p.adjust-methods {podkat} | R Documentation

## Adjust p-Value for Multiple Tests

### Description

Given an `AssocTestResultRanges` object, this method adds a metadata column with adjusted p-values.

### Usage

    ## S4 method for signature 'AssocTestResultRanges'
    p.adjust(p, method=p.adjust.methods, n=length(p))

### Arguments

`p` | object of class `AssocTestResultRanges`
`method` | correction method (see `p.adjust.methods`)
`n` | parameter available for consistency with standard `p.adjust` function; ignored in this implementation

### Details

This function is a wrapper around the standard `p.adjust` function from the stats package. It takes the `p.value` metadata column from the `AssocTestResultRanges` object `p`, applies the multiple testing correction method specified as `method` argument. The method returns a copy of `p` with an additional metadata column `p.value.adj` that contains the adjusted p-values. If `p` already contained a metadata column `p.value.adj`, this column is overwritten with the new adjusted p-values.

If `p` also contains a metadata column `p.value.resampled`, multiple testing correction is also applied to resampled p-values. The resulting adjusted p-values are placed in the metadata column `p.value.resampled.adj`.

Note that, for consistency with the standard `p.adjust` function, the default correction method is “holm”.

### Value

an `AssocTestResultRanges` object (see details above)

### Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/podkat

### See Also

`AssocTestResultRanges`, `p.adjust`

### Examples

    ## load genome description
    data(hgA)

    ## partition genome into overlapping windows
    windows <- partitionRegions(hgA)

    ## load genotype data from VCF file
    vcfFile <- system.file("examples/example1.vcf.gz", package="podkat")
    Z <- readGenotypeMatrix(vcfFile)

    ## read phenotype data from CSV file (continous trait + covariates)
    phenoFile <- system.file("examples/example1lin.csv", package="podkat")
    pheno <-read.table(phenoFile, header=TRUE, sep=",")

    ## train null model with all covariates in data frame 'pheno'
    nm.lin <- nullModel(y ~ ., pheno)

    ## perform association test for multiple regions
    res <- assocTest(Z, nm.lin, windows)
    print(res)

    ## perform multiple testing correction
    res.adj <- p.adjust(res, method="BH")
    print(res.adj)

[Package podkat version 1.18.0 Index]