mCSEATest {mCSEA} | R Documentation

## mCSEA core analysis

### Description

Perform a methylated CpG sites enrichment analysis in predefined genomic regions

### Usage

    mCSEATest(rank, methData, pheno = NULL, column = 1,
      regionsTypes = c("promoters", "genes", "CGI"), customAnnotation = NULL,
      minCpGs = 5, nproc = 1, nperm = 10000, platform = "450k")

### Arguments

`rank` | A named numeric vector with the ranking statistic of each CpG site
`methData` | A data frame or a matrix containing Illumina's CpG probes in rows and samples in columns. A SummarizedExperiment object can be used too
`pheno` | A data frame or a matrix containing samples in rows and covariates in columns. If NULL (default), pheno is extracted from the SummarizedExperiment object
`column` | The column name or number from pheno used to split the samples into groups (first column is used by default)
`regionsTypes` | A character or character vector indicating the predefined regions to be analyzed. NULL to skip this step and use customAnnotation.
`customAnnotation` | An optional list with the CpGs associated to each feature (default = NULL)
`minCpGs` | Minimum number of CpGs associated to a region. Regions below this threshold are not tested
`nproc` | Number of processors to use in GSEA step (default = 1)
`nperm` | Number of permutations to do in GSEA step (default = 10000)
`platform` | Platform used to get the methylation data (450k or EPIC)

### Value

A list with the results of each of the analyzed regions. For each region type, a data frame with the results and a list with the probes associated to each region are generated. In addition, this list also contains the input methData, pheno and platform objects

### Author(s)

Jordi Martorell Marugán, jordi.martorell@genyo.es

### References

Subramanian, A. et al (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles . PNAS 102, 15545-15550.

### See Also

`rankProbes`, `mCSEAPlot`, `mCSEAPlotGSEA`

### Examples

    ## Not run:
    library(mCSEAdata)
    data(mcseadata)
    myRank <- rankProbes(betaTest, phenoTest, refGroup = "Control")
    set.seed(123)
    myResults <- mCSEATest(myRank, betaTest, phenoTest,
    regionsTypes = "promoters", platform = "EPIC")

    ## End(Not run)
    data(precomputedmCSEA)
    head(myResults[["promoters"]])
    head(myResults[["promoters_association"]])

[Package mCSEA version 1.6.0 Index]