plotDEGHeatmap: Differentially Expressed Gene Heatmap
In WeiSong-bio/roryk-bcbioRNASeq: RNA-Seq Utilities

Description Usage Arguments Details Value Author(s) See Also Examples

This function is a simplified version of plotHeatmap() that is optimized for handling a DESeqResults object rather a gene vector. All of the optional parameters for plotHeatmap() are also available to this function.

plotDEGHeatmap(results, counts, ...)

## S4 method for signature 'DESeqResults,SummarizedExperiment'
plotDEGHeatmap(results, counts,
  alpha, lfcThreshold = 0L, title = TRUE, ...)

## S4 method for signature 'DESeqResults,DESeqDataSet'
plotDEGHeatmap(results, counts, ...)

## S4 method for signature 'DESeqResults,bcbioRNASeq'
plotDEGHeatmap(results, counts,
  normalized = c("rlog", "vst", "tmm", "tpm"), ...)

`results`	`DESeqResults` object.
`counts`	Object containing a normalized counts matrix.
`...`	Passthrough arguments to `plotHeatmap()`.
`alpha`	Adjusted P value ("alpha") cutoff.
`lfcThreshold`	Log fold change ratio (base 2) cutoff threshold.
`title`	Optional. Plot title.
`normalized`	Character indicating which normalization method to apply: "`tpm`": Transcripts per million (tximport). "`tmm`": edgeR trimmed mean of M-values. Calculated on the fly. "`rlog`": DESeq2 log2 regularized log transformation. "`vst`": DESeq2 log2 variance stabilizing transformation.

To adjust the annotation columns, modify the colData of the counts argument, which must contain a SummarizedExperiment (e.g. DESeqTransform, DESeqDataSet).

Show heatmap and invisibly return a list of the components.

Michael Steinbaugh

help("plotHeatmap", "bcbioBase").
findMethod("plotHeatmap", "SummarizedExperiment").

Other Differential Expression Functions: alphaSummary, contrastName, plotDEGPCA, plotMeanAverage, plotVolcano, resultsTables

# DESeqResults, SummarizedExperiment ====
plotDEGHeatmap(
    results = res_small,
    counts = rld_small
)

# DESeqResults, DESeqDataSet ====
# This always uses normalized counts
# Using default ggplot2 colors
plotDEGHeatmap(
    results = res_small,
    counts = dds_small,
    color = NULL,
    legendColor = NULL
)

# DESeqResults, bcbioRNASeq ====
plotDEGHeatmap(
    results = res_small,
    counts = bcb_small
)