Hello community, I'm using DESeq and edgeR to conduct RNA-Seq data analysis. I want to get log2FC adjusted for (possible) lane effects. For example, in edgeR I use: design = model.matrix(~ lane + condition, data=tmp) de = DGEList(counts, group=tmp$condition) de = calcNormFactors(de) de = estimateGLMCommonDisp(de, design) de = estimateGLMTrendedDisp(de, design) de = estimateGLMTagwiseDisp(de, design) fit = glmFit(de, design) lrt = glmLRT(fit, coef='conditiontreated') while in DESeq2, I have: rawData <- DESeqDataSetFromMatrix(counts, pd, ~ lane + condition) dds <- DESeq(rawData, test='LRT', reduced= ~ lane) >From the documentation, this seems the right way of getting the DE genes when I want to account for the lane effect. Is this correct? Or would be something like DESeq(rawData) sufficient? (and of course getting the results for the proper coefficient) Does the same apply for edgeR? -- output of sessionInfo(): R version 3.0.3 Patched (2014-03-06 r65200) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] parallel splines stats graphics grDevices utils datasets [8] methods base other attached packages: [1] DESeq2_1.2.10 RcppArmadillo_0.4.200.0 Rcpp_0.11.1 [4] GenomicRanges_1.14.4 XVector_0.2.0 IRanges_1.20.7 [7] BiocGenerics_0.8.0 edgeR_3.4.2 limma_3.18.13 loaded via a namespace (and not attached): [1] annotate_1.40.1 AnnotationDbi_1.24.0 Biobase_2.22.0 [4] DBI_0.2-7 genefilter_1.44.0 grid_3.0.3 [7] lattice_0.20-29 locfit_1.5-9.1 RColorBrewer_1.0-5 [10] RSQLite_0.11.4 stats4_3.0.3 survival_2.37-7 [13] XML_3.95-0.2 xtable_1.7-3 -- Sent via the guest posting facility at bioconductor.org.

hi Rafael, On Tue, Apr 15, 2014 at 9:50 AM, Rafael Moreira [guest] <guest at="" bioconductor.org=""> wrote: > > > while in DESeq2, I have: > rawData <- DESeqDataSetFromMatrix(counts, pd, ~ lane + condition) > dds <- DESeq(rawData, test='LRT', reduced= ~ lane) > > >From the documentation, this seems the right way of getting the DE genes when I want to account for the lane effect. Is this correct? Or would be something like > > DESeq(rawData) > > sufficient? (and of course getting the results for the proper coefficient) These are both correct for DESeq2. The first formulation performs the likelihood ratio test (by default not shrinking LFCs and the second performs the Wald test by default shrinking LFCs). See ?DESeq for more information. Either way results() will give you the log2 fold change for condition. Mike

> Date: Tue, 15 Apr 2014 06:50:34 -0700 (PDT) > From: "Rafael Moreira [guest]" <guest at="" bioconductor.org=""> > To: bioconductor at r-project.org, rafaelmoreira at gmail.com > Subject: [BioC] Which log2FC to report? > > > Hello community, I'm using DESeq and edgeR to conduct RNA-Seq data analysis. > I want to get log2FC adjusted for (possible) lane effects. For example, in edgeR I use: > > design = model.matrix(~ lane + condition, data=tmp) > de = DGEList(counts, group=tmp$condition) > de = calcNormFactors(de) > de = estimateGLMCommonDisp(de, design) > de = estimateGLMTrendedDisp(de, design) > de = estimateGLMTagwiseDisp(de, design) > fit = glmFit(de, design) > lrt = glmLRT(fit, coef='conditiontreated') Yes, this is the right way to test for DE genes for condition adjusted for lane effects. It is also the right way to get the corresponding log-fold-changes, although for that purpose the glmLRT() step is not needed. Note that edgeR gives shrunk logFC, and the amount of shrinkage is controlled by the prior.count argument to glmFit(). The default is to do a minimal but useful amount of shrinkage, which is almost always preferable to raw fold changes. The edgeR User's guide includes two fully worked case studies that are very closely analogous to your experiment. In the oral carcinoma case study (Section 4.4), the patients are equivalent to the lane effects in your experiment. In the arabdopsis case study (Section 4.5), the batch effects are equivalent to the lane effects in your experiment. > while in DESeq2, I have: > rawData <- DESeqDataSetFromMatrix(counts, pd, ~ lane + condition) > dds <- DESeq(rawData, test='LRT', reduced= ~ lane) > >> From the documentation, this seems the right way of getting the DE genes when I want to account for the lane effect. Is this correct? Or would be something like > > DESeq(rawData) > > sufficient? (and of course getting the results for the proper coefficient) > Does the same apply for edgeR? Does what apply to edgeR? Best wishes Gordon > -- output of sessionInfo(): > > R version 3.0.3 Patched (2014-03-06 r65200) > Platform: x86_64-apple-darwin10.8.0 (64-bit) > > locale: > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] parallel splines stats graphics grDevices utils datasets > [8] methods base > > other attached packages: > [1] DESeq2_1.2.10 RcppArmadillo_0.4.200.0 Rcpp_0.11.1 > [4] GenomicRanges_1.14.4 XVector_0.2.0 IRanges_1.20.7 > [7] BiocGenerics_0.8.0 edgeR_3.4.2 limma_3.18.13 > > loaded via a namespace (and not attached): > [1] annotate_1.40.1 AnnotationDbi_1.24.0 Biobase_2.22.0 > [4] DBI_0.2-7 genefilter_1.44.0 grid_3.0.3 > [7] lattice_0.20-29 locfit_1.5-9.1 RColorBrewer_1.0-5 > [10] RSQLite_0.11.4 stats4_3.0.3 survival_2.37-7 > [13] XML_3.95-0.2 xtable_1.7-3 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}