Calculates GSEA statistics for a given query gene set

Description

Takes O(k log k) time, where k is a size of selectedSize.

Usage

calcGseaStat(stats, selectedStats, gseaParam = 1,
  returnAllExtremes = FALSE, returnLeadingEdge = FALSE)

Arguments

Value

Value of GSEA statistic if both returnAllExtremes and returnLeadingEdge are FALSE. Otherwise returns list with the folowing elements:

• res -- value of GSEA statistic

• tops -- vector of top peak values of cumulative enrichment statistic for each gene;

• bottoms -- vector of bottom peak values of cumulative enrichment statistic for each gene;

• leadingGene -- vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading .

Examples

data(exampleRanks)
data(examplePathways)
ranks <- sort(exampleRanks, decreasing=TRUE)
es <- calcGseaStat(ranks, na.omit(match(examplePathways[[1]], names(ranks))))

Calculates GSEA statistic valus for all gene sets in selectedStats list.

Description

Takes O(n + mKlogK) time, where n is the number of genes, m is the number of gene sets, and k is the mean gene set size.

Usage

calcGseaStatBatchCpp(stats, selectedGenes, geneRanks)

Arguments

Value

Numeric vector of GSEA statistics of the same length as selectedGenes list

Collapse list of enriched pathways to independent ones.

Description

Collapse list of enriched pathways to independent ones.

Usage

collapsePathways(fgseaRes, pathways, stats, pval.threshold = 0.05,
  nperm = 10/pval.threshold, gseaParam = 1)

Arguments

Value

Named list with two elments: mainPathways containing IDs of pathways not reducable to each other, and parentPathways with vector describing for all the pathways to which ones they can be reduced. For pathways from mainPathwyas vector parentPathways contains NA values.

Examples

data(examplePathways)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=10000, maxSize=500)
collapsedPathways <- collapsePathways(fgseaRes[order(pval)][padj < 0.01],
examplePathways, exampleRanks)
mainPathways <- fgseaRes[pathway %in% collapsedPathways$mainPathways][
order(-NES), pathway]

Example list of mouse Reactome pathways.

Description

The list was obtained by selecting all the pathways from reactome.db package that contain mouse genes. The exact script is available as system.file("gen_reactome_pathways.R", package="fgsea")

Example vector of gene-level statistics obtained for Th1 polarization.

Description

The data were obtained by doing differential expression between Naive and Th1-activated states for GEO dataset GSE14308. The exact script is available as system.file("gen_gene_ranks.R", package="fgsea")

Runs preranked gene set enrichment analysis.

Description

The function takes about O(nk^{3/2}) time, where n is number of permutations and k is a maximal size of the pathways. That means that setting maxSize parameter with a value of ~500 is strongly recommended.

Usage

fgsea(pathways, stats, nperm, minSize = 1, maxSize = Inf, nproc = 0,
  gseaParam = 1, BPPARAM = NULL)

Arguments

Value

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following:

• pathway -- name of the pathway as in names(pathway);

• pval -- an enrichment p-value;

• padj -- a BH-adjusted p-value;

• ES -- enrichment score, same as in Broad GSEA implementation;

• NES -- enrichment score normalized to mean enrichment of random samples of the same size;

• nMoreExtreme-- a number of times a random gene set had a more extreme enrichment score value; * size -- size of the pathway after removing genes not present innames(stats)`.
  * leadingEdge -- vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading . ## Examples r data(examplePathways) data(exampleRanks) fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=10000, maxSize=500) # Testing only one pathway is implemented in a more efficient manner fgseaRes1 <- fgsea(examplePathways[1], exampleRanks, nperm=10000)

Runs label-permuring gene set enrichment analysis.

Description

Runs label-permuring gene set enrichment analysis.

Usage

fgseaLabel(pathways, mat, labels, nperm, minSize = 1, maxSize = Inf,
  nproc = 0, gseaParam = 1, BPPARAM = NULL)

Arguments

Value

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following:

• pathway -- name of the pathway as in names(pathway);

• pval -- an enrichment p-value;

• padj -- a BH-adjusted p-value;

• ES -- enrichment score, same as in Broad GSEA implementation;

• NES -- enrichment score normalized to mean enrichment of random samples of the same size;

• nMoreExtreme-- a number of times a random gene set had a more extreme enrichment score value; * size -- size of the pathway after removing genes not present innames(stats). * leadingEdge -- vector with indexes of leading edge genes that drive the enrichment, see [http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading](http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading) . ## Examples ```r list(" ", "library(limma) ", "library(GEOquery) ", "es <- getGEO("GSE19429", AnnotGPL = TRUE)[[1]] ", "exprs(es) <- normalizeBetweenArrays(log2(exprs(es)+1), method="quantile") ", "es <- es[!grepl("///", fData(es)$Gene ID), ] ", "es <- es[fData(es)$Gene ID!= "", ] ", "es <- es[order(apply(exprs(es), 1, mean), decreasing=TRUE), ] ", "es <- es[!duplicated(fData(es)$Gene ID), ] ", "rownames(es) <- fData(es)$Gene ID` ", " ", "pathways <- reactomePathways(rownames(es)) ", "mat <- exprs(es) ", "labels <- as.numeric(as.factor(gsub(" .*", "", es$title))) ", "fgseaRes <- fgseaLabel(pathways, mat, labels, nperm = 1000, minSize = 15, maxSize = 500) ") ```

Runs preranked gene set enrichment analysis.

Description

This feature is based on the adaptive multilevel splitting Monte Carlo approach. This allows us to exceed the results of simple sampling and calculate arbitrarily small P-values.

Usage

fgseaMultilevel(pathways, stats, sampleSize = 101, minSize = 1,
  maxSize = Inf, absEps = 0, nproc = 0, BPPARAM = NULL)

Arguments

Value

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following

• pathway -- name of the pathway as in names(pathway);

• pval -- an enrichment p-value;

• padj -- a BH-adjusted p-value;

• log2err -- the expected error for the standard deviation of the P-value logarithm.

• ES -- enrichment score, same as in Broad GSEA implementation;

• NES -- enrichment score normalized to mean enrichment of random samples of the same size;

• size -- size of the pathway after removing genes not present in names(stats).

• leadingEdge -- vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading .

Examples

data(examplePathways)
data(exampleRanks)
fgseaMultilevelRes <- fgseaMultilevel(examplePathways, exampleRanks, maxSize=500)

Runs preranked gene set enrichment analysis for preprocessed input data.

Description

Runs preranked gene set enrichment analysis for preprocessed input data.

Usage

fgseaSimpleImpl(pathwayScores, pathwaysSizes, pathwaysFiltered,
  leadingEdges, permPerProc, seeds, toKeepLength, stats, BPPARAM)

Arguments

Value

A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following:

• pathway -- name of the pathway as in names(pathway);

• pval -- an enrichment p-value;

• padj -- a BH-adjusted p-value;

• ES -- enrichment score, same as in Broad GSEA implementation;

• NES -- enrichment score normalized to mean enrichment of random samples of the same size;

• nMoreExtreme-- a number of times a random gene set had a more extreme enrichment score value; * size -- size of the pathway after removing genes not present innames(stats)`.
  * leadingEdge -- vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading .

Returns a list of pathways from a GMT file.

Description

Returns a list of pathways from a GMT file.

Usage

gmtPathways(gmt.file)

Arguments

Value

A list of vectors with gene sets.

Examples

pathways <- gmtPathways(system.file(
"extdata", "mouse.reactome.gmt", package="fgsea"))

Calculates the expected error for the standard deviation of the P-value logarithm.

Description

Calculates the expected error for the standard deviation of the P-value logarithm.

Usage

multilevelError(pval, sampleSize)

Arguments

Value

The value of the expected error

Examples

expectedError <- multilevelError(pval=1e-10, sampleSize=1001)

Calculates P-values for preprocessed data.

Description

Calculates P-values for preprocessed data.

Usage

multilevelImpl(multilevelPathwaysList, stats, sampleSize, seed, absEps,
  sign = FALSE, BPPARAM = NULL)

Arguments

Value

List of P-values.

Plots GSEA enrichment plot.

Description

Plots GSEA enrichment plot.

Usage

plotEnrichment(pathway, stats, gseaParam = 1, ticksSize = 0.2)

Arguments

Value

ggplot object with the enrichment plot.

Examples

data(examplePathways)
data(exampleRanks)
plotEnrichment(examplePathways[["5991130_Programmed_Cell_Death"]],
exampleRanks)

Plots table of enrichment graphs using ggplot and gridExtra.

Description

Plots table of enrichment graphs using ggplot and gridExtra.

Usage

plotGseaTable(pathways, stats, fgseaRes, gseaParam = 1,
  colwidths = c(5, 3, 0.8, 1.2, 1.2), render = TRUE)

Arguments

Value

TableGrob object returned by grid.arrange.

Examples

data(examplePathways)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=1000,
minSize=15, maxSize=100)
topPathways <- fgseaRes[head(order(pval), n=15)][order(NES), pathway]
plotGseaTable(examplePathways[topPathways], exampleRanks,
fgseaRes, gseaParam=0.5)

Returns a list of Reactome pathways for given Entrez gene IDs

Description

Returns a list of Reactome pathways for given Entrez gene IDs

Usage

reactomePathways(genes)

Arguments

Value

A list of vectors with gene sets.

Examples

data(exampleRanks)
pathways <- reactomePathways(names(exampleRanks))