i met this problem, i got two batch of data. but the expression values looks more similar within batch than within groups. then i checked the data, i found the first batch of data contain much contamination of bateria. by the way, if we remove the batch effect. it will remove the some biological variation? do we need remove them or not? On Wed, Jun 5, 2013 at 8:54 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote: > Hi David, > > I think you're right that this is too good to be true. > > First, cpm values are grossly non-normal. If you wish to use a method > designed for microarrays, you need to convert to the log-scale: > > data = cpm(dge, log=TRUE, prior.count=2) > > See Section 2.10 of the edgeR User's Guide. > > Second, as Evan has said in a separate email, your sample sizes are too > small for ComBat to be reliable. You have only 8 samples total, and only > two samples in each batch. ComBat isn't designed to work in this range. > > So adjusting for batch in the glm model is the way to go. > > Best wishes > Gordon > > PS. We didn't get your attached file, presumably because it is over the > size limit for the mailing list. > > Date: Tue, 4 Jun 2013 15:40:20 -0400 >> From: "David O'Brien" <dobrie10@jhmi.edu> >> To: bioconductor@r-project.org >> Subject: [BioC] Remove batch effects from RNA-seq data using edgeR and >> sva/ComBat >> >> I'm trying to analyze an RNA-seq experiment where the PCA plot shows >> better >> clustering by the day the experiment was done rather than treatment type. >> Using edgeR to determine differentially expressed genes resulted in less >> than 5 genes with an FDR under 5%. Creating a GLM model to remove batch >> effects for day of experiment as stated in the edgeR manual resulted in 42 >> genes with an FDR less than 5%. An improvement, but still not good. So I >> tried using ComBat and the result was 986 genes with an FDR under 5%. >> Looking at the GO enrichment, the differentially expressed genes seem to >> make sense, but since ComBat was developed for microarrays, I'm concerned >> that there may be some caveats with this approach that I'm missing. >> Looking >> at the top genes below, the log2 fold change is really low and generally >> this just seems too good to be true. So my question is: Are there any >> reasons why using ComBat with RNA-seq data is not legit? And if so, can >> you >> see any problems with the approach below? >> >> mean_control mean_treatment logFC pval padj >> Gene5727 51.224797 45.371919 -0.1750427 3.474361e-08 0.0003224554 >> Gene3059 8.998311 5.740828 -0.6483954 1.056473e-07 0.0003268376 >> Gene11899 35.044302 39.027842 0.1553238 7.398559e-08 0.0003268376 >> Gene11724 2.556712 3.684178 0.5270535 1.959058e-07 0.0003636404 >> Gene12218 30.852989 23.702209 -0.3803888 1.908726e-07 0.0003636404 >> >> Gene4952 26.122068 30.466346 0.2219474 3.346424e-07 0.0005176360 >> >> >> My code is below. I've attached a file, dge.Rdata, that contains the >> counts info that is output from readDGE, so you can have the initial >> counts info. >> >> >> require(edgeR) >> require(sva) >> source('code/annotate_edgeR.R'**) >> files = data.frame(files=c('counts.**control0', 'counts.control1', >> 'counts.control2', 'counts.control3', 'counts.treatment0', >> 'counts.treatment1', 'counts.treatment2', 'counts.treatment3'), >> group=c('control', 'control', 'control', 'control', >> 'treatment', 'treatment', 'treatment', 'treatment'), >> day=rep(0:3,2) >> ) >> labels <- paste0(files$group, files$day) >> dge <- readDGE(files=files, path='data/HTSeq/', labels=labels) >> rownames(dge$counts) <- paste0('Gene', 1:nrow(dge$counts)) #Change gene >> names to anonymize data >> ##############################**## >> # save(dge, file='objs/dge.Rdata') >> # SEE ATTACHED FILE # >> ##############################**# >> >> ## filter out the no_feature etc. rows >> dge <- dge[1:(nrow(dge)-5), ] >> ## This mitochondrial rRNA gene takes up a massive portion of my >> libraries >> dge <- dge[!rownames(dge)%in%'**Gene13515', ] >> ## filter out lowly expressed genes >> keep <- rowSums(cpm(dge) > 1) >= 3 ## gene has at least 3 columns where >> cpm >> is > 1 >> dge <- dge[keep, ] >> ## Recompute library sizes >> dge$samples$lib.size <- colSums(dge$counts) >> ## Normalize for lib size >> dge <- calcNormFactors(dge) >> >> ## ComBat >> mod <- model.matrix(~as.factor(group)**, data=dge$sample) >> mod0 <- model.matrix(~1, data=dge$sample) >> batch <- dge$sample$day >> >> combat <- ComBat(dat=cpm(dge), batch=batch, mod=mod) >> pval_combat = f.pvalue(combat, mod, mod0) >> padj_combat = p.adjust(pval_combat, method="BH") >> mean_control <- rowMeans(combat[, 1:4]) >> mean_treatment <- rowMeans(combat[, 5:8]) >> logFC <- log2(mean_treatment/mean_**control) >> >> res <- data.frame(mean_control, mean_treatment, logFC, pval=pval_combat, >> padj=padj_combat) >> res <- res[order(res$padj), ] >> >> R version 3.0.1 (2013-05-16) >> Platform: x86_64-pc-linux-gnu (64-bit) >> >> locale: >> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C >> LC_TIME=en_US.UTF-8 >> [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 >> LC_MESSAGES=en_US.UTF-8 >> [7] LC_PAPER=C LC_NAME=C >> LC_ADDRESS=C >> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 >> LC_IDENTIFICATION=C >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> >> other attached packages: >> [1] biomaRt_2.16.0 sva_3.6.0 mgcv_1.7-23 corpcor_1.6.6 >> edgeR_3.2.3 limma_3.16.4 >> >> loaded via a namespace (and not attached): >> [1] grid_3.0.1 lattice_0.20-15 Matrix_1.0-12 nlme_3.1-109 >> RCurl_1.95-4.1 tools_3.0.1 >> [7] XML_3.96-1.1 >> >> ------------------------------ >> >> > ______________________________**______________________________**____ ______ > The information in this email is confidential and inte...{{dropped:25}}