tna.gsea2 {RTN} | R Documentation

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

### 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

    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

    data(tniData)
    data(tnaData)

    ## Not run:

    rtni <- tni.constructor(expData=tniData$expData,
            regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"),
            rowAnnotation=tniData$rowAnnotation)
    rtni <- tni.permutation(rtni)
    rtni <- tni.bootstrap(rtni)
    rtni <- tni.dpi.filter(rtni)
    rtna <- tni2tna.preprocess(rtni, phenotype=tnaData$phenotype,
            hits=tnaData$hits, phenoIDs=tnaData$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=snow::makeCluster(3, "SOCK"))
    rtna <- tna.gsea2(rtna,stepFilter=FALSE)
    stopCluster(getOption("cluster"))

    ## End(Not run)

[Package RTN version 2.10.1 Index]