plotPCA {DESeq2} | R Documentation |
This plot helps to check for batch effects and the like.
plotPCA(x, intgroup = "condition", ntop = 500, col)
x |
a SummarizedExperiment, with data in
|
intgroup |
a character vector of names in
|
ntop |
number of top genes to use for principal components, selected by highest row variance |
col |
a vector of colors for each level of intgroup |
A trellis
object.
See the vignette for an example of variance stabilization and PCA plots.
Wolfgang Huber
dds = makeExampleDESeqDataSet(betaSD=1) vsd = varianceStabilizingTransformation(dds) p = plotPCA(vsd) print(p) ## Add text labels (for presentation graphics, consider additional ## layout operations that avoid overplotting, such as the FField package on CRAN) names = colData(vsd)$sample p = update(p, panel = function(x, y, ...) { lattice::panel.xyplot(x, y, ...); lattice::ltext(x=x, y=y, labels=names, pos=1, offset=1, cex=0.8) }) print(p)