Principal component analysis plot

plotPCA(object, ...)

# S4 method for DESeqAnalysis
plotPCA(object, ...)

# S4 method for DESeqDataSet
plotPCA(object, ...)

# S4 method for DESeqTransform
plotPCA(object, ...)

Arguments

object

Object.

...

Additional arguments.

Value

ggplot.

Details

Passes to SummarizedExperiment defined in AcidPlots package.

Functions

  • plotPCA,DESeqAnalysis-method: Extracts DESeqTransform and passes to corresponding method.

  • plotPCA,DESeqDataSet-method: Method intentionally errors. Use DESeqAnalysis or DESeqTransform methods instead.

  • plotPCA,DESeqTransform-method: Passes to SummarizedExperiment method defined in AcidPlots package. Uses values defined in assay().

Note

Updated 2021-03-15.

Principal component analysis

PCA (Jolliffe, et al., 2002) is a multivariate technique that allows us to summarize the systematic patterns of variations in the data. PCA takes the expression levels for genes and transforms it in principal component space, reducing each sample into one point. Thereby, we can separate samples by expression variation, and identify potential sample outliers. The PCA plot is a way to look at how samples are clustering.

SingleCellExperiment

The SingleCellExperiment method that visualizes dimension reduction data slotted in reducedDims() is defined in pointillism package.

References

Jolliffe, et al., 2002.

See also

Examples

data(deseq) ## DESeqAnalysis ==== plotPCA(deseq)