Dear all,

I am using DESeq2 on 63 RNASeq samples.

The design used to explain the counts is based on the mutational status of 2 genes: ~GroupGene1+GroupGene2

I performed 3 PCA on this dataset:

1) on all the genes with rowSums(counts(dds)) > 10, that are in my case 36276 genes.

The PCA are similar between rlog, vsd and log2 transformation of the data

2&3) on the 2000 and 500 most variable genes, selected with ntop parameter from PCAplot().

Here the PCA are very different between rlog and vsd. With rlog, 2 samples are very large outliers, so the 61 other samples look pretty similar. With vsd, these 2 samples are not outliers.

Do it exist cases where rlog is not working ?

And have some of you already encountered this problem?

Thank you in advance

Thank you for your response Michael.

These 2 samples have not the lowest sizeFactor and there is no outlier among sizeFactors:

 summary(sizeFactors(dds))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
 0.5952  0.8673  1.0220  1.0290  1.1430  1.5610

I'd go with vst because it is faster but I don't know the rationale for choosing it over rld.

And for now, I have noticed nothing special about these samples (no bad RIN, no specific tumor cases, ...)