tni.prune {RTN} | R Documentation |
Uses network pruning methods to compute a 'core' regulon that retains good correlation with original regulon activity.
tni.prune(object, regulatoryElements = NULL, minRegCor = 0.95, tarPriorityMethod = "EC", minPrunedSize = 30, verbose = TRUE, ...)
object |
a preprocessed object of |
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 |
a TNI-class object, with the pruned regulons.
Clarice Groeneveld
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)