dotplot.DE: Differential expression plot to identify populations within...

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

Differential expression plot to identify populations within clusters

Description

It draws dotplot for each cell cluster identified and each marker to facilitate identification of cell populations in each cell cluster.

Usage

dotplot.DE(
  fcs.SCE,
  assay.i = "normalized",
  clusters.named = "SOM_named",
  markers.to.use = "all",
  psig.cutoff = 0.05,
  return.stats = F,
  scale.size = 9
)

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".
clusters.named	Column name from the initial.fcs.SCE object which contains renamed clusters (through FlowCT::clusters.rename()).
markers.to.use	Vector with markers to use. Default = "all".
psig.cutoff	P-value cutoff. Default = 0.05.
return.stats	Logical indicating if calculated statistics should be returned in a new variable. Default = FALSE.
scale.size	Numerical value indicating how much scale points. Default = 9.

Examples

## Not run: 
dotplot.DE(fcs.SCE = fcs, clusters.named = "SOM_named", 
   markers.to.use = c("CD8", "CD27", "CCR4", "CD45RA", "CD4"))

## End(Not run)

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