runGSVAmods: Perform ssGSVA on gene sets and get set signature hits

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

Perform ssGSVA on gene sets and get set signature hits

Description

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

Usage

runGSVAmods(K2res, ssGSEAalg = NULL, ssGSEAcores = NULL, ...)

Arguments

K2res	An object of class K2. The output of runDGEmods().
ssGSEAalg	A character string, specifying which algorithm to use for running the gsva() function from the GSVA package. Options are 'gsva', 'ssgsea', 'zscore', and 'plage'. 'gsva' by default.
ssGSEAcores	Number of cores to use for running gsva() from the GSVA package. Default is 1.
...	Additional arguments passed onto GSVA::gsva()

Value

An object of class K2.

References

\insertRef

reed_2020K2Taxonomer \insertRefgsvaK2Taxonomer

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)

## Run GSVA on genesets
K2res <- runGSVAmods(K2res,
                ssGSEAalg='gsva',
                ssGSEAcores=1,
                verbose=FALSE)

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