Package 'fgsea'

Description

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

Usage

calcGseaStat(
  stats,
  selectedStats,
  gseaParam = 1,
  returnAllExtremes = FALSE,
  returnLeadingEdge = FALSE,
  scoreType = c("std", "pos", "neg")
)

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))))

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

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]

Description

Collapse list of enriched pathways to independent ones (GESECA version, highly experimental).

Usage

collapsePathwaysGeseca(
  gesecaRes,
  pathways,
  E,
  center = TRUE,
  scale = FALSE,
  eps = min(c(1e-50, gesecaRes$pval)),
  checkDepth = 10,
  nproc = 0,
  BPPARAM = NULL
)

Arguments

Description

Collapse list of enriched pathways to independent ones. Version for ORA hypergeometric test.

Usage

collapsePathwaysORA(foraRes, pathways, genes, universe, pval.threshold = 0.05)

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)
foraRes <- fora(examplePathways, genes=tail(names(exampleRanks), 200), universe=names(exampleRanks))
collapsedPathways <- collapsePathwaysORA(foraRes[order(pval)][padj < 0.01],
                                             examplePathways,
                                             genes=tail(names(exampleRanks), 200),
                                             universe=names(exampleRanks))

mainPathways <- foraRes[pathway %in% collapsedPathways$mainPathways][
                          order(pval), pathway]

Description

Expression data was obtained by preprocessing the GSE14308 dataset. For the matrix of gene expression value, the following steps were performed:

• expression values were log2-scaled

• quantile-type normalization was perfomred between arrays

• rows were collapsed by 'ENTREZID'

• rows were sorted in descending order by mean expression value per gene

• finally, top-10_000 genes were taken

The exact script is available as system.file("gen_gse14308_expression_matrix.R", package="fgsea")

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")

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")

Description

This function provide an interface to two existing functions: fgseaSimple, fgseaMultilevel. By default, the fgseaMultilevel function is used for analysis. For compatibility with the previous implementation you can pass the 'nperm' argument to the function.

Usage

fgsea(
  pathways,
  stats,
  minSize = 1,
  maxSize = length(stats) - 1,
  gseaParam = 1,
  ...
)

Arguments

Value

A table with GSEA results. Each row corresponds to a tested pathway.

Examples

data(examplePathways)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks, maxSize=500)
# Testing only one pathway is implemented in a more efficient manner
fgseaRes1 <- fgsea(examplePathways[1], exampleRanks)

Description

Runs label-permuring gene set enrichment analysis.

Usage

fgseaLabel(
  pathways,
  mat,
  labels,
  nperm,
  minSize = 1,
  maxSize = nrow(mat) - 1,
  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 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

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)

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 = length(stats) - 1,
  eps = 1e-50,
  scoreType = c("std", "pos", "neg"),
  nproc = 0,
  gseaParam = 1,
  BPPARAM = NULL,
  nPermSimple = 1000,
  absEps = 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)

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

fgseaSimple(
  pathways,
  stats,
  nperm,
  minSize = 1,
  maxSize = length(stats) - 1,
  scoreType = c("std", "pos", "neg"),
  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 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)
fgseaRes <- fgseaSimple(examplePathways, exampleRanks, nperm=10000, maxSize=500)
# Testing only one pathway is implemented in a more efficient manner
fgseaRes1 <- fgseaSimple(examplePathways[1], exampleRanks, nperm=10000)

Description

Runs preranked gene set enrichment analysis for preprocessed input data.

Usage

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

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 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.

Description

Simple overrepresentation analysis based on hypergeometric test

Usage

fora(pathways, genes, universe, minSize = 1, maxSize = length(universe) - 1)

Arguments

Value

A table with ORA 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 from hypergeometric test;

• padj – a BH-adjusted p-value;

• overlap – size of the overlap;

• size – size of the gene set;

• leadingEdge – vector with overlapping genes.

Examples

data(examplePathways)
data(exampleRanks)
foraRes <- fora(examplePathways, genes=tail(names(exampleRanks), 200), universe=names(exampleRanks))

Description

This function is based on the adaptive multilevel splitting Monte Carlo approach and allows to estimate arbitrarily small P-values for the task of analyzing variance along a set of genes.

Usage

geseca(
  pathways,
  E,
  minSize = 1,
  maxSize = nrow(E) - 1,
  center = TRUE,
  scale = FALSE,
  sampleSize = 101,
  eps = 1e-50,
  nproc = 0,
  BPPARAM = NULL,
  nPermSimple = 1000
)

Arguments

Value

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

• pathway – name of the pathway as in 'names(pathways)';

• pctVar – percent of explained variance along gene set;

• pval – P-value that corresponds to the gene set score;

• padj – a BH-adjusted p-value;

• size – size of the pathway after removing genes not present in 'rownames(E)'.

Examples

data("exampleExpressionMatrix")
data("examplePathways")
gr <- geseca(examplePathways, exampleExpressionMatrix, minSize=15, maxSize=500)

Description

This function is based on the rude Monte Carlo sampling approach and P-value calculation accuracy is limited to '1 / nperm' value.

Usage

gesecaSimple(
  pathways,
  E,
  minSize = 1,
  maxSize = nrow(E) - 1,
  center = TRUE,
  scale = FALSE,
  nperm = 1000,
  nproc = 0,
  BPPARAM = NULL
)

Arguments

Value

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

• pathway – name of the pathway as in 'names(pathways)';

• pctVar – percent of explained variance along gene set;

• pval – P-value that corresponds to the gene set score;

• padj – a BH-adjusted p-value;

• size – size of the pathway after removing genes not present in 'rownames(E)'.

Examples

data("exampleExpressionMatrix")
data("examplePathways")
gesecaRes <- gesecaSimple(examplePathways, exampleExpressionMatrix, minSize=15, maxSize=500)

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"))

Description

Effeciently converts collection of pathways using AnnotationDbi::mapIds function. Parameters are the sames as for mapIds except for keys, which is assumed to be a list of vectors.

Usage

mapIdsList(x, keys, column, keytype, ...)

Arguments

See Also

AnnotationDbi::mapIds

Examples

library(org.Mm.eg.db)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks, maxSize=500, eps=1e-4)
fgseaRes[, leadingEdge := mapIdsList(org.Mm.eg.db, keys=leadingEdge, column="SYMBOL", keytype="ENTREZID")]

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)

Description

Calculates P-values for preprocessed data.

Usage

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

Arguments

Value

List of P-values.

Description

Plots expression profile of a gene set

Usage

plotCoregulationProfile(
  pathway,
  E,
  center = TRUE,
  scale = FALSE,
  titles = colnames(E),
  conditions = NULL
)

Arguments

Value

ggplot object with the coregulation profile plot

Description

Plot a spatial expression profile of a gene set

Usage

plotCoregulationProfileReduction(
  pathway,
  object,
  title = NULL,
  assay = DefaultAssay(object),
  reduction = NULL,
  colors = c("darkblue", "lightgrey", "darkred"),
  guide = "colourbar",
  ...
)

Arguments

Value

ggplot object (or a list of objects) with the coregulation profile plot

When the input is a list of pathways, pathway names are used for titles. A list of ggplot objects a returned in that case.

Description

Plot a spatial expression profile of a gene set

Usage

plotCoregulationProfileSpatial(
  pathway,
  object,
  title = NULL,
  assay = DefaultAssay(object),
  colors = c("darkblue", "lightgrey", "darkred"),
  guide = "colourbar"
)

Arguments

Value

ggplot object (or a list of objects) with the coregulation profile plot

When the input is a list of pathways, pathway names are used for titles. A list of ggplot objects a returned in that case.

Description

Plots GSEA enrichment plot. For more flexibility use 'plotEnrichmentData' function.

Usage

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

Arguments

Value

ggplot object with the enrichment plot.

Examples

data(examplePathways)
data(exampleRanks)
## Not run: 
plotEnrichment(examplePathways[["5991130_Programmed_Cell_Death"]],
               exampleRanks)

## End(Not run)

Description

Returns data required for doing an enrichment plot.

Usage

plotEnrichmentData(pathway, stats, gseaParam = 1)

Arguments

Value

returns list with the following data: * 'curve' - data.table with the coordinates of the enrichment curve; * 'ticks' - data.table with statistic entries for each pathway gene,adjusted with gseaParam; * 'stats' - data.table with statistic values for all of the genes, adjusted with gseaParam; * 'posES', 'negES', 'spreadES' - values of the positive enrichment score, negative enrichment score, and difference between them; * 'maxAbsStat' - maximal absolute value of statistic entries, adjusted with gseaParam

Examples

library(ggplot2)
data(examplePathways)
data(exampleRanks)

pd <- plotEnrichmentData(
    pathway = examplePathways[["5991130_Programmed_Cell_Death"]],
    stats = exampleRanks
)

with(pd,
     ggplot(data=curve) +
         geom_line(aes(x=rank, y=ES), color="green") +
         geom_ribbon(data=stats,
                     mapping=aes(x=rank, ymin=0,
                                 ymax=stat/maxAbsStat*(spreadES/4)),
                     fill="grey") +
         geom_segment(data=ticks,
                      mapping=aes(x=rank, y=-spreadES/16,
                                  xend=rank, yend=spreadES/16),
                      size=0.2) +
         geom_hline(yintercept=posES, colour="red", linetype="dashed") +
         geom_hline(yintercept=negES, colour="red", linetype="dashed") +
         geom_hline(yintercept=0, colour="black") +
         theme(
             panel.background = element_blank(),
             panel.grid.major=element_line(color="grey92")
         ) +
         labs(x="rank", y="enrichment score"))

Description

Plots table of gene set profiles.

Usage

plotGesecaTable(
  gesecaRes,
  pathways,
  E,
  center = TRUE,
  scale = FALSE,
  colwidths = c(5, 3, 0.8, 1.2, 1.2),
  titles = colnames(E),
  colors = c("blue", "white", "red"),
  pathwayLabelStyle = NULL,
  headerLabelStyle = NULL,
  valueStyle = NULL,
  axisLabelStyle = NULL,
  axisLabelHeightScale = NULL
)

Arguments

Value

ggplot object with gene set profile plots

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),
  pathwayLabelStyle = NULL,
  headerLabelStyle = NULL,
  valueStyle = NULL,
  axisLabelStyle = NULL,
  render = NULL
)

Arguments

Value

ggplot object with enrichment barcode plots

Examples

data(examplePathways)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks, minSize=15, maxSize=500)
topPathways <- fgseaRes[head(order(pval), n=15)][order(NES), pathway]
plotGseaTable(examplePathways[topPathways], exampleRanks,
              fgseaRes, gseaParam=0.5)

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))

Description

Write collection of pathways (list of vectors) to a gmt file

Usage

writeGmtPathways(pathways, gmt.file)

Arguments

Examples

data(examplePathways)
writeGmtPathways(examplePathways, tempfile("examplePathways", fileext=".gmt"))