Perform differential analysis of single-sample gene set enrichment

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

runDSSEmods(K2res)

Arguments

K2res	An object of class K2. The output of K2tax().

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. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47--e47. ISSN 1362-4962, 0305-1048, doi: 10.1093/nar/gkv007 , http://academic.oup.com/nar/article/43/7/e47/2414268/limma-powers-differential-expression-analyses-for. Benjamini Y, Hochberg Y (1995). “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289--300. ISSN 00359246, doi: 10.1111/j.2517-6161.1995.tb02031.x , http://doi.wiley.com/10.1111/j.2517-6161.1995.tb02031.x. 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)

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