regulatorInfluence: Regulator Influence, estimating the sample specific activity...

In RemyNicolle/CoRegNet-bioc: CoRegNet : reconstruction and integrated analysis of co-regulatory networks

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.

Author(s)

Remy Nicolle <remy.c.nicolle AT gmail.com>

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)

RemyNicolle/CoRegNet-bioc documentation built on Dec. 18, 2021, 9:57 a.m.