fitContrasts = contrasts.fit(fit,contrast.matrix) @ The next step is to calculate the test statistics using the \Rfunction{eBayes} function: <>= eb = eBayes(fitContrasts) topTableF(eb, adjust="BH") @ \section{Applying the \Rfunction{ComBat} function to adjust for known batches} The \Rfunction{ComBat} function adjusts for known batches using an empirical Bayesian framework \cite{johnson:2007aa}. In order to use the function, you must have a known batch variable in your dataset. <>= batch = pheno$batch @ Just as with \Rfunction{sva}, we then need to create a model matrix for the adjustment variables, including the variable of interest. Note that you do not include batch in creating this model matrix - it will be included later in the \Rfunction{ComBat} function. In this case there are no other adjustment variables so we simply fit an intercept term. <>= modcombat = model.matrix(~1, data=pheno) @ Note that adjustment variables will be treated as given to the \Rfunction{ComBat} function. This means if you are trying to adjust for a categorical variable with p different levels, you will need to give \Rfunction{ComBat} p-1 indicator variables for this covariate. We recommend using the \Rfunction{model.matrix} function to set these up. For continuous adjustment variables, just give a vector in the containing the covariate values in a single column of the model matrix. We now apply the \Rfunction{ComBat} function to the data, using parametric empirical Bayesian adjustments. <>= combat_edata = ComBat(dat=edata, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE) @ This returns an expression matrix, with the same dimensions as your original dataset. This new expression matrix has been adjusted for batch. Significance analysis can then be performed directly on the adjusted data using the model matrix and null model matrix as described before: <>= pValuesComBat = f.pvalue(combat_edata,mod,mod0) qValuesComBat = p.adjust(pValuesComBat,method="BH") @ These P-values and Q-values now account for the known batch effects included in the batch variable. There are a few additional options for the \Rfunction{ComBat} function. By default, it performs parametric empirical Bayesian adjustments. If you would like to use nonparametric empirical Bayesian adjustments, use the \texttt{par.prior=FALSE} option (this will take longer). Additionally, use the \texttt{prior.plots=TRUE} option to give prior plots with black as a kernel estimate of the empirical batch effect density and red as the parametric estimate. For example, you might chose to use the parametric Bayesian adjustments for your data, but then can check the plots to ensure that the estimates were reasonable. Also, we have now added the \texttt{mean.only=TRUE} option, that only adjusts the mean of the batch effects across batches (default adjusts the mean and variance). This option is recommended for cases where milder batch effects are expected (so no need to adjust the variance), or in cases where the variances are expected to be different across batches due to the biology. For example, suppose a researcher wanted to project a knock-down genomic signature to be projected into the TCGA data. In this case, the knockdowns samples may be very similar to each other (low variance) whereas the signature will be at varying levels in the TCGA patient data. Thus the variances may be very different between the two batches (signature perturbation samples vs TCGA), so only adjusting the mean of the batch effect across the samples might be desired in this case. Finally, we have now added a \texttt{ref.batch} parameter, which allows users to select one batch as a reference to which other batches will be adjusted. Specifically, the means and variances of the non-reference batches will be adjusted to make the mean/variance of the reference batch. This is a useful feature for cases where one batch is larger or better quality. In addition, this will be useful in biomarker situations where the researcher wants to fix the traning set/model and then adjust test sets to the reference/training batch. This avoids test-set bias in such studies. When using the \texttt{mean.only=TRUE} or the \texttt{ref.batch} options, please cite \cite{zhang2018alternative}. \section{\Rfunction{ComBat-Seq} for batch adjustment on RNA-Seq count data} ComBat-Seq is an improved model based on the ComBat framework, which specifically targets RNA-Seq count data. It uses a negative binomial regression to model the count matrix, and estimate parameters representing the batch effects. Then it provides adjusted data by mapping the original data to an expected distribution if there were no batch effects. The adjusted data preserve the integer nature of count matrix. Like ComBat, it requires known a batch variable. <>= count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8) batch <- c(rep(1, 4), rep(2, 4)) adjusted <- ComBat_seq(count_matrix, batch=batch, group=NULL) @ In ComBat-Seq, user may specify biological covariates, whose signals will be preserved in the adjusted data. If the user would like to specify one biological variable, they may use the \texttt{group} parameter <>= group <- rep(c(0,1), 4) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group) @ If users wish to specify multiple biological variables, they may pass them as a matrix or data frame to the \texttt{covar\_mod} parameter <>= cov1 <- rep(c(0,1), 4) cov2 <- c(0,0,1,1,0,0,1,1) covar_mat <- cbind(cov1, cov2) adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, covar_mod=covar_mat) @ \section{Removing known batch effects with a linear model} Direct adjustment for batch effects can also be performed using the \Rfunction{f.pvalue} function. In the bladder cancer example, one of the known variables is a batch variable. This variable can be included as an adjustment variable in both \texttt{mod} and \texttt{mod0}. Then the \Rfunction{f.pvalue} function can be used to detect differential expression. This approach is a simplified version of \Rfunction{ComBat}. <>= modBatch = model.matrix(~as.factor(cancer) + as.factor(batch),data=pheno) mod0Batch = model.matrix(~as.factor(batch),data=pheno) pValuesBatch = f.pvalue(edata,modBatch,mod0Batch) qValuesBatch = p.adjust(pValuesBatch,method="BH") @ \section{Surrogate variables versus direct adjustment} The goal of the \Rfunction{sva} is to remove all unwanted sources of variation while protecting the contrasts due to the primary variables included in \texttt{mod}. This leads to the identification of features that are consistently different between groups, removing all common sources of latent variation. In some cases, the latent variables may be important sources of biological variability. If the goal of the analysis is to identify heterogeneity in one or more subgroups, the \Rfunction{sva} function may not be appropriate. For example, suppose that it is expected that cancer samples represent two distinct, but unknown subgroups. If these subgroups have a large impact on expression, then one or more of the estimated surrogate variables may be very highly correlated with subgroup. In contrast, direct adjustment only removes the effect of known batch variables. All sources of latent biological variation will remain in the data using this approach. In other words, if the samples were obtained in different environments, this effect will remain in the data. If important sources of heterogeneity (from different environments, lab effects, etc.) are not accounted for, this may lead to increased false positives. \section{Variance filtering to speed computations when the number of features is large ($m >100,000$)} When the number of features is very large ($m > 100,000$) both the \Rfunction{num.sv} and \Rfunction{sva} functions may be slow, since multiple singular value decompositions of the entire data matrix must be computed. Both functions include a variance filtering term, \texttt{vfilter}, which may be used to speed up the calculation. \texttt{vfilter} must be an integer between 100 and the total number of features $m$. The features are ranked from most variable to least variable by standard deviation. Computations will only be performed on the \texttt{vfilter} most variable features. This can improve computational time, but caution should be exercised, since the surrogate variables will only be estimated on a subset of the matrix. Running the functions with fewer than 1,000 features is not recommended. <>= n.sv = num.sv(edata,mod,vfilter=2000,method="leek") svobj = sva(edata,mod,mod0,n.sv=n.sv,vfilter=2000) @ \section{Applying the \Rfunction{fsva} function to remove batch effects for prediction} The surrogate variable analysis functions have been developed for population-level analyses such as differential expression analysis in microarrays. In some cases, the goal of an analysis is prediction. In this case, data sets are generally composed a training set and a test set. For each sample in the training set, the outcome/class is known, but latent sources of variability are unknown. For the samples in the test set, neither the outcome/class or the latent sources of variability are known. ``Frozen'' surrogate variable analysis can be used to remove latent variation in the test data set. To illustrate these functions, the bladder data can be separated into a training and test set. <>= set.seed(12354) trainIndicator = sample(1:57,size=30,replace=FALSE) testIndicator = (1:57)[-trainIndicator] trainData = edata[,trainIndicator] testData = edata[,testIndicator] trainPheno = pheno[trainIndicator,] testPheno = pheno[testIndicator,] @ Using these data sets, the \Rpackage{pamr} package can be used to train a predictive model on the training data, as well as test that prediction on a test data set. <>= mydata = list(x=trainData,y=trainPheno$cancer) mytrain = pamr.train(mydata) table(pamr.predict(mytrain,testData,threshold=2),testPheno$cancer) @ Next, the \Rfunction{sva} function can be used to calculate surrogate variables for the training set. <>= trainMod = model.matrix(~cancer,data=trainPheno) trainMod0 = model.matrix(~1,data=trainPheno) trainSv = sva(trainData,trainMod,trainMod0) @ The \Rfunction{fsva} function can be used to adjust both the training data and the test data. The training data is adjusted using the calculated surrogate variables. The testing data is adjusted using the ``frozen'' surrogate variable algorithm. The output of the \Rfunction{fsva} function is an adjusted training set and an adjusted test set. These can be used to train and test a second, more accurate, prediction function. <>= fsvaobj = fsva(trainData,trainMod,trainSv,testData) mydataSv = list(x=fsvaobj$db,y=trainPheno$cancer) mytrainSv = pamr.train(mydataSv) table(pamr.predict(mytrainSv,fsvaobj$new,threshold=1),testPheno$cancer) @ \section{sva for sequencing (svaseq)} In our original work we used the identify function for data measured on an approximately symmetric and continuous scale. For sequencing data, which are often represented as counts, a more suitable model may involve the use of a moderated log function \cite{frazee2014differential,bullard2010evaluation}. For example in Step 1 of the algorithm we may first transform the gene expression measurements by applying the function $log(g_{ij} + c)$ for a small positive constant. In the analyses that follow we will set $c = 1$. First we set up the data by filtering low count genes and identify potential control genes. The group variable in this case consists of two different treatments. <>= library(zebrafishRNASeq) data(zfGenes) filter = apply(zfGenes, 1, function(x) length(x[x>5])>=2) filtered = zfGenes[filter,] genes = rownames(filtered)[grep("^ENS", rownames(filtered))] controls = grepl("^ERCC", rownames(filtered)) group = as.factor(rep(c("Ctl", "Trt"), each=3)) dat0 = as.matrix(filtered) @ Now we can apply svaseq to estimate the latent factor. In this case, we set $n.sv=1$ because the number of samples is small ($n = 6$) but in general svaseq can be used to estimate the number of latent factors. <>= ## Set null and alternative models (ignore batch) mod1 = model.matrix(~group) mod0 = cbind(mod1[,1]) svseq = svaseq(dat0,mod1,mod0,n.sv=1)$sv plot(svseq,pch=19,col="blue") @ \section{Supervised sva} In our original work we introduced an algorithm for estimating the genes affected only by unknown artifacts empirically \cite{leek:2007aa, leek:2008aa}. Subsequently, Gagnon-Bartsch and colleagues \cite{gagnon2012using} used our surrogate variable model but made the important point that for some technologies or experiments control probes can be used to identify the set of genes only affected by artifacts. Supervised sva uses known control probes to estimate the surrogate variables. You can use supervised sva with the standard sva function. Here we show an example of how to perform supervised sva with the svaseq function. <>= sup_svseq = svaseq(dat0,mod1,mod0,controls=controls,n.sv=1)$sv plot(sup_svseq, svseq,pch=19,col="blue") @ Here we passed the controls argument, which is a vector of values between 0 and 1, representing the probability that a gene is affected by batch but not affected by the group variable. Since we have known negative control genes in this example, we simply set controls[i] = TRUE for all control genes and controls[i] = FALSE for all non-controls. \section{What to cite} The sva package includes multiple different methods created by different faculty and students. It would really help them out if you would cite their work when you use this software. To cite the overall sva package cite: \begin{itemize} \item Leek JT, Johnson WE, Parker HS, Jaffe AE, and Storey JD. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics DOI:10.1093/bioinformatics/bts034 \end{itemize} For sva please cite: \begin{itemize} \item Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences , 105: 18718-18723. \item Leek JT and Storey JD. (2007) Capturing heterogeneity in gene expression studies by `Surrogate Variable Analysis'. PLoS Genetics, 3: e161. \end{itemize} For combat please cite: \begin{itemize} \item Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8 (1), 118-127 \end{itemize} For mean-only or reference-batch combat please cite: \begin{itemize} \item Zhang, Y., Jenkins, D. F., Manimaran, S., Johnson, W. E. (2018). Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC bioinformatics, 19 (1), 262. \end{itemize} For svaseq please cite: \begin{itemize} \item Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD \end{itemize} For supervised sva please cite: \begin{itemize} \item Leek JT (2014) svaseq: removing batch and other artifacts from count-based sequencing data. bioRxiv doi: TBD \item Gagnon-Bartsch JA, Speed TP (2012) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13:539-52. \end{itemize} For fsva please cite: \begin{itemize} \item Parker HS, Bravo HC, Leek JT (2013) Removing batch effects for prediction problems with frozen surrogate variable analysis arXiv:1301.3947 \end{itemize} For psva please cite: \begin{itemize} \item Parker HS, Leek JT, Favorov AV, Considine M, Xia X, Chavan S, Chung CH, Fertig EJ (2014) Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction Bioinformatics doi: 10.1093/bioinformatics/btu375 \end{itemize} \bibliography{sva} \end{document} sva/vignettes/sva.bib0000644000175200017520000000543514710217751015660 0ustar00biocbuildbiocbuild@string{jasa="Journal of the American Statistical Association"} @string{jrssa="Journal of the Royal Statistical Society, Series A"} @string{jrssb="Journal of the Royal Statistical Society, Series B"} @Article{leek:storey:2007, Author = "Leek, J.T. and Storey, J.D.", Title = "Capturing Heterogeneity in Gene Expression Studies by `Surrogate Variable Analysis'", Journal = "PLoS Genetics 3:e161", Year = 2007, } @Article{leek:storey:2008, Author = "Leek, J.T. and Storey, J.D.", Title = "A general framework for multiple testing dependence", Journal = "Proceedings of the National Academy of Sciences 105:18718-18723", Year = 2008, } @Article{buja:eyuboglu:1992, Author = "Buja A. and Eyuboglu N.", Title = " Remarks on parrallel analysis", Journal = "Multivariate Behavioral Research, 27(4), 509-540", Year = 1992, } % 20838408 @Article{leek:2010aa, Author="Leek, J. T. and Scharpf, R. B. and Bravo, H. C. and Simcha, D. and Langmead, B. and Johnson, W. E. and Geman, D. and Baggerly, K. and Irizarry, R. A. ", Title="{{T}ackling the widespread and critical impact of batch effects in high-throughput data}", Journal="Nat. Rev. Genet.", Year="2010", Volume="11", Pages="733--739", Month="Oct" } @article{gagnon2012using, title={Using control genes to correct for unwanted variation in microarray data}, author={Gagnon-Bartsch, Johann A and Speed, Terence P}, journal={Biostatistics}, volume={13}, number={3}, pages={539--552}, year={2012}, publisher={Biometrika Trust} } % 15173019 @Article{dyrskjot:2004aa, Author="Dyrskjot, L. and Kruhoffer, M. and Thykjaer, T. and Marcussen, N. and Jensen, J. L. and Moller, K. and Orntoft, T. F. ", Title="{{G}ene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification}", Journal="Cancer Res.", Year="2004", Volume="64", Pages="4040--4048", Month="Jun" } % 20560929 @Article{leek:2011aa, Author="Leek, J. T. ", Title="{{A}symptotic conditional singular value decomposition for high-dimensional genomic data}", Journal="Biometrics", Year="2011", Volume="67", Pages="344--352", Month="Jun" } @ARTICLE{johnson:2007aa, author = {W.E. Johnson and C. Li and A. Rabinovic}, title = {Adjusting batch effects in microarray data using empirical Bayes methods}, journal = {Biostatistics}, year = {2007}, volume = {8}, pages = {118--127}, number = {1} } @article{zhang2018alternative, title={Alternative empirical Bayes models for adjusting for batch effects in genomic studies}, author={Zhang, Yuqing and Jenkins, David F and Manimaran, Solaiappan and Johnson, W Evan}, journal={BMC bioinformatics}, volume={19}, number={1}, pages={262}, year={2018}, publisher={BioMed Central} }