tna.graph {RTN} | R Documentation |
Extract results from a TNA object and compute a graph.
tna.graph(object, tnet = "dpi", gtype="rmap", minRegulonSize=15, tfs=NULL, amapFilter="quantile", amapCutoff=NULL, ...)
object |
an object of class 'TNA' |
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" and "amap". The "rmap" option returns regulatory maps represented by TFs and targets (regulons) and "amap" computes association maps among regulons (estimates the overlap using the Jaccard Coefficient). |
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. |
... |
additional arguments passed to tna.graph function. |
a graph object.
Mauro Castro
data(tniData) data(tnaData) ## Not run: rtni <- tni.constructor(expData=tniData$expData, regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"), rowAnnotation=tniData$rowAnnotation) rtni <- tni.permutation(rtni) rtni <- tni.bootstrap(rtni) rtni <- tni.dpi.filter(rtni) rtna <- tni2tna.preprocess(rtni, phenotype=tnaData$phenotype, hits=tnaData$hits, phenoIDs=tnaData$phenoIDs) rtna <- tna.mra(rtna) # get the regulatory map g1 <- tna.graph(rtna, tnet="dpi", gtype="rmap", tfs=c("PTTG1","E2F2","FOXM1")) # get the association map g2 <- tna.graph(rtna, tnet="ref", gtype="amap") # option: plot the igraph objects using RedeR #library(RedeR) #rdp <- RedPort() #calld(rdp) #addGraph(rdp,g1) #relax(rdp, ps=TRUE) ## End(Not run)