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
https://github.com/UBod/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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