tna.shadow {RTN}R Documentation

shadow analysis over a list of regulons.

Description

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

Usage

tna.shadow(object, pValueCutoff=0.05, pAdjustMethod="BH", minRegulonSize=15, minIntersectSize=1,
                  nPermutations=1000, exponent=1, tnet="ref", orderAbsValue=TRUE, 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.

minIntersectSize

a single integer or numeric value specifying the minimum number of elements in the intersect between any two regulons in the shadow analysis (as percentage value).

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 shadow and shadow analyses. Options: "dpi" and "ref".

orderAbsValue

a single logical value indicating whether the values should be converted to absolute values and then ordered (if TRUE), or ordered as they are (if FALSE).

stepFilter

a single logical value specifying to use a step-filter algorithm removing non-significant regulons derived from tna.gsea1 (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 'shadow' in tna.get.

Author(s)

Mauro Castro

See Also

TNA-class tna.shadow

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 overlap analysis pipeline
rtna <- tna.overlap(rtna)

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

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

# run parallel version with SNOW package!
library(snow)
options(cluster=makeCluster(4, "SOCK"))
rtna <- tna.shadow(rtna,stepFilter=FALSE)
stopCluster(getOption("cluster"))

## End(Not run)

[Package RTN version 2.8.5 Index]