RTN (version 1.10.0)

tni.graph: Compute a graph from TNI objects.

Description

Extract results from a TNI object and compute a graph.

Usage

tni.graph(object, tnet = "dpi", gtype="rmap", minRegulonSize=15, tfs=NULL, amapFilter="quantile", 
amapCutoff=NULL, ntop=NULL, ...)

Arguments

object

an object of class 'TNI' TNI-class.

tnet

a single character value specifying which network information should be used to compute the graph. Options: "ref" and "dpi".

gtype

a single character value specifying the graph type. Options: "rmap", "amap", "mmap" and "mmapDetailed". The "rmap" option returns regulatory maps represented by TFs and targets (regulons); "amap" computes association maps among regulons (estimates the overlap using the Jaccard Coefficient); "mmap" and "mmapDetailed" return modulated maps derived from the tni.conditional function.

minRegulonSize

a single integer or numeric value specifying the minimum number of elements in a regulon. Regulons with fewer than this number are removed from the graph.

tfs

a vector with transcription factor identifiers.

amapFilter

a single character value specifying which method should be used to filter association maps (only when gtype="amap"). Options: "phyper","quantile" and "custom".

amapCutoff

a single numeric value (>=0 and

ntop

when gtype="mmapDetailed", ntop is an optional single integer value (>=1) specifying the number of TF's targets that should be used to compute the modulated map. The n targets is derived from the top ranked TF-target interations, as defined in the mutual information analysis used to construct the regulon set.

...

additional arguments passed to tni.graph function.

Value

a graph object.

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, eps=0.05)
# 
# # compute regulatory maps
# g<-tni.graph(rtni, tnet="dpi", gtype="rmap", tfs=tfs4test)
# 
# # option: plot the igraph object using RedeR
# library(RedeR)
# rdp<-RedPort()
# calld(rdp)
# addGraph(rdp,g)
# addLegend.shape(rdp,g)
# addLegend.color(rdp,g,type="edge")
# relax(rdp,p1=50,p5=20)
# 
# # compute association maps
# resetd(rdp)
# g<-tni.graph(rtni, tnet="ref", gtype="amap", tfs=tfs4test)
# addGraph(rdp,g)
# addLegend.size(rdp,g)
# addLegend.size(rdp,g,type="edge")
# 
# ## End(Not run)