filterResult-methods | R Documentation |
Given an AssocTestResultRanges
object,
this method filters regions according to p-values or variants' contributions.
## S4 method for signature 'AssocTestResultRanges' filterResult(object, cutoff=0.05, filterBy=c("p.value", "p.value.adj", "p.value.resampled", "p.value.resampled.adj")) ## S4 method for signature 'GRanges' filterResult(object, cutoff=0.1) ## S4 method for signature 'GRangesList' filterResult(object, cutoff=0.1)
object |
object of class
|
cutoff |
threshold |
filterBy |
according to which p-value column filtering should be done; the default is “p.value”. |
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.
an object of class AssocTestResultRanges
,
GRanges
, or GRangesList
(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 (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)
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