RTN (version 1.10.0)

tna.shadow: 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.

See Also

TNA-class tna.shadow

Examples

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 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)
# stopCluster(getOption("cluster"))
# ## End(Not run)