I have implemented a linear model to fit RNAseq read counts for ~1000 samples using ~200 covariates plus the intercept. The pipeline follows the example in the user manual, i.e. v=voom(y,design) -> fit=lmFit(v,design) -> fit2=eBayes(fit). Then I am using decideTests() to assess the effect of each covariate on gene expression. However, the output is less complete than topTable(), so I used this function to obtain values on fold-changes and individual p-values. At this stage, values become difficult to interpret. If the LM is: gene_i,j = intercept_i + alfa_i*Factor1_j + beta_i*Factor2_j + (~200 other factors) and, to simplify, let's say covariates are binary. Then what do the fold-changes for topTable(fit2,coef="alfa") mean? My interpretation was the up/down-regulation due to the presence of covariate alfa, but if I boxplot or summarize the expression values for the two groups (alfa = 1 vs alfa = 0) I get quite contradictory results. For example this is the most DE gene when the sample has a mutation in KRAS (the covariate, 0 if wild-type, 1 if mutated): > topTable(fit2,coef="metadataKRAS",number=1) hgnc_symbol logFC AveExpr t P.Value adj.P.Val B GOLGA6A -1.314213 -5.9000494 -7.375175 3.800569e-13 7.204358e-09 18.80961 The summary of gene expression values for wild-type KRAS vs. mutated is: > by(E["GOLGA6A",],design[,"metadataKRAS"],summary) design[, "metadataKRAS"]: 0 Min. 1st Qu. Median Mean 3rd Qu. Max. -7.882 -6.865 -6.461 -6.021 -5.770 1.548 ---------------------------------------------------------------- design[, "metadataKRAS"]: 1 Min. 1st Qu. Median Mean 3rd Qu. Max. -7.657 -6.247 -5.487 -5.239 -4.582 -1.311 This is an up-regulation, and of magnitude way lower than the one indicated by topTable. Is this because I am misusing the function topTable? I cannot wrap my head around this. Thanks for your help! -- output of sessionInfo(): R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] sv_SE.UTF-8/sv_SE.UTF-8/sv_SE.UTF-8/C/sv_SE.UTF-8/sv_SE.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] edgeR_3.4.2 limma_3.18.9 loaded via a namespace (and not attached): [1] tools_3.0.2 -- Sent via the guest posting facility at bioconductor.org.