p.adjust-methods: Adjust p-Value for Multiple Tests
In podkat: Position-Dependent Kernel Association Test

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	## S4 method for signature 'AssocTestResultRanges' p.adjust(p, method=p.adjust.methods, n=length(p))

`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

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”.

an AssocTestResultRanges object (see details above)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

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

AssocTestResultRanges, p.adjust

## 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)