plotPCA-methods {EDASeq} | R Documentation

## Methods for Function `plotPCA` in Package EDASeq

### Description

`plotPCA` produces a Principal Component Analysis (PCA) plot of the counts in `object`

### Usage

    ## S4 method for signature 'matrix'
    plotPCA(object, k=2, labels=TRUE, isLog=FALSE, ...)
    ## S4 method for signature 'SeqExpressionSet'
    plotPCA(object, k=2, labels=TRUE, ...)

### Arguments

`object` | Either a numeric matrix or a `SeqExpressionSet` object containing the gene expression.
`k` | The number of principal components to be plotted.
`labels` | Logical. If `TRUE`, and `k=2`, it plots the `colnames` of `object` as point labels.
`isLog` | Logical. Set to `TRUE` if the data are already on the log scale.
`...` | See `par`

### Details

The Principal Component Analysis (PCA) plot is a useful diagnostic plot to highlight differences in the distribution of replicate samples, by projecting the samples into a lower dimensional space.

If there is strong differential expression between two classes, one expects the samples to cluster by class in the first few Principal Components (PCs) (usually 2 or 3 components are enough). This plot also highlights possible batch effects and/or outlying samples.

### Methods

`signature(x = "matrix")`
`signature(x = "SeqExpressionSet")`

### Examples

    library(yeastRNASeq)
    data(geneLevelData)

    mat <- as.matrix(geneLevelData)

    data <- newSeqExpressionSet(mat,
                                phenoData=AnnotatedDataFrame(
                                          data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
                                                     row.names=colnames(geneLevelData))))

    plotPCA(data, col=rep(1:2, each=2))

[Package EDASeq version 2.22.0 Index]