Calculates GSEA statistics for a given query gene set
Takes O(k log k) time, where k is a size of 'selectedSize'.
calcGseaStat(
stats,
selectedStats,
gseaParam = 1,
returnAllExtremes = FALSE,
returnLeadingEdge = FALSE,
scoreType = c("std", "pos", "neg")
)stats |
Named numeric vector with gene-level statistics sorted in decreasing order (order is not checked). |
selectedStats |
Indexes of selected genes in the 'stats' array. |
gseaParam |
GSEA weight parameter (0 is unweighted, suggested value is 1). |
returnAllExtremes |
If TRUE return not only the most extreme point, but all of them. Can be used for enrichment plot |
returnLeadingEdge |
If TRUE return also leading edge genes. |
scoreType |
This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg") |
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.
data(exampleRanks) data(examplePathways) ranks <- sort(exampleRanks, decreasing=TRUE) es <- calcGseaStat(ranks, na.omit(match(examplePathways[[1]], names(ranks))))
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