runDSSEmods: Perform differential analysis of single-sample gene set...

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

Perform differential analysis of single-sample gene set enrichment

Description

Adds limma differential analysis results of single-sample enrichment scores to to the output of K2tax().

Usage

runDSSEmods(K2res)

Arguments

K2res

An object of class K2. The output of K2tax().

Value

An object of class K2.

References

\insertRef

reed_2020K2Taxonomer \insertReflimmaK2Taxonomer \insertRefbhK2Taxonomer \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)

## Run differential analysis on GSVA results
K2res <- runDSSEmods(K2res)

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