tni.bootstrap {RTN} | R Documentation |
This function takes a TNI object and returns the consensus transcriptional network.
tni.bootstrap(object, estimator="pearson", nBootstraps=100, consensus=95, parChunks=10, verbose=TRUE)
object |
a processed object of class 'TNI' |
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) |
a matrix in the slot "results" containing a reference transcriptional network,
see 'tn.ref' option in tni.get
.
Mauro Castro
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)