tni.prune {RTN} | R Documentation

## Prune regulons to remove redundant targets for regulon activity analysis

### 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)

[Package RTN version 2.10.1 Index]