gsva: Gene Set Variation Analysis

In rcastelo/GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data

Gene Set Variation Analysis

Description

Estimates GSVA enrichment scores. The API of this function has changed in the Bioconductor release 3.18 and this help page describes the new API. The old API is defunct and will be removed in the next Bioconductor release. If you are looking for the documentation of the old API to the gsva() function, please consult GSVA-pkg-defunct.

Usage

## S4 method for signature 'plageParam'
gsva(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

## S4 method for signature 'zscoreParam'
gsva(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

## S4 method for signature 'ssgseaParam'
gsva(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

## S4 method for signature 'gsvaParam'
gsva(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

Arguments

param	A parameter object of one of the following classes: A gsvaParam object built using the constructor function gsvaParam. This object will trigger gsva() to use the GSVA algorithm by Hänzelmann et al. (2013). A plageParam object built using the constructor function plageParam. This object will trigger gsva() to use the PLAGE algorithm by Tomfohr et al. (2005). A zscoreParam object built using the constructor function zscoreParam. This object will trigger gsva() to use the combined z-score algorithm by Lee et al. (2008). A ssgseaParam object built using the constructor function ssgseaParam. This object will trigger gsva() to use the ssGSEA algorithm by Barbie et al. (2009).
verbose	Gives information about each calculation step. Default: TRUE.
BPPARAM	An object of class BiocParallelParam specifying parameters related to the parallel execution of some of the tasks and calculations within this function.

Value

A gene-set by sample matrix of GSVA enrichment scores stored in a container object of the same type as the input expression data container. If the input was a base matrix or a dgCMatrix object, then the output will be a base matrix object with the gene sets employed in the calculations stored in an attribute called geneSets. If the input was an ExpressionSet object, then the output will be also an ExpressionSet object with the gene sets employed in the calculations stored in an attributed called geneSets. If the input was an object of one of the classes described in GsvaExprData, such as a SingleCellExperiment, then the output will be of the same class, where enrichment scores will be stored in an assay called es and the gene sets employed in the calculations will be stored in the rowData slot of the object under the column name gs.

References

Barbie, D.A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(5):108-112, 2009. DOI

Hänzelmann, S., Castelo, R. and Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics, 14:7, 2013. DOI

Lee, E. et al. Inferring pathway activity toward precise disease classification. PLoS Comp Biol, 4(11):e1000217, 2008. DOI

Tomfohr, J. et al. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics, 6:225, 2005. DOI

See Also

plageParam, zscoreParam, ssgseaParam, gsvaParam

Examples

library(GSVA)
library(limma)

p <- 10 ## number of genes
n <- 30 ## number of samples
nGrp1 <- 15 ## number of samples in group 1
nGrp2 <- n - nGrp1 ## number of samples in group 2

## consider three disjoint gene sets
geneSets <- list(set1=paste("g", 1:3, sep=""),
                 set2=paste("g", 4:6, sep=""),
                 set3=paste("g", 7:10, sep=""))

## sample data from a normal distribution with mean 0 and st.dev. 1
y <- matrix(rnorm(n*p), nrow=p, ncol=n,
            dimnames=list(paste("g", 1:p, sep="") , paste("s", 1:n, sep="")))

## genes in set1 are expressed at higher levels in the last 'nGrp1+1' to 'n' samples
y[geneSets$set1, (nGrp1+1):n] <- y[geneSets$set1, (nGrp1+1):n] + 2

## build design matrix
design <- cbind(sampleGroup1=1, sampleGroup2vs1=c(rep(0, nGrp1), rep(1, nGrp2)))

## fit linear model
fit <- lmFit(y, design)

## estimate moderated t-statistics
fit <- eBayes(fit)

## genes in set1 are differentially expressed
topTable(fit, coef="sampleGroup2vs1")

## build GSVA parameter object
gsvapar <- gsvaParam(y, geneSets, maxDiff=TRUE)

## estimate GSVA enrichment scores for the three sets
gsva_es <- gsva(gsvapar)

## fit the same linear model now to the GSVA enrichment scores
fit <- lmFit(gsva_es, design)

## estimate moderated t-statistics
fit <- eBayes(fit)

## set1 is differentially expressed
topTable(fit, coef="sampleGroup2vs1")

rcastelo/GSVA documentation built on May 9, 2024, 7:23 p.m.