tna.mra: Master Regulator Analysis (MRA) over a list of regulons.

In RTN: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons

Description

This function takes a TNA object and returns the results of the RMA analysis over a list of regulons from a transcriptional network (with multiple hypothesis testing corrections).

Usage

tna.mra(object, pValueCutoff=0.05, pAdjustMethod="BH", minRegulonSize=15, 
tnet="dpi", 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.
tnet	a single character value specifying which transcriptional network should to used to compute the MRA analysis. Options: "dpi" and "ref".
tfs	an optional vector with transcription factor identifiers.
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 'rma' option in tna.get.

Author(s)

Mauro Castro

See Also

TNA-class

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 MRA analysis pipeline
rtna <- tna.mra(rtna)

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


## End(Not run)

RTN documentation built on Nov. 12, 2020, 2:02 a.m.