% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gsvaNewAPI.R
\name{gsva}
\alias{gsva}
\alias{gsva,plageParam-method}
\alias{gsva,zscoreParam-method}
\alias{gsva,ssgseaParam-method}
\alias{gsva,gsvaParam-method}
\title{Gene Set Variation Analysis}
\usage{
\S4method{gsva}{plageParam}(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

\S4method{gsva}{zscoreParam}(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

\S4method{gsva}{ssgseaParam}(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))

\S4method{gsva}{gsvaParam}(param, verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))
}
\arguments{
\item{param}{A parameter object of one of the following classes:
\itemize{
\item A \code{\link{gsvaParam}} object built using the constructor function
\code{\link{gsvaParam}}.
This object will trigger \code{gsva()} to use the GSVA algorithm by
Hänzelmann et al. (2013).
\item A \code{\link{plageParam}} object built using the constructor function
\code{\link{plageParam}}.
This object will trigger \code{gsva()} to use the PLAGE algorithm by
Tomfohr et al. (2005).
\item A \code{\link{zscoreParam}} object built using the constructor function
\code{\link{zscoreParam}}.
This object will trigger \code{gsva()} to use the combined z-score algorithm by
Lee et al. (2008).
\item A \code{\link{ssgseaParam}} object built using the constructor function
\code{\link{ssgseaParam}}.
This object will trigger \code{gsva()} to use the ssGSEA algorithm by
Barbie et al. (2009).
}}

\item{verbose}{Gives information about each calculation step. Default: \code{TRUE}.}

\item{BPPARAM}{An object of class \code{\link{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 \code{\linkS4class{dgCMatrix}} object, then the output will
be a base matrix object with the gene sets employed in the calculations
stored in an attribute called \code{geneSets}. If the input was an
\code{\link{ExpressionSet}} object, then the output will be also an \code{\link{ExpressionSet}}
object with the gene sets employed in the calculations stored in an
attributed called \code{geneSets}. If the input was an object of one of the
classes described in \code{\link{GsvaExprData}}, such as a \code{\link{SingleCellExperiment}},
then the output will be of the same class, where enrichment scores will be
stored in an assay called \code{es} and the gene sets employed in the
calculations will be stored in the \code{rowData} slot of the object under the
column name \code{gs}.
}
\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 \code{gsva()} function, please consult \code{\link{GSVA-pkg-defunct}}.
}
\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)

## 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")
}
\references{
Barbie, D.A. et al. Systematic RNA interference reveals that
oncogenic KRAS-driven cancers require TBK1.
\emph{Nature}, 462(5):108-112, 2009.
\href{https://doi.org/10.1038/nature08460}{DOI}

Hänzelmann, S., Castelo, R. and Guinney, J. GSVA: Gene set
variation analysis for microarray and RNA-Seq data.
\emph{BMC Bioinformatics}, 14:7, 2013.
\href{https://doi.org/10.1186/1471-2105-14-7}{DOI}

Lee, E. et al. Inferring pathway activity toward precise
disease classification.
\emph{PLoS Comp Biol}, 4(11):e1000217, 2008.
\href{https://doi.org/10.1371/journal.pcbi.1000217}{DOI}

Tomfohr, J. et al. Pathway level analysis of gene expression
using singular value decomposition.
\emph{BMC Bioinformatics}, 6:225, 2005.
\href{https://doi.org/10.1186/1471-2105-6-225}{DOI}
}
\seealso{
\code{\link{plageParam}}, \code{\link{zscoreParam}}, \code{\link{ssgseaParam}}, \code{\link{gsvaParam}}
}
