Plot differentially expressed gene principal component analysis

This function is an extension of plotPCA() that is optimized for automatic handling of differentially expressed genes, rather than requiring manual input of a gene vector or subset object.

Usage

plotDegPca(object, ...)

# S4 method for DESeqAnalysis
plotDegPca(
  object,
  i,
  contrastSamples = FALSE,
  alphaThreshold = NULL,
  baseMeanThreshold = NULL,
  lfcThreshold = NULL,
  ...
)

# S4 method for DESeqResults
plotDegPca(
  object,
  DESeqTransform,
  direction = c("both", "up", "down"),
  alphaThreshold = NULL,
  baseMeanThreshold = NULL,
  lfcThreshold = NULL,
  ...
)

Arguments

object

Object.

i

Indices specifying elements to extract or replace. Indices are numeric or character vectors, empty (missing), or NULL.

For more information:

help(topic = "Extract", package = "base")

contrastSamples

logical(1). Only include the samples used to define the contrast passed to DESeq2::results(). This setting will break for complex DESeq2 contrasts (e.g. interaction effect).

alphaThreshold

numeric(1) or NULL. Adjusted P value ("alpha") cutoff. If left NULL, will use the cutoff defined in the object.

baseMeanThreshold

numeric(1) or NULL. Base mean (i.e. average expression across all samples) threshold. If left NULL, will use the cutoff defined in the object. Applies in general to DESeq2 RNA-seq differential expression output.

lfcThreshold

numeric(1) or NULL. Log (base 2) fold change ratio cutoff threshold. If left NULL, will use the cutoff defined in the object.

...

Additional arguments.

DESeqTransform

DESeqTransform.

direction

character(1). Include "both", "up", or "down" directions.

Value

Plot.

Details

To adjust the annotation columns, modify the colData() of the counts argument, which must contain/extend a SummarizedExperiment.

Functions

plotDegPca(DESeqAnalysis): Passes to DESeqResults method.

Note

Updated 2021-08-02.

Examples

data(deseq)

## DESeqAnalysis ====
plotDegPca(deseq, i = 1L)