Package 'geneAttribution'

geneAttribution: Identification of candidate genes associated with noncoding genetic variation

Description

Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in UCSC .bed file format. A typical workflow requires loading gene models and empirical data, then running geneAttribution() on the locus of interest

Given genomic coordinate, return normalized probability for each gene

Usage

geneAttribution(chr, pos, geneCoordinates, empiricalData, lambda = 7.61e-06,
  maxDist = 1e+06, minPP = 0.01)

Arguments

chr	A character string representing a chromosome (e.g. "chr2")
pos	An integer representing a genomic position in the same genome build that gene models
geneCoordinates	A GenomicRanges object, as generated by geneModels()
empiricalData	A list of GenomicRanges objects, as generated by loadBed(). Optional
lambda	Float. Variable for exponential distribution. Default based on empirical eQTL data from multiple tissues. Optional
maxDist	Integer. Only genes within this distance of query locus are considered. Optional
minPP	Float. Genes with a posterior probability < minPP are lumped as "Other". Can be set to 0 when all genes should be reported. Optional

Value

A sorted, numeric vector of normalized gene probabilities

Examples

geneLocs <- geneModels()
fileName <- system.file("extdata", "eqtlHaplotypeBlocks.b38.bed", package="geneAttribution")
empirical <- loadBed(fileName)
geneAttribution("chr2", 127156000, geneLocs, empirical)

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Load gene models

Description

Get gene models as a GenomicRanges object, with gene names in the symbol column For hg19, you may want to use TxDb.Hsapiens.UCSC.hg19.knownGene and for GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene (set as default)

Usage

geneModels(txdb = TxDb.Hsapiens.UCSC.hg38.knownGene::TxDb.Hsapiens.UCSC.hg38.knownGene,
  maxGeneLength = 1e+06, genesToInclude, genesToExclude)

Arguments

txdb	GenomicFeatures TxDb object containing genomic coordinates of genes
maxGeneLength	An integer. Gene models that are longer than this are excluded. Optional
genesToInclude	A character vector of gene symbols of genes to include (e.g. only protein coding genes). Optional
genesToExclude	A character vector of gene symbols of genes to exclude. Optional

Value

A GenomicRanges object containing human gene models

Examples

geneModels()
geneModels(genesToInclude = c("CYYR1", "ADAMTS1", "ADAMTS5", "N6AMT1", "LTN1"))

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Load UCSC *.BED files containing empirical data

Description

Required *.BED file format (tab-separated): chr start end name (optional column: score). Sample files supplied with package are limited to chromosome 2.

Usage

loadBed(files, weights)

Arguments

files	A character vector containing *.BED file names
weights	An integer vector containing weighting for each bed file. Optional

Value

A list of GenomicRanges objects containing the data from the *.BED files, with weightings in the score column

Examples

fileName1 <- system.file("extdata", "hiCRegions.b38.bed", package="geneAttribution")
fileName2 <- system.file("extdata", "eqtlHaplotypeBlocks.b38.bed", package="geneAttribution")
loadBed(c(fileName1, fileName2), c(2, 5))

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Normalize likelihoods and return probabilities

Description

Normalize likelihoods and return probabilities

Usage

normP(pVector, minPP = 0)

Arguments

pVector	A numeric vector of pre-normalization likelihoods
minPP	A float. Genes with a posterior probability < minPP are lumped as "Other". Optional

Value

A sorted, numeric vector of normalized probabilities

Examples

normP(c(5, 1, 1, 1, 1, 1, 0.1))
normP(c(5, 1, 1, 1, 1, 1, 0.1), minPP=0.1)

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