fgseaPostprocessing: Postprocessing for GSEA analyses

In calebclass/NanoTube: An Easy Pipeline for NanoString nCounter Data Analysis

Postprocessing for GSEA analyses

Description

Clusters GSEA results by leading edge genes, and writes reports showing gene expression profiles of these genes.

Usage

fgseaPostprocessing(
  genesetResults,
  leadingEdge,
  limmaResults,
  join.threshold = 0.5,
  ngroups = NULL,
  dist.method = "binary",
  reportDir
)

Arguments

genesetResults	Results from pathway analysis using limmaToFGSEA.
leadingEdge	Results from fgseaToLEdge
limmaResults	Results from runLimmaAnalysis
join.threshold	The threshold distance to join gene sets. Gene sets with a distance below this value will be joined to a single "cluster."
ngroups	The desired number of gene set groups. Either 'join.threshold' or 'ngroups' must be specified, 'ngroups' takes priority if both are specified.
dist.method	Method for distance calculation (see options for dist()). We recommend the 'binary' (also known as Jaccard) distance.
reportDir	Directory for the GSEA reports (each comparison will be a separate txt file). Directory will be created if it does not exist.

Value

A table of gene set analysis results, as well as reports showing differential expression of leading edge genes.

Examples

data("ExamplePathways")
data("ExampleResults") # Results from runLimmaAnalysis

fgseaResults <- limmaToFGSEA(ExampleResults, gene.sets = ExamplePathways)

leadingEdge <- fgseaToLEdge(fgseaResults, cutoff.type = "padj", cutoff = 0.1)


fgseaPostprocessing(fgseaResults, leadingEdge, 
                    limmaResults = ExampleResults,
                    join.threshold = 0.5,
                    reportDir = "GSEAresults")

calebclass/NanoTube documentation built on Nov. 21, 2023, 12:31 p.m.