gsvaSCE: Run GSVA analysis on a SCtkExperiment object.

In mmkhan19/singleCellTK: Interactive Analysis of Single Cell RNA-Seq Data

Run GSVA analysis on a SCtkExperiment object.

Description

Run GSVA analysis on a SCtkExperiment object.

Usage

gsvaSCE(inSCE, useAssay = "logcounts", pathwaySource, pathwayNames, ...)

gsvaPlot(
  inSCE,
  gsvaData,
  plotType,
  condition = NULL,
  show_column_names = TRUE,
  show_row_names = TRUE,
  text_size = 12
)

Arguments

inSCE	Input SCtkExperiment object. Required
useAssay	Indicate which assay to use. The default is "logcounts"
pathwaySource	The pathway source if "Manual Input", the pathwayNames should be rowData annotations that are (0,1) vectors. If, "MSigDB c2 (Human, Entrez ID only)", the pathwayNames should be pathways from MSigDB c2 or "ALL" to run on all available pathways.
pathwayNames	List of pathway names to run, depending on pathwaySource parameter.
...	Parameters to pass to gsva()
gsvaData	GSVA data to plot. Required.
plotType	The type of plot to use, "Violin" or "Heatmap". Required.
condition	The condition(s) to use for the Violin plot, or the condition(s) to add as color bars above the Heatmap. Required for Violin, optional for Heatmap.
show_column_names	Display the column labels on the heatmap. The default is TRUE
show_row_names	Display the row labels on the heatmap. The default is TRUE.
text_size	Text size for plots. The default is 12

Value

gsvaSCE(): A data.frame of pathway activity scores from GSVA.

gsvaPlot(): The requested plot of the GSVA results.

Functions

• gsvaPlot: Plot GSVA results.

  Plot GSVA Results

Examples

utils::data(maits, package = "MAST")
utils::data(c2BroadSets, package = "GSVAdata")
maitslogtpm <- t(maits$expressionmat)
genesToSubset <- rownames(maitslogtpm)[which(rownames(maitslogtpm) %in%
                 GSEABase::geneIds(c2BroadSets[["KEGG_PROTEASOME"]]))]
maitslogtpm <- maitslogtpm[rownames(maitslogtpm) %in% genesToSubset, ]
maitsfeatures <- maits$fdat[rownames(maits$fdat) %in% genesToSubset, ]
maitsSCE <- createSCE(assayFile = maitslogtpm, annotFile = maits$cdat,
                      featureFile = maitsfeatures, assayName = "logtpm",
                      inputDataFrames = TRUE, createLogCounts = FALSE)
rowData(maitsSCE)$testbiomarker <- rep(1, nrow(maitsSCE))
res <- gsvaSCE(inSCE = maitsSCE, useAssay = "logtpm",
               pathwaySource = "Manual Input", pathwayNames = "testbiomarker",
               parallel.sz = 1)
#Create a small example to run
utils::data(maits, package = "MAST")
utils::data(c2BroadSets, package = "GSVAdata")
maitslogtpm <- t(maits$expressionmat)
genesToSubset <- rownames(maitslogtpm)[which(rownames(maitslogtpm) %in%
                 GSEABase::geneIds(c2BroadSets[["KEGG_PROTEASOME"]]))]
maitslogtpm <- maitslogtpm[rownames(maitslogtpm) %in% genesToSubset, ]
maitsfeatures <- maits$fdat[rownames(maits$fdat) %in% genesToSubset, ]
maitsSCE <- createSCE(assayFile = maitslogtpm, annotFile = maits$cdat,
                      featureFile = maitsfeatures, assayName = "logtpm",
                      inputDataFrames = TRUE, createLogCounts = FALSE)
rowData(maitsSCE)$testbiomarker <- rep(1, nrow(maitsSCE))
res <- gsvaSCE(inSCE = maitsSCE, useAssay = "logtpm",
               pathwaySource = "Manual Input", pathwayNames = "testbiomarker",
               parallel.sz = 1)
gsvaPlot(inSCE = maitsSCE, gsvaData = res,
         plotType = "Violin", condition = "condition")

mmkhan19/singleCellTK documentation built on March 22, 2022, 7:43 a.m.