tna.graph {RTN} | R Documentation

## Compute a graph from TNA objects.

### Description

Extract results from a TNA object and compute a graph.

### Usage

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

### Arguments

`object` | an object of class 'TNA' `TNA-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" 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.

### Value

a graph object.

### Author(s)

Mauro Castro

### Examples

    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=0.05)

    #run MRA analysis pipeline
    rtna<-tni2tna.preprocess(rtni, phenotype=dt4rtn$pheno, hits=dt4rtn$hits, phenoIDs=dt4rtn$phenoIDs)
    rtna <- tna.mra(rtna)

    # compute regulatory maps
    g<-tna.graph(rtna, tnet="dpi", gtype="rmap", tfs=tfs4test)

    # option: plot the igraph object using RedeR
    #library(RedeR)
    #rdp<-RedPort()
    #calld(rdp)
    #addGraph(rdp,g)
    #relax(rdp,p1=50,p5=20)

    # compute association maps
    g<-tna.graph(rtna, tnet="ref", gtype="amap", tfs=tfs4test)

    ## End(Not run)

[Package RTN version 2.6.3 Index]