Hi, 

   I used to plot PCA  using DESeq2, and it works great.  In DESeq2, the normalized counts are transformed through vst(variance stabilized transformation, based on the NB variance ~ expectation relationships?) function, rather than the direct log transformed counts.  

  I tried limma-voom in my new datasets, which has more than 500 samples with various factors(sex, Batches, treatments, genotypes, etc).  Then I used plotMDS function:

plotMDS(lcpm[,subsamples], top=500, 
        col=df_annotation$col[subsamples], 
        labels=NULL, dim = c(1,2))

plotMDS with dim=c(1,2) ,c(1,3), c(1,4) , c(2,3), c(2,4),or c(3,4) showed no obvious separations by those known factors.  

   I noticed that plotMDS uses the log transformed TMM normalized counts directly.  As I could also get the mean-variance relationships from efit, why there is no such vst transformed expression data for the PCoA plot? 

  In the PlotMDS plot, if I chose gene.selection = "common", was the output identical as PCA plot with the same log transformed datasets?  To my understanding, if Euclidean distance were applied, PCA and PCoA are identical, are they?

Thanks & regards,

Raymond

Yes, gene.selection="common" will make the MDS distance equivalent to PCA.

To stabilize the variances for the MDS plot, use cpm() with prior.count=5.