sc.heatmap: Heatmap at event-level

In jgarces02/FlowCT: A semi-automated workflow for deconvolution of immunophenotypic data and objective reporting on large datasets

Heatmap at event-level

Description

This function draws a heatmap with single-cell fluorescence values.

Usage

sc.heatmap(
  fcs.SCE,
  assay.i = "normalized",
  markers.to.use = "all",
  not.metadata = c("filename", "cell_id", "sample_id"),
  clustering.method = "average",
  subsampling = 100,
  color.expression = NULL,
  colors = NULL
)

Arguments

fcs.SCE	A fcs.SCE object generated through FlowCT::fcs.SCE().
assay.i	Name of matrix stored in the fcs.SCE object from which calculate correlation. Default = "normalized".
markers.to.use	Vector with markers to use. Default = "all".
not.metadata	Vector with variable names (from colData(fcs.SCE)) for not including in the heatmap annotation. Default = c("filename", "cell_id", "sample_id").
clustering.method	Clustering method for rows and columns clustering within the heatmap. Possible values are "average" (default), "ward.D", "ward.D2", "single", "complete", "mcquitty", "median" or "centroid".
subsampling	Numeric value indicating how many events use to draw heatmap and speed up plotting. Default = 100.
color.expression	Color vector for coloring expression values within the heatmap. Default = NULL (i.e., scale "YlGnBu" from RColorBrewer).
colors	Vector with colors for plotting (if provided, it must be as long as the number of unique elements in the longer metadata field). Default = NULL (i.e., it will choose automatically a vector of colors according to FlowCT::div.colors()).

Examples

## Not run: 
sc.heatmap(fcs, subsampling = 100)

## End(Not run)

jgarces02/FlowCT documentation built on March 28, 2023, 12:42 p.m.