runGSEmods: Perform hyperenrichment of split specific gene signatures

In montilab/K2Taxonomer: Creating annotated submodules of high-throughput data through recursive partitioning

Perform hyperenrichment of split specific gene signatures

Description

Adds hyperenrichment analysis results to the output of runDGEmods().

Usage

runGSEmods(
  K2res,
  genesets = NULL,
  qthresh = NULL,
  cthresh = NULL,
  ntotal = NULL
)

Arguments

K2res	An object of class K2. The output of runDGEmods().
genesets	A named list of feature IDs
qthresh	A numeric value between 0 and 1 of the FDR cuttoff to define feature sets.
cthresh	A positive value for the coefficient cuttoff to define
ntotal	The total number of genes sampled from. feature sets.

Value

An object of class K2.

References

\insertRef

reed_2020K2Taxonomer \insertRefbhK2Taxonomer

Examples

## Read in ExpressionSet object
library(Biobase)
data(sample.ExpressionSet)

## Pre-process and create K2 object
K2res <- K2preproc(sample.ExpressionSet)

## Run K2 Taxonomer algorithm
K2res <- K2tax(K2res,
            stabThresh=0.5)

## Run differential analysis on each partition
K2res <- runDGEmods(K2res)

## Create dummy set of gene sets
DGEtable <- getDGETable(K2res)
genes <- unique(DGEtable$gene)
genesetsMadeUp <- list(
    GS1=genes[1:50],
    GS2=genes[51:100],
    GS3=genes[101:150])

## Run gene set hyperenrichment
K2res <- runGSEmods(K2res,
                genesets=genesetsMadeUp,
                qthresh=0.1)

montilab/K2Taxonomer documentation built on Nov. 8, 2024, 2:36 a.m.