PlotCosineSimHeatmap: Plot heatmap of the cosine similarity score

In nmah/CellScore: Tool for Evaluation of Cell Identity from Transcription Profiles

Plot heatmap of the cosine similarity score

Description

This function plots a triangular heatmap of the cosine similarity scores.

Usage

PlotCosineSimHeatmap(
  data,
  desc = "xx",
  width = 20,
  height = 20,
  x = -30,
  y = 3
)

Arguments

data	a data.frame of cosine similarity scores, as generated by the function CosineSimScore().
desc	a single character, with description for the file name. Suggested are "general.groups", "subgroups", and "samples".
width	the width of the output pdf, in inches.
height	the height of the output pdf, in inches.
x	the x-position of the heatmap legend. It may be necessary to change the value to position the legend in a suitable place on the plot.
y	the y-position of the heatmap legend. It may be necessary to change the value to position the legend in a suitable place on the plot.

Value

This function will print a pdf of the cosine similarity scores in the current working directory.

See Also

CosineSimScore for details on cosine similarity calculation.

Examples

## Load the expression set for the standard cell types
library(Biobase)
library(hgu133plus2CellScore) # eset.std

## Locate the external data files in the CellScore package
rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore")
tsvdata.path <- system.file("extdata", "cell_change_test.tsv",
                            package = "CellScore")

if (file.exists(rdata.path) && file.exists(tsvdata.path)) {

   ## Load the expression set with normalized expressions of 48 test samples
   load(rdata.path)

   ## Import the cell change info for the loaded test samples
   cell.change <- read.delim(file= tsvdata.path, sep="\t",
                             header=TRUE, stringsAsFactors=FALSE)

   ## Combine the standards and the test data
   eset <- combine(eset.std, eset48)

   ## Generate cosine similarity for the combined data
   ## NOTE: May take 1-2 minutes on the full eset object,
   ## so we subset it for 4 cell types
   pdata <- pData(eset)
   sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", 
                 "ASC", "NPC", "MSC")
   eset.sub <- eset[, sel.samples]
   cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1)

   ## Generate pdf of cosine similarity heatmap in the working directory
   PlotCosineSimHeatmap(cs$cosine.general.groups, "general groups",
                        width=7, height=7, x=-3.5, y=1)
}

nmah/CellScore documentation built on May 4, 2023, 2:52 p.m.