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="spearman", nPermutations=1000, pooledNullDistribution=TRUE,
            boxcox=TRUE, parChunks=NULL, 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 specifying the mutual information estimator. One of "pearson", "kendall", or "spearman" (default).
`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).
`boxcox` | a single logical value specifying to use Box-Cox procedure to find a transformation of inferred associations that approaches normality (when boxcox=TRUE) or not (when boxcox=FALSE). Dam et al. (2018) have acknowledged that different RNA-seq normalization methods introduce different biases in co-expression analysis, usually towards positive correlation, possibly affected by read-depth differences between samples and the large abundance of 0 values present in RNA-seq-derived expression matrices. In order to correct this positive correlation bias we suggest using this box-cox correction strategy. See `powerTransform` and `bcPower`.
`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

### References

Dam et al. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2018 Jul 20;19(4):575-592. doi: 10.1093/bib/bbw139.

### See Also

`TNI-class`

### Examples

    data(tniData)

    ## Not run:

    # preprocessing
    rtni <- tni.constructor(expData=tniData$expData,
            regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"),
            rowAnnotation=tniData$rowAnnotation)

    # linear version (set nPermutations >= 1000)
    rtni <- tni.permutation(rtni, nPermutations = 100)

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

    ## End(Not run)

[Package RTN version 2.18.0 Index]