Generate violin plots for pathway analysis results

plotPathway(
  inSCE,
  resultName,
  geneset,
  groupBy = NULL,
  boxplot = FALSE,
  violin = TRUE,
  dots = TRUE,
  summary = "median",
  axisSize = 10,
  axisLabelSize = 10,
  dotSize = 0.5,
  transparency = 1,
  defaultTheme = TRUE,
  gridLine = FALSE,
  title = geneset,
  titleSize = NULL
)

Arguments

inSCE: Input SingleCellExperiment object. With runGSVA() or runVAM() applied in advance.
resultName: A single character of the name of a score matrix, which should be found in getPathwayResultNames(inSCE).
geneset: A single character specifying the geneset of interest. Should be found in the geneSetCollection used for performing the analysis.
groupBy: Either a single character specifying a column of colData(inSCE) or a vector of equal length as the number of cells. Default NULL.
boxplot: Boolean, Whether to add a boxplot. Default FALSE.
violin: Boolean, Whether to add a violin plot. Default TRUE.
dots: Boolean, If TRUE, will plot dots for each violin plot. Default TRUE.
summary: Adds a summary statistic, as well as a crossbar to the violin plot. Options are "mean" or "median", and NULL for not adding. Default "median".
axisSize: Size of x/y-axis ticks. Default 10.
axisLabelSize: Size of x/y-axis labels. Default 10.
dotSize: Size of dots. Default 0.5.
transparency: Transparency of the dots, values will be 0-1. Default 1.
defaultTheme: Removes grid in plot and sets axis title size to 10 when TRUE. Default TRUE.
gridLine: Adds a horizontal grid line if TRUE. Will still be drawn even if defaultTheme is TRUE. Default FALSE.
title: Title of plot. Default using geneset.
titleSize: Size of the title of the plot. Default 15.

Value

A ggplot object for the violin plot

Details

runGSVA() or runVAM() should be applied in advance of using this function. Users can group the data by specifying groupby.

Examples

data("scExample", package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- scaterlogNormCounts(sce, assayName = "logcounts")
gs1 <- rownames(sce)[seq(10)]
gs2 <- rownames(sce)[seq(11,20)]
gs <- list("geneset1" = gs1, "geneset2" = gs2)
sce <- importGeneSetsFromList(inSCE = sce, geneSetList = gs,
                              by = "rownames")
sce <- runVAM(inSCE = sce, geneSetCollectionName = "GeneSetCollection",
              useAssay = "logcounts")
#> Sat Mar 18 10:29:28 2023 ... Running VAM
#> gene.weights not specified, defaulting all weights to 1
#> Computing VAM distances for 2 gene sets, 195 cells and 200 genes.
#> Min set size: 10, median size: 10
plotPathway(sce, "VAM_GeneSetCollection_CDF", "geneset1")