tni.gsea2 {RTN} | R Documentation

## Compute regulon activity by calling GSEA2 (two-tailed Gene Set Enrichment Analysis) algorithm

### Description

Uses GSEA2 algorithm to compute regulon activity for `TNI-class` objects.

### Usage

    tni.gsea2(object, minRegulonSize=15, sizeFilterMethod="posORneg", scale=FALSE,
    exponent=1, tnet="dpi", regulatoryElements=NULL, samples=NULL, features=NULL, refsamp=NULL,
    log=TRUE, alternative=c("two.sided", "less", "greater"), targetContribution=FALSE,
    additionalData=FALSE, verbose=TRUE, doSizeFilter=NULL)

### Arguments

`object` | a preprocessed object of class 'TNI' `TNI-class`.
`minRegulonSize` | a single integer or numeric value specifying the minimum number of elements in a regulon. Regulons smaller than this number are removed from the analysis.
`sizeFilterMethod` | a single character value specifying the use of the 'minRegulonSize' argument, which is applyed to regulon's positive and negative targets. Options: "posANDneg", "posORneg", "posPLUSneg". For "posANDneg", the number of both positive and negative targets should be > 'minRegulonSize'; for "posORneg", the number of either positive or negative targets should be > 'minRegulonSize'; and for "posPLUSneg", the number of all targets should be > 'minRegulonSize'.
`scale` | A logical value specifying if expression values should be centered and scaled across samples (when verbose=TRUE) or not (when verbose=FALSE).
`exponent` | a single integer or numeric value used in weighting phenotypes in GSEA.
`tnet` | can take values of 'refnet', 'dpi' or 'cdt'. It refers to the version of the regulatory network that will be used for GSEA analysis.
`regulatoryElements` | an optional vector with transcription factor identifiers.
`samples` | an optional string vector containing the sample names for which will be computed the GSEA2.
`features` | a string vector containing features for feature selection.
`refsamp` | an optional string vector containing the names of the reference samples for differential expression calculations. If not provided, then the average of all samples will be used as reference.
`log` | a logical value. If TRUE, it will check whether the expression values are provided as logged data; if not, it will performe a log2 transformation on expression values before the differential expression calculations.
`alternative` | a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
`targetContribution` | This argument is used for internal calls. A single logical value specifying to return the contribution of each target in enrichment scores (when verbose=TRUE) or not (when verbose=FALSE).
`additionalData` | This argument is used for internal calls. A single logical value specifying to return the additional data objects (when verbose=TRUE) or not (when verbose=FALSE).
`verbose` | a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).
`doSizeFilter` | 'doSizeFilter' is deprecated, please use the 'sizeFilterMethod' parameter.

### Value

a list with enrichment scores for all samples in the TNI. The list contains the following elements:

differential:

  A numeric `"matrix"` with differential enrichment scores (dES).

positive:

  A numeric `"matrix"` with enrichment scores from positive targets.

negative:

  A numeric `"matrix"` with enrichment scores from negative targets.

status:

  A numeric `"matrix"` with discretized scores derived from the dES values.

regulatoryElements:

  A character vector listing the regulatory elements assessed by the GSEA-2T algorithm.

sections:

  A single numeric value used in internal plots.

### Author(s)

Mauro Castro

### See Also

`TNI-class` `tna.gsea2` `tna.plot.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)

    #run GSEA2 analysis pipeline
    EScores <- tni.gsea2(rtni)

    #parallel version with SNOW package!
    library(snow)
    options(cluster=snow::makeCluster(3, "SOCK"))
    EScores <- tni.gsea2(rtni)
    stopCluster(getOption("cluster"))

    ## End(Not run)

[Package RTN version 2.11.5 Index]