tni.bootstrap {RTN}R Documentation

Inference of consensus transcriptional networks.

Description

This function takes a TNI object and returns the consensus transcriptional network.

Usage

tni.bootstrap(object, estimator="pearson", nBootstraps=100, consensus=95, parChunks=10, verbose=TRUE)

Arguments

object

a processed object of class 'TNI' TNI-class evaluated by the method tni.permutation.

estimator

a character string indicating which estimator to be used for mutual information computation. One of "pearson" (default), "kendall", or "spearman", can be abbreviated.

nBootstraps

a single integer or numeric value specifying the number of bootstraps for deriving a consensus between every TF-target association inferred in the mutual information analysis. If running in parallel, nBootstraps should be greater and multiple of parChunks.

consensus

a single integer or numeric value specifying the consensus fraction (in percentage) under which a TF-target association is accepted.

parChunks

an optional single integer value specifying the number of bootstrap chunks to be used in the parallel analysis.

verbose

a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE)

Value

a matrix in the slot "results" containing a reference transcriptional network, see 'tn.ref' option in tni.get.

Author(s)

Mauro Castro

See Also

TNI-class

Examples


data(dt4rtn)

# select 5 regulatoryElements for a quick demonstration!
tfs4test <- dt4rtn$tfs[c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")]

## Not run: 

# preprocessing
rtni <- tni.constructor(expData=dt4rtn$gexp, regulatoryElements=tfs4test, 
        rowAnnotation=dt4rtn$gexpIDs)

# linear version!
rtni<-tni.permutation(rtni)
rtni<-tni.bootstrap(rtni)

# parallel version with SNOW package!
library(snow)
options(cluster=makeCluster(3, "SOCK"))
rtni<-tni.permutation(rtni)
rtni<-tni.bootstrap(rtni)
stopCluster(getOption("cluster"))

## End(Not run)

[Package RTN version 2.4.6 Index]