mostSignificantHeat: Plot most significant genes: heatmap

In perllb/deseqAbstraction: A set of functions to make DESeq pipes more smooth

Plot most significant genes: heatmap

Description

Plots heatmap showing the expression of most variable genes

Usage

mostSignificantHeat(data, test, ntop = 50, a1 = NULL, a2 = NULL,
  n1 = NULL, n2 = NULL)

Arguments

data:	normalized deseq matrix data (with varianceStablizing, rlog etc - eg. assay(vsd)
test:	output from results(dds)
ntop:	how many genes to display (the <ntop> most variable genes)
a1:	annotation of the samples
a2:	annotation of the samples
n1:	name of annotation in a1
n2:	name of annotation in a2

Examples

vst <- varianceStabilizingTransformation(dds)
test <- results(dds)
mostSignificantHeat(data = assay(vst),test=test,ntop = 100, a1 = colData$cellLine, a2 = colData$treatment,n1="Cell Line",n2="Treatment")

perllb/deseqAbstraction documentation built on Oct. 31, 2023, 2:13 a.m.