After browsing through many of the posts on biostars, seqanswers etc, I still am a bit shaky on how best to handle multiple treatments with DESeq2. I understand that the analysis takes the condition into account during the creation of the deseqDataSet object via the design argument, but how is that handled by the DESeq function?
For instance, when using the plotMA function following this guide, which conditions are being plotted against the control by default? I get a nice plot, but which contrast is being plotted?
Below is how I am going about constructing the deseqDataSet object":
countsTable <-data.matrix(df[0:2301,1:10]) samples<-names(df[1:10]) condition<-c(rep("ctrl",2),rep("A",2),rep("B",2),rep("C",2),rep("D",2)) pData = data.frame(cbind(samples, condition)) ddsfm <- DESeqDataSetFromMatrix(countData = countsTable, colData=pData, design=~condition)
I had assumed that a way to index out the individual contrasts would be to use the following results functions:
aRes<-results(dds, contrast=c("condition", "A", "ctrl")) bRes<-results(dds, contrast=c("condition", "B", "ctrl")) cRes<-results(dds, contrast=c("condition", "C", "ctrl")) dRes<-results(dds, contrast=c("condition", "D", "ctrl"))
This doesn't seem to be returning the contrasts that I was anticipating. Is there a more idiomatic way to approach this process?
> sessionInfo() R version 3.1.2 (2014-10-31) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale:  en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages:  parallel stats4 stats graphics grDevices utils datasets methods  base other attached packages:  plyr_1.8.1 Biobase_2.26.0 DESeq2_1.6.3  RcppArmadillo_0.4.600.0 Rcpp_0.11.3 GenomicRanges_1.18.4  GenomeInfoDb_1.2.4 IRanges_2.0.1 S4Vectors_0.4.0  BiocGenerics_0.12.1 ggplot2_1.0.0