Hi Simon, 2 questions: (1) I am working with a multi-factor experiment that, in a sense, doesn't have biological replicates (replicates were done at different times, and the differences in the chemistry/instrumentation is enough to be a rather large component of variance) and has three levels. By three levels, I mean there are 2 different treatments and 1 control. When I push the workflow through, everything works fine, but I'm having a hard time delineating which level is responsible for a gene being identified as significantly differentially expressed. I understand that (in the following example) ConditionX, ConditionY, AlternativeVariableold are log2 fold changes in relation to "ctrl" (in the case of condition) or "new" (in the case of AlternativeVariable). Can I assume that the largest FC of these three fields within each gene instance is likely the reason that gene had a padj within significance? My plan is to separate out the dataset so that I'm only running 2 levels at once (x vs ctrl; y vs ctrl), but I'm hoping you can shed light on how I can interpret these results I've mentioned here. The output I'm referring to has the following headers (generated by the last line (write.table) in my workflow below): Gene Pval Padj Intercept ConditionX ConditionY AlternativeVariableold Deviance Converged My workflow: counts <- (file, header=TRUE, row.names=1) Design<- data.frame( + row.names = colnames(SampledCounts), + Condition = c( "x", "ctrl", "ctrl", "x", "y", "y"), + AlterativeVariable= c( "old", "new", "old", "new", "new", "old")) library(DESeq) cdsFull <- newCountDataSet (counts, Design) cdsFull <-estimateSizeFactors(cdsFull) cdsFull <-estimateDispersions(cdsFull, method="blind", sharingMode="fit-only") fit1<-fitNbinomGLMs(cdsFull, count ~ condition + AlternativeVariable) fit0<-fitNbinomGLMs(cdsFull, count ~ AlternativeVariable) pvalsGLM <-nbinomGLMTest(fit1, fit0) padjGLM <-p.adjust(pvalsGLM, method = "BH") write.table(data.frame(geneID=row.names(counts(cdsFull)), pval=pvalsGLM, padj=padjGLM, fit1), file= "result.txt") (2)nd question: Also, a followup question (I've since done the pairwise comparisons w/ the mult-factor design); is there a 'baseMean' stored anywhere within the multi-factor experiment workflow? I'd like to mock up MvA plots, but can't seem to find a value for BaseMean for each gene. Thanks for your help, Ingrid