STATegRa (version 1.6.2)

holistOmics: HolistOmics an application of NPC on omics datasets

Description

This function applies the NonParametric Combination methodology on the integrative analysis of different omics data modalities. It retrieves diffential expressed genes between control and treatment, taking into account all omics data. First, each datatype is analyzed independently using the appropriate method. HolistOmics analyses static (one time point) RNAseq, using Voom+limma method, and static (one time point) Microarray, using limma. Then, the resulting p-values are combined employing Fisher, Liptak and Tippett combining functions. Tippett function returns findings which are supported by at least one omics modality. Liptak function returns findings which are supportd by most modalities. Fisher function has an intermediate behavior between those of Tippett and Liptak.

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

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)