Two-tailed Gene Set Enrichment Analysis (GSEA) over a list of regulons.

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Description

This function takes a TNA object and returns a CMAP-like analysis obtained by two-tailed GSEA over a list of regulons in a transcriptional network (with multiple hypothesis testing corrections).

Usage

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tna.gsea2(object, pValueCutoff=0.05, pAdjustMethod="BH",  minRegulonSize=15, nPermutations=1000, 
        exponent=1, tnet="dpi", stepFilter=TRUE, tfs=NULL, verbose=TRUE)

Arguments

object

a preprocessed object of class 'TNA' TNA-class.

pValueCutoff

a single numeric value specifying the cutoff for p-values considered significant.

pAdjustMethod

a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details).

minRegulonSize

a single integer or numeric value specifying the minimum number of elements in a regulon that must map to elements of the gene universe. Gene sets with fewer than this number are removed from the analysis.

nPermutations

a single integer or numeric value specifying the number of permutations for deriving p-values in GSEA.

exponent

a single integer or numeric value used in weighting phenotypes in GSEA (see 'gseaScores' function at HTSanalyzeR).

tnet

a single character value specifying which transcriptional network should to used to compute the GSEA analysis. Options: "dpi" and "ref".

stepFilter

a single logical value specifying to use a step-filter algorithm removing non-significant regulons derived from tna.mra (when stepFilter=TRUE) or not (when stepFilter=FALSE). It may have a substantial impact on the overall processing time.

tfs

an optional vector with transcription factor identifiers (this option overrides the 'stepFilter' argument).

verbose

a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).

Value

a data frame in the slot "results", see 'gsea2' option in tna.get.

Author(s)

Mauro Castro

See Also

TNA-class tna.plot.gsea2

Examples

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data(dt4rtn)

tfs4test<-c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")
rtni <- new("TNI", gexp=dt4rtn$gexp, transcriptionFactors=dt4rtn$tfs[tfs4test])

## Not run: 

rtni <- tni.preprocess(rtni,gexpIDs=dt4rtn$gexpIDs)
rtni<-tni.permutation(rtni)
rtni<-tni.bootstrap(rtni)
rtni<-tni.dpi.filter(rtni)
rtna<-tni2tna.preprocess(rtni, phenotype=dt4rtn$pheno, hits=dt4rtn$hits, phenoIDs=dt4rtn$phenoIDs)

#run GSEA2 analysis pipeline
rtna <- tna.gsea2(rtna,stepFilter=FALSE)

#get results
tna.get(rtna,what="gsea2")

# run parallel version with SNOW package!
library(snow)
options(cluster=makeCluster(3, "SOCK"))
rtna <- tna.gsea2(rtna,stepFilter=FALSE)
stopCluster(getOption("cluster"))

## End(Not run)

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