Heatmap for Differential Analysis Results.

DEHeatmap(
  deobj,
  deres,
  group.key = NULL,
  ref.group = NULL,
  group.color = c("blue", "red"),
  gene.df = NULL,
  label.key = NULL,
  signif = "padj",
  signif.threshold = 0.05,
  l2fc.threshold = 1,
  exp.range = c(-2, 2),
  exp.color = c("green", "black", "red"),
  heatmap.height = 20,
  heatmap.width = 20,
  col.gap = 2,
  legend.height = 5,
  link.height = 4
)

Arguments

deobj	Object created by DESeq2 or edgeR.
deres	Data frame contains all genes/peaks.
group.key	Sample group information. When set NULL, select first column of metadata. Default: NULL.
ref.group	Reference group name. When set NULL, select first element of groups. Default: NULL.
group.color	Color for different sample group. Default: blue for `ref.group`, and red for the other group.
gene.df	Gene data frame, at least contains Gene column. Default: NULL. When provided, the second column should not be in `deres`.
label.key	Which column to use as label. Default: NULL. When set NULL, use rownames of `deres` or Gene column of `gene.df`.
signif	Significance criterion. For DESeq2 results, can be chosen from padj, pvalue. For edgeR results, can be chosen from FDR, PValue. Default: padj.
signif.threshold	Significance threshold to get differentially expressed genes or accessible/binding peaks. Default: 0.05.
l2fc.threshold	Log2 fold change threshold to get differentially expressed genes or accessible/binding peaks. Default: 1.
exp.range	Z-score range to plot. Default: c(-2,2).
exp.color	Color map used for heatmap. Default: c("green","black","red").
heatmap.height	The height of whole heatmap. Default: 20cm.
heatmap.width	The width of whole heatmap. Default: 20cm.
col.gap	Gap between column slices. Default: 2mm.
legend.height	The height of legend. Default: 5cm.
link.height	The height of the segments. Default: 4mm.

Value

A Heatmap-class.

Examples

library(DESeq2)
library(DEbPeak)
count.file <- system.file("extdata", "snon_count.txt", package = "DEbPeak")
meta.file <- system.file("extdata", "snon_meta.txt", package = "DEbPeak")
count.matrix <- read.table(file = count.file, header = TRUE, sep = "\t")
meta.info <- read.table(file = meta.file, header = TRUE)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = count.matrix, colData = meta.info, design = ~condition)
#> Warning: some variables in design formula are characters, converting to factors
keep.genes <- rowSums(DESeq2::counts(dds, normalized = FALSE)) >= 10
dds <- dds[keep.genes, ]
dds$condition <- relevel(dds$condition, ref = "WT")
dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
dds.results <- results(dds, contrast = c("condition", "KO", "WT"))
dds.results.ordered <- dds.results[order(dds.results$log2FoldChange, decreasing = TRUE), ]
DEHeatmap(deobj = dds, deres = dds.results.ordered, group.key = "condition", ref.group = "WT", signif = "pvalue", l2fc.threshold = 0.3)
#> Differential expression analysis with DESeq2!