tni.graph {RTN} | R Documentation |
Extract results from a TNI object and compute a graph.
tni.graph(object, tnet = "dpi", gtype="rmap", minRegulonSize=15, tfs=NULL, amapFilter="quantile", amapCutoff=NULL, ntop=NULL, ...)
object |
an object of class 'TNI' |
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 |
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 <=1) specifying the cutoff for the association map filter. When amapFilter="phyper", amapCutoff corresponds to a pvalue cutoff; when amapFilter="quantile", amapCutoff corresponds to a quantile threshold; and when amapFilter="custom", amapCutoff is a JC threshold. |
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. |
a graph object.
Mauro Castro
data(dt4rtn) # select 5 regulatoryElements for a quick demonstration! tfs4test <- dt4rtn$tfs[c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")] ## Not run: rtni <- tni.constructor(expData=dt4rtn$gexp, regulatoryElements=tfs4test, rowAnnotation=dt4rtn$gexpIDs) rtni <- tni.permutation(rtni) rtni <- tni.bootstrap(rtni) rtni <- tni.dpi.filter(rtni, eps=0) # 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=20,p3=50,p4=50,p5=10,p8=50,ps=TRUE) #...it should take some time to relax! # 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)