podkat (version 1.4.2)

filterResult-methods: Filter Association Test Results According to p-Values or Variants' Contributions

Description

Given an AssocTestResultRanges object, this method filters regions according to p-values or variants' contributions.

Usage

## S3 method for class 'AssocTestResultRanges':
filterResult(object, cutoff=0.05,
             filterBy=c("p.value", "p.value.adj", "p.value.resampled",
             "p.value.resampled.adj"))
## S3 method for class 'GRanges':
filterResult(object, cutoff=0.1)
## S3 method for class 'GRangesList':
filterResult(object, cutoff=0.1)

Arguments

object

object of class AssocTestResultRanges, GRanges, or GRangesList

cutoff

threshold

filterBy

according to which p-value column filtering should be done; the default is p.value.

Value

• an object of class AssocTestResultRanges, GRanges, or GRangesList (see details above)

Details

If called for an AssocTestResultRanges object as first argument, this method strips off all regions the p-values of which exceed the threshold cutoff. By default, this filtering is applied to raw p-values (metadata column p.value). The filterBy argument allows for performing filtering on any of the other three p-value metadata columns (if available, otherwise the method quits with an error).

If called for a GRanges object as first argument, this method checks if the first argument object has a metadata column named weight.contribution. If it exists, it returns a GRanges object with the elements of object that have a value greater than cutoff in the weight.contribution metadata column. If this metadata column does not exist, the method quits with an error.

If called for a GRangesList object as first argument object, this method applies the filterResult method for each of its list components and returns a GRangesList object. If any of the components of object does not have a metadata column named weight.contribution, the method quits with an error.

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 (continuous 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)
res.adj <- p.adjust(res, method="BH")

## show filtered results
res.f <- filterResult(res.adj)
print(res.f)
res.f <- filterResult(res.adj, filterBy="p.value.adj")
print(res.f)

## compute contributions
contrib <- weights(res.f, Z, nm.lin)
contrib

## extract most indicative variants
filterResult(contrib[[1]])
filterResult(contrib)