PathSEA is analogous to the Gene Set Enrichment Analysis (GSEA), but for pathways instead of single genes. It can therefore be used to look for conditions under which a given set of pathways is consistently UP- or DOWN-regulated.
A repository created with
A database of pathways in the same format as input
Another list like
A subset of the collection names returned by
Character vector including PEP names to be considered (all by default, which may take time).
If TRUE (default) details will be reported for each
The function used to rank PEPs column-wise. By
For each condition, all pathways are ranked by how much
they are dysregulated by it (from the most UP-regulated to the
most DOWN-regulatied, according to the corresponding
p-values). Then, a Kolmogorov-Smirnov (KS) test is performed to
compare the ranks assigned to pathways in
against the ranks assigned to pathways in
positive (negative) Enrichment Score (ES) of the KS test
indicates whether each pathway is UP- (DOWN-) regulated by
pgset as compared to
bgset. A p-value is
associated to the ES.
When PEPs are obtained from drug-induced gene expression
PathSEA can be used together with
gene2pathways to perform gene2drug  analysis, which
predicts which drugs may target a gene of interest (or mimick
rankingFun must take in input PEPs like those loaded
from the repository and return a matrix of column-wise
ranks. Each column must contain ranks from 1 to the number of
gene sets minus the number of NAs in the column.
A list of 2, by names "PathSEA" and "details". The
"PathSEA" entry is a 2-columns matrix including ESs and
p-values for each collection and condition. The "details" entry
reports the rank of each pathway in
pathways for each
 Napolitano, F. et al. gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics (2017). https://doi.org/10.1093/bioinformatics/btx800
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library(GSEABase) db <- loadSamplePWS() repo_path <- file.path(tempdir(), "gep2pepTemp") rp <- createRepository(repo_path, db) geps <- loadSampleGEP() buildPEPs(rp, geps) pathways <- c("M11607", "M10817", "M16694", ## from c3_TFT "M19723", "M5038", "M13419", "M1094") ## from c4_CGN w <- sapply(db, setIdentifier) %in% pathways psea <- PathSEA(rp, db[w]) ## [15:35:29] Working on collection: c3_TFT ## [15:35:29] Common pathway sets removed from bgset. ## [15:35:29] Column-ranking collection... ## [15:35:29] Computing enrichments... ## [15:35:29] done. ## [15:35:29] Working on collection: C4_CGN ## [15:35:29] Common pathway sets removed from bgset. ## [15:35:29] Column-ranking collection... ## [15:35:29] Computing enrichments... ## [15:35:29] done. getResults(psea, "c3_TFT") ## ES PV ## (_)_mk_801 0.7142857 0.1666667 ## (_)_atenolol 0.7142857 0.1666667 ## (+)_isoprenaline 0.5714286 0.4000000 ## (+/_)_catechin 0.5714286 0.4000000 ## (+)_chelidonine 0.3333333 0.9333333 unlink(repo_path, TRUE)
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