tni.prune: Prune regulons to remove redundant targets for regulon...

In RTN: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons

Description

Uses network pruning methods to compute a 'core' regulon that retains good correlation with original regulon activity.

Usage

tni.prune(object, regulatoryElements = NULL, minRegCor = 0.95, 
tarPriorityMethod = "EC", minPrunedSize = 30, verbose = TRUE, ...)

Arguments

object	a preprocessed object of TNI-class.
regulatoryElements	an optional vector with regulatoryElements identifiers. If NULL, all regulatoryElements are used.
minRegCor	an numeric value between 0 and 1. The minimum correlation between the original activity values for a regulon and the activity after pruning.
tarPriorityMethod	method for prioritizing targets for the target backwards elimination. One of "EC" (expression correlation), "MI" (mutual information) or "TC" (target contribution).
minPrunedSize	a single integer or numeric value specifying the minimum number of elements in a regulon after pruning.
verbose	a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).
...	arguments passed to tni.gsea2

Value

a TNI-class object, with the pruned regulons.

Author(s)

Clarice Groeneveld

See Also

TNI-class tni.gsea2

Examples

data(tniData)

## Not run: 

rtni <- tni.constructor(expData=tniData$expData, 
        regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"), 
        rowAnnotation=tniData$rowAnnotation)
rtni <- tni.permutation(rtni)
rtni <- tni.bootstrap(rtni)
rtni <- tni.dpi.filter(rtni)

# prune the PTTG1 regulon
rtni_pruned <- tni.prune(rtni, "PTTG1", tarPriorityMethod = "TC")

#parallel version with SNOW package!
#library(snow)
#options(cluster=makeCluster(3, "SOCK"))
#rtni_pruned <- tni.prune(rtni, c("PTTG1", "E2F2"))
#stopCluster(getOption("cluster"))


## End(Not run)

RTN documentation built on Nov. 12, 2020, 2:02 a.m.