tni.permutation {RTN} | R Documentation

## 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`.

### Author(s)

Mauro Castro

### See Also

`TNI-class`

### Examples

    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)

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

    ## End(Not run)

[Package RTN version 2.6.3 Index]