Hello, 

I already found out how to select specific comparisons from the object resulting from the DESeq() function

dds <- DESeq(ddsHTSeq)

using the 'contrast' variable in the 'results' function like this:

deseq2.res <- results(dds, contrast=c("condition","treated_A","untreated_A"))

With this 'deseq2.res' object, I can now do my further analyses, also including MA plot, ...

But now, lets say I also want to plot a PCA only for this specific comparison between 'treated_A' and 'untreated_A' using the rld transformed values:

rld <- rlog(dds)
plotPCA(rld, intgroup=c("condition", "type"), returnData=TRUE)

Is there a way, to build from the full 'dds' object with all conditions and values a subset only including those for the comparisons 'treated_A' and 'untreated_A'? 

It would be nice, to have such an object to continue with downstream analyses like PCA, heatmaps, etc... but still have the normalization and estimations over all samples and conditions from the full 'dds' object.

Is there such a way?

Thanks!

Martin

Sure:

rld.sub <- rld[ , rld$condition %in% c("treated_A", "untreated_A") ]
​plotPCA(rld.sub, "condition")

rld.sub<-rld[,rld@colData@listData$condition %in% c("treated_A","untreated_A")]

You could find the detailed rld information by str(rld) and then find the corresponding condition location for subsetting.

Hope this helps!