Take difference of two paired GSVA scores

Replaces GSVA results from paired up- and down- gene sets with the difference of the up-regulated genes and down-regulated genes

aggregateGSVAscores(aggList, K2res)

Arguments

aggList	A list where each item is a vector of 3 items: the new name, the name of the 'up' gene set, and the name of the 'down' gene set.
K2res	An object of class K2. The output of runDGEmods().

Value

An object of class K2.

References

Reed ER, Monti S (2020). “Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.” Bioinformatics. doi: 10.1101/2020.11.05.370197 , http://biorxiv.org/lookup/doi/10.1101/2020.11.05.370197. Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14(1), 7. ISSN 1471-2105, doi: 10.1186/1471-2105-14-7 , http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7.

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)

## Aggregate paired gene sets
aggList <- list(c('GS12', 'GS1', 'GS2'))
K2res <- aggregateGSVAscores(aggList, K2res)