CondSEA {gep2pep}R Documentation

Performs Condition Set Enrichment Analysis

Description

Condition Set Enrichment Analysis (CondSEA) can be seen as a Gene-SEA performed over rows (as opposed to columns) of a matrix of GEPs. It tells how much a pathway is consistently dysregulated under a set of conditions (such as a set of drug treatments, disease states, cell types, etc.) when compared to a statistical background of other conditions.

Usage

CondSEA(rp_peps, pgset, bgset = "all", collections = "all",
  details = TRUE)

Arguments

rp_peps

A repository created with createRepository, and containing PEPs created with buildPEPs.

pgset

A vector of names of conditions. Corresponding PEPs must exist in all the pathway collections currently in rp.

bgset

The background against which to compare pgset. If set to all (default), all the remaining PEPs will be used. If provided, the corresponding PEPs must exist in all the pathway collections currently in rp.

collections

A subset of the collection names returned by getCollections. If set to "all" (default), all the collections in rp will be used.

details

If TRUE (default) rank details will be reported for each condition in pgset.

Details

For each pathway, all conditions are ranked by how much they dysregulate it (from the most UP-regulating to the most DOWN-regulating). Then, a Kolmogorov-Smirnov (KS) test is performed to compare the ranks assigned to conditions in pgset against the ranks assigned to conditions in bgset. 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 to perform Drug-Set Enrichment Analysis [1].

Value

A list of 2, by names "CondSEA" and "details". The "CondSEA" entry is a 2-columns matrix including ESs and p-values (see details) for each pathway database and condition. The "details" entry reports the rank of each condition in pgset for each pathway.

References

[1] Napolitano F. et al, Drug-set enrichment analysis: a novel tool to investigate drug mode of action. Bioinformatics 32, 235-241 (2016).

See Also

getResults, getDetails

Examples

db <- loadSamplePWS()
db <- as.CategorizedCollection(db)
repo_path <- file.path(tempdir(), "gep2pepTemp")

rp <- createRepository(repo_path, db)
geps <- loadSampleGEP()
buildPEPs(rp, geps)

pgset <- c("(+)_chelidonine", "(+/_)_catechin")
psea <- CondSEA(rp, pgset)

getResults(psea, "c3_TFT")

unlink(repo_path, TRUE)


[Package gep2pep version 1.0.0 Index]