snpEnrichment (version 1.2-3)

reSample: Compute enrichment analysis on an Enrichment object

Description

After initFiles and readEnrichment has been run. reSample computes a statistic value and a p-value for each chromosomes and for the whole genome.

Usage

## S3 method for class 'Enrichment':
reSample(object, nSample = 100, 
         MAFpool = c(0.05, 0.10, 0.2, 0.3, 0.4, 0.5),
         mc.cores = 1, onlyGenome = FALSE)
## S3 method for class 'Chromosome':
reSample(object, nSample = 100, sigThresh = 0.05, 
         MAFpool = c(0.05, 0.10, 0.2, 0.3, 0.4, 0.5),
         extendMethod = "ld", mc.cores = 1)

Arguments

object

[Enrichment or Chromosome]: an object to be updated. It is intended, an object returned by the readEnrichment function.

nSample

[numeric]: the number of resampling done by reSample for p-values computation (minimum is 100).

sigThresh

[numeric]: statistical threshold for signal (e.g. sigThresh = 0.05 for a given GWAS signal) used to compute an Enrichment Ratio.

MAFpool

[vector(numeric)]: either a numeric vector giving the breaks points of intervals into which SNP's MAF (Minor Allele Frequency) is to be split.

extendMethod

[character]: character string naming the method to be used to extend the list of eSNPs. Default is "ld", the list of eSNPs is extended with all SNPs in LD (r^2 threshold, defined in writeLD). Other

mc.cores

[numeric]: The number of cores to use (default is mc.cores=1), i.e. at most how many child processes will be run simultaneously. Must be at least one, and parallelization requires at least two cores.

onlyGenome

[logical]: onlyGenome=FALSE (default) compute resampling step for all chromosomes.

Value

• Return the object given in argument, updated by the resampling results.

See Also

Overview : snpEnrichment-package Classes : Enrichment, Chromosome, EnrichSNP Methods : plot, reSample, excludeSNP, compareEnrichment, enrichment, is.enrichment, chromosome, is.chromosome Functions : initFiles, writeLD, readEnrichment

Examples

data(toyEnrichmentM1)
reSample(object = toyEnrichmentM1, 
         nSample = 10, 
         MAFpool = c(0.05, 0.10, 0.2, 0.3, 0.4, 0.5), 
         onlyGenome = FALSE)
toyEnrichmentM1