PathSEA {gep2pep} | R Documentation |
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.
PathSEA(rp_peps, pathways, bgsets = "all", collections = "all", subset = "all", details = TRUE, rankingFun = rankPEPsByCols.SPV)
rp_peps |
A repository created with
|
pathways |
A database of pathways in the same format as input
to |
bgsets |
Another list like |
collections |
A subset of the collection names returned by
|
subset |
Character vector including PEP names to be considered (all by default, which may take time). |
details |
If TRUE (default) details will be reported for each
condition in |
rankingFun |
The function used to rank PEPs column-wise. By
default |
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 pathways
against the ranks assigned to pathways in bgsets
. A
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
profiles, PathSEA
can be used together with
gene2pathways
to perform gene2drug [1] analysis, which
predicts which drugs may target a gene of interest (or mimick
such effect).
The 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
condition.
[1] Napolitano, F. et al. gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics (2017). https://doi.org/10.1093/bioinformatics/btx800
getResults, getDetails
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)