plotPCA {DESeq2} | R Documentation

## Sample PCA plot for transformed data

### Description

This plot helps to check for batch effects and the like.

### Usage

    ## S4 method for signature 'DESeqTransform'
    plotPCA(object, intgroup = "condition",
      ntop = 500, returnData = FALSE)

### Arguments

`object` | a `DESeqTransform` object, with data in `assay(x)`, produced for example by either `rlog` or `varianceStabilizingTransformation`.
`intgroup` | interesting groups: a character vector of names in `colData(x)` to use for grouping
`ntop` | number of top genes to use for principal components, selected by highest row variance
`returnData` | should the function only return the data.frame of PC1 and PC2 with intgroup covariates for custom plotting (default is FALSE)

### Value

An object created by `ggplot`, which can be assigned and further customized.

### Note

See the vignette for an example of variance stabilization and PCA plots. Note that the source code of `plotPCA` is very simple. The source can be found by typing `DESeq2:::plotPCA.DESeqTransform` or `getMethod("plotPCA","DESeqTransform")`, or browsed on github at https://github.com/Bioconductor-mirror/DESeq2/blob/master/R/plots.R Users should find it easy to customize this function.

### Author(s)

Wolfgang Huber

### Examples

    # using rlog transformed data:
    dds <- makeExampleDESeqDataSet(betaSD=1)
    rld <- rlog(dds)
    plotPCA(rld)

    # also possible to perform custom transformation:
    dds <- estimateSizeFactors(dds)
    # shifted log of normalized counts
    se <- SummarizedExperiment(log2(counts(dds, normalized=TRUE) + 1),
                               colData=colData(dds))
    # the call to DESeqTransform() is needed to
    # trigger our plotPCA method.
    plotPCA( DESeqTransform( se ) )

[Package DESeq2 version 1.22.2 Index]