parallel.plot: Longitudinal differential dotplot

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

Longitudinal differential dotplot

Description

It draws a differential dot plot (longitudinaly) according condition for each cell cluster identified.

Usage

parallel.plot(
  fcs.SCE,
  assay.i = "normalized",
  cell.clusters,
  condition,
  psig.cutoff = 0.05,
  return.stats = FALSE,
  colors,
  return.mode = "percentage",
  hide.nosig = FALSE,
  log.trans = FALSE,
  size = 3,
  labels.pos,
)

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".
cell.clusters	Name of column containing clusters identified through FlowCT::clustering.flow().
condition	Column name from the colData(fcs.SCE) object which contains condition information. De
psig.cutoff	P-value cutoff. Default = 0.05.
return.stats	Logical indicating if calculated statistics should be returned in a new variable. Default = FALSE.
colors	Vector with colors for plotting.
return.mode	String for specifying if final resuls should be proportions ("percentage") or raw counts ("counts"). Default = "percentage".
hide.nosig	Logical indicating whether hiding non-significal cell populations. Default = FALSE.
log.trans	Logarithmic transformation of counts/percentage values?. Default = FALSE.
size	Point size. Default = 3.
labels.pos	Position for cell clusters labelling, user should indicate the numeric position (i.e., 1 for first condition, 2 for second, and so on). By default, last condition will be used.

Examples

## Not run: 
parallel.plot(fcs.SCE = fcs, cell.clusters = "SOM_named")

## End(Not run)

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