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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/statistic-fgsea.R
\name{run_fgsea}
\alias{run_fgsea}
\title{Fast Gene Set Enrichment Analysis (FGSEA)}
\usage{
run_fgsea(
mat,
network,
.source = source,
.target = target,
times = 100,
nproc = availableCores(),
seed = 42,
minsize = 5,
...
)
}
\arguments{
\item{mat}{Matrix to evaluate (e.g. expression matrix).
Target nodes in rows and conditions in columns.
\code{rownames(mat)} must have at least one intersection with the elements
in \code{network} \code{.target} column.}
\item{network}{Tibble or dataframe with edges and it's associated metadata.}
\item{.source}{Column with source nodes.}
\item{.target}{Column with target nodes.}
\item{times}{How many permutations to do?}
\item{nproc}{Number of cores to use for computation.}
\item{seed}{A single value, interpreted as an integer, or NULL.}
\item{minsize}{Integer indicating the minimum number of targets per source.}
\item{...}{
Arguments passed on to \code{\link[fgsea:fgseaMultilevel]{fgsea::fgseaMultilevel}}
\describe{
\item{\code{sampleSize}}{The size of a random set of genes which in turn has size = pathwaySize}
\item{\code{minSize}}{Minimal size of a gene set to test. All pathways below the threshold are excluded.}
\item{\code{maxSize}}{Maximal size of a gene set to test. All pathways above the threshold are excluded.}
\item{\code{eps}}{This parameter sets the boundary for calculating the p value.}
\item{\code{scoreType}}{This parameter defines the GSEA score type.
Possible options are ("std", "pos", "neg").
By default ("std") the enrichment score is computed as in the original GSEA.
The "pos" and "neg" score types are intended to be used for one-tailed tests
(i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).}
\item{\code{gseaParam}}{GSEA parameter value, all gene-level statis are raised to the power of `gseaParam`
before calculation of GSEA enrichment scores.}
\item{\code{BPPARAM}}{Parallelization parameter used in bplapply.
Can be used to specify cluster to run. If not initialized explicitly or
by setting `nproc` default value `bpparam()` is used.}
\item{\code{absEps}}{deprecated, use `eps` parameter instead}
}}
}
\value{
A long format tibble of the enrichment scores for each source
across the samples. Resulting tibble contains the following columns:
\enumerate{
\item \code{statistic}: Indicates which method is associated with which score.
\item \code{source}: Source nodes of \code{network}.
\item \code{condition}: Condition representing each column of \code{mat}.
\item \code{score}: Regulatory activity (enrichment score).
}
}
\description{
Calculates regulatory activities using FGSEA.
}
\details{
GSEA (Aravind et al., 2005) starts by transforming the input molecular
readouts in mat to ranks for each sample. Then, an enrichment score
\code{fgsea} is calculated by walking down the list of features, increasing
a running-sum statistic when a feature in the target feature set is
encountered and decreasing it when it is not. The final score is the maximum
deviation from zero encountered in the random walk. Finally, a normalized
score \code{norm_fgsea}, can be obtained by computing the z-score of the estimate
compared to a null distribution obtained from N random permutations. The used
implementation is taken from the package \code{fgsea} (Korotkevich et al., 2021).
Aravind S. et al. (2005) Gene set enrichment analysis: A knowledge-based
approach for interpreting genome-wide expression profiles. PNAS. 102, 43.
Korotkevich G. et al. (2021) Fast gene set enrichment analysis. bioRxiv.
DOI: https://doi.org/10.1101/060012.
}
\examples{
inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")
mat <- readRDS(file.path(inputs_dir, "mat.rds"))
net <- readRDS(file.path(inputs_dir, "net.rds"))
run_fgsea(mat, net, minsize=0, nproc=1)
}
\seealso{
Other decoupleR statistics:
\code{\link{decouple}()},
\code{\link{run_aucell}()},
\code{\link{run_gsva}()},
\code{\link{run_mdt}()},
\code{\link{run_mlm}()},
\code{\link{run_ora}()},
\code{\link{run_udt}()},
\code{\link{run_ulm}()},
\code{\link{run_viper}()},
\code{\link{run_wmean}()},
\code{\link{run_wsum}()}
}
\concept{decoupleR statistics}
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