RTN (version 1.10.0)

tni.permutation: Inference of transcriptional networks.

Description

This function takes a TNI object and returns a transcriptional network inferred by mutual information (with multiple hypothesis testing corrections).

Usage

tni.permutation(object, pValueCutoff=0.01, pAdjustMethod="BH", globalAdjustment=TRUE, estimator="pearson",nPermutations=1000, pooledNullDistribution=TRUE,  parChunks=50, verbose=TRUE)

Arguments

object

a preprocessed object of class 'TNI' TNI-class.

pValueCutoff

a single numeric value specifying the cutoff for p-values considered significant.

pAdjustMethod

a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details).

globalAdjustment

a single logical value specifying to run global p.value adjustments (when globalAdjustment=TRUE) or not (when globalAdjustment=FALSE).

estimator

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

nPermutations

a single integer value specifying the number of permutations for deriving TF-target p-values in the mutual information analysis. If running in parallel, nPermutations should be greater and multiple of parChunks.

pooledNullDistribution

a single logical value specifying to run the permutation analysis with pooled regulons (when pooledNullDistribution=TRUE) or not (when pooledNullDistribution=FALSE).

parChunks

an optional single integer value specifying the number of permutation chunks to be used in the parallel analysis (effective only for "pooledNullDistribution = TRUE").

verbose

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

Value

a mutual information 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)
# 
# # parallel version with SNOW package!
# library(snow)
# options(cluster=makeCluster(3, "SOCK"))
# rtni<-tni.permutation(rtni)
# stopCluster(getOption("cluster"))
# ## End(Not run)