Straight from plotPCA code:

dds <- rlog( dds, blind = T )
rv <- rowVars(assay(dds))
# select the ntop genes by variance
select <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))]
# perform a PCA on the data in assay(x) for the selected genes
pca <- prcomp(t(assay(dds)[select,]))

Now you can access more components:

PC1=pca$x[,1]
PC2=pca$x[,2]
PC3=pca$x[,3]

See the pcaPlot source for more ideas.

Here is my version of plotPCA

## Modified plotPCA from DESeq2 package. Shows the Names of the Samples (the first col of SampleTable), and uses ggrepel pkg to plot them conveniently.
# @SA 10.02.2017 
library(genefilter)
library(ggplot2)
library(ggrepel)

plotPCA.san <- function (object, intgroup = "condition", ntop = 500, returnData = FALSE) 
{
  rv <- rowVars(assay(object))
  select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, 
                                                     length(rv)))]
  pca <- prcomp(t(assay(object)[select, ]))
  percentVar <- pca$sdev^2/sum(pca$sdev^2)
  if (!all(intgroup %in% names(colData(object)))) {
    stop("the argument 'intgroup' should specify columns of colData(dds)")
  }
  intgroup.df <- as.data.frame(colData(object)[, intgroup, drop = FALSE])
  group <- if (length(intgroup) > 1) {
    factor(apply(intgroup.df, 1, paste, collapse = " : "))
  }
  else {
    colData(object)[[intgroup]]
  }

  ## Select the PCAs and percentVar that you like instead of 1 and 2
  d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], group = group, 
                  intgroup.df, name = colData(rld)[,1])
  if (returnData) {
    attr(d, "percentVar") <- percentVar[1:2]
    return(d)
  }
    ggplot(data = d, aes_string(x = "PC1", y = "PC2", color = "group", label = "name")) + geom_point(size = 3) + xlab(paste0("PC1: ", round(percentVar[1] * 100), "% variance")) + ylab(paste0("PC2: ", round(percentVar[2] * 100), "% variance")) + coord_fixed() + geom_text_repel(size=3) 

}

Select the PCs and percentVars you like by changing the argument below and also later in the ggplot (x="PC3" etc..)

  ## Select the PCAs and percentVar that you like instead of 1 and 2
  d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], group = group, 
                  intgroup.df, name = colData(rld)[,1])
  if (returnData) {
    attr(d, "percentVar") <- percentVar[1:2]
    return(d)
  }

Yet more possibilities via base R functions: A: PCA plot from read count matrix from RNA-Seq

DESeq2's PCA functionality automatically filters out a bunch of your transcripts based on low variance (biased / supervised). The code to which I have linked you does not (unbiased / unsupervised).

Kevin

May be you can store plotPCA output (with option returnData=TRUE) in an object and plot PCs independently with qplot.

Thanks, everyone. For those who are interested in plotting the PCA component 3, 4 and ... the following lines could help too.

library("DESeq2")
library(ggfortify)

project.pca <- prcomp(t(assay(dds)[select,]))

pdf("PCA34.pdf")

autoplot(prcomp(t(assay(dds)[select,])), x=3, y=4, data=sampleTable ,colour = 'genotype')

dev.off()

pdf("PCA56.pdf")

autoplot(prcomp(t(assay(dds)[select,])), x=5, y=6, data=sampleTable ,colour = 'genotype')

dev.off()

you can also add shape to the plot.

autoplot(prcomp(t(assay(dds)[select,])), data=sampleTable ,colour = 'genotype', shape='diet', size=3)

You can look at here for further discussion.

Have fun!