CoRegNet (version 1.8.2)

regulatorInfluence: Regulator Influence, estimating the sample specific activity of Transcription Factors.

Description

Uses a network in the form of a coregnet object to compute regulatory influence to estimate the transcriptional activity of each regulators in each sample of the given expression data.

Usage

regulatorInfluence(object,expData,minTarg = 10,withEvidences=FALSE,addCoregulators=FALSE, is.scaled=FALSE)

Arguments

object

A network in the form of a coregnet object.

expData

An expression data matrix or data.frame.

addCoregulators

Compute influence for coregulators with sufficient number of targets. Default to FALSE.

minTarg

The minimum number of targets for a regulator to be considered for actvity prediction. Default set to 10.

withEvidences

Use only the target genes which are validated by an external validation dataset (ChIP-seq for example). This is only possible if external evidence was added using addEvidences. Default set to False.

is.scaled

Wether the input expression data is scaled, if not it will be.

Value

An N by R matrix with N columns the number of sample in the original expression data and R rows the number of regulators with sufficient targets to compute their influence.The expression data is centered by default but not scaled.

References

Nicolle R, Elati M and Radvanyi F (2012) Network Transformation of Gene Expression for Feature Extraction. In pp 108-113.

See Also

hLICORN and coregnet-class to create the network. addEvidences to add external evidences.

Examples

acts=apply(matrix(rep(letters[1:4],7),nrow=2),2,paste,collapse=" ")[1:13]
reps=apply(matrix(rep(letters[5:8],7),nrow=2),2,paste,collapse=" ")[1:13]
grn=data.frame("Target"= LETTERS[1:26] ,"coact"=c(acts,reps),"corep"= c(reps,acts),"R2"=runif(26),stringsAsFactors=FALSE)
co=coregnet(grn)
samples= paste("S",1:100,sep="")
expression=matrix(rnorm(3400),ncol=100)
dimnames(expression) = list(c(grn$Target,names(regulators(co))),samples)

#Minimum number of targets is adjusted because of the small size of the network
TFA = regulatorInfluence(co,expression,minTarg=4)