RTN (version 1.10.0)

tni.bootstrap: Inference of 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.

See Also

TNI-class makeCluster

Examples

data(dt4rtn)

# just a few TFs for quick demonstration!
tfs4test<-c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")

# create a new TNI object
rtni <- new("TNI", gexp=dt4rtn$gexp, transcriptionFactors=dt4rtn$tfs[tfs4test])

## Not run: 
# 
# # preprocessing
# rtni <- tni.preprocess(rtni,gexpIDs=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)