PlotHtmp: Produce ComplexHeatmap from count data.

In ATpoint/misterplotR: Functions for plotting and clustering of count data

Description

This minimal function accepts a count matrix (columns=samples, rows=regions/genes) and then uses ComplexHeatmap::Heatmap() to produce plot.

Usage

PlotHtmp(
  InputData,
  ScaleByQuantile = NULL,
  Htmp.colors = c("blue", "black", "red"),
  Htmp.title = NULL,
  Htmp.titlesize = 25,
  Htmp.legendname = NULL,
  Htmp.legendpos = "left",
  Htmp.colnamesize = 1.5,
  Htmp.columnorder = NULL,
  Htmp.colorsplit = NULL,
  Htmp.return = FALSE,
  Htmp.hclustRow = NULL,
  Htmp.nclusters = NULL
)

Arguments

InputData	a matrix or dataframe with the count data to be plotted
ScaleByQuantile	a numeric vector with two elements that define the lower and upper quantile which will be used to winsorize the data in order to limit infuence of outliers on the plot.
Htmp.colors	three colors to be used for color graient
Htmp.title	Main title
Htmp.titlesize	Main title font size
Htmp.legendname	the name of the legend
Htmp.legendpos	legend position
Htmp.colnamesize	font size of the colnames
Htmp.columnorder	a numeric vector to switch column order, see details
Htmp.colorsplit	a numeric vector to define gaps in column groups, see details
Htmp.return	logical, whether to return heatmap to be saved as variable
Htmp.hclustRow	a hclust object to cluster the rows
Htmp.nclusters	number of clusters to extract based on hclust object (basically the k parameter from cutree)

Details

(tba...)

Author(s)

Alexander Toenges

Examples

cts <- sapply(seq(1,10), function(x) rnorm(1000,100))
colnames(cts) <- paste0("sample", seq(1,10))
Htmp <- PlotHtmp(InputData = cts, Htmp.return = TRUE, 
                 Htmp.legendname = "dummy counts", Htmp.title = "A title",
                 Htmp.nclusters = 3, Htmp.hclustRow = hclust(dist(cts)))
Htmp

ATpoint/misterplotR documentation built on Feb. 15, 2020, 12:17 a.m.