holistOmics {STATegRa} | R Documentation

## HolistOmics an application of NPC on omics datasets

### Description

This function is defunct. Use omicsNPC instead.

### Usage

    holistOmics(dataInput, dataTypes, comb.method = c("Fisher", "Liptak", "Tippett"),
                   numPerm = 1000, numCores = 1, verbose = FALSE)

### Arguments

`dataInput` | List of ExpressionSet objects, one for each data modality.
`dataTypes` | Character vector with possible values: 'RNA-seq', 'microarray'
`comb.method` | Character vector with possible values: 'Fisher', 'Liptak', 'Tippett', if more than one is specified, all will be used.
`numPerm` | Number of permutations
`numCores` | Number of CPU cores to use
`verbose` | Logical, if set to TRUE holistOmics prints out the step that it performs

### Value

A data.frame

### Author(s)

Nestoras Karathanasis

### References

Pesarin, Fortunato, and Luigi Salmaso. Permutation tests for complex data: theory, applications and software. John Wiley & Sons, 2010.

### Examples

    # Load the data
    data("TCGA_BRCA_Batch_93")
    # Setting dataTypes, the first two ExpressionSets include RNAseq data,
    # the third ExpressionSet includes Microarray data.
    dataTypes <- c("RNAseq", "RNAseq", "Microarray")
    # Setting methods to combine pvalues
    comb.method = c("Fisher", "Liptak", "Tippett")
    # Setting number of permutations
    numPerm = 1000
    # Setting number of cores
    numCores = 1
    # Setting holistOmics to print out the steps that it performs.
    verbose = TRUE
    # Run holistOmics analysis.
    # The output is a data.frame of p-values.
    # Each row corresponds to a gene name. Each column corresponds to a method
    # used in the analysis.
    ## Not run: out <- holistOmics(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes,
                                comb.method = comb.method, numPerm = numPerm,
                                numCores = numCores, verbose = verbose)
    ## End(Not run)

[Package STATegRa version 1.20.0 Index]