Hi All,

I'm trying to draw scaled PCA plot for my sequencing data while using DESeq2. The code, I am using is:

ds <- DESeqDataSetFromMatrix(countData=counts, colData=samples, design=~condition) 
ds$condition <- relevel(ds$condition, "Ctrl")
ds <- ds[rowSums(counts(ds)>1) >= 2, ] #PCA plot

# transformation
rld_ds <- rlog(ds, blind=FALSE) #regularized log-transformation

#PCA plot1
plotPCA(rld_ds, intgroup = c("SampleName"))

# PCA plot 2 using ggplot2
(pcadata <- plotPCA(rld_ds, intgroup = c( "condition", "SampleName"), returnData=TRUE))
(percentVar <- round(100 * attr(pcadata, "percentVar"))

ggplot(pcadata, aes(PC1, PC2, color=condition, label=SampleName)) +
  geom_point(size=3) +
  geom_text(aes(label=SampleName),hjust=.5, vjust=-1, size=3) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) + 
  ylab(paste0("PC2: ",percentVar[2],"% variance")) #plot 2

Now, my question:

Are these PCA plots already scaled due to rld transformation? if not, how can I do that?

Thanks,

Sofia

You can take a look at the source for plotPCA by fetching it like so:

library(DESeq2)
getMethod("plotPCA", "DESeqTransform")

You will find in there the call to prcomp that does the PCA, namely:

pca <- prcomp(t(assay(object)[select, ]))

and a call to ?prcomp will show you the default values for the center and scale. paramenters (TRUE, and FALSE, respecitvely).

If you want to change the behavior, you can make your own pcaPlot function by copying the the code of that function into your own and tweaking accordingly.