Permutation Test for Overlap

Description

Performs a permutation test to see if there is an association in overlap between a region set A and a region set B creating random regions through the genome.

Usage

overlapPermTest (A, B, alternative="auto", ...)

Arguments

Value

A list of class permTestResults containing the following components:

• pval the p-value of the test.

• ntimes the number of permutations.

• alternative a character string describing the alternative hypotesis.

• observed the value of the statistic for the original data set.

• permuted the values of the statistic for each permuted data set.

• zscore the value of the standard score. (observed-mean(permuted))/sd(permuted)

See Also

overlapGraphicalSummary, overlapRegions, toDataframe, toGRanges, permTest

Examples

genome <- filterChromosomes(getGenome("hg19"), keep.chr="chr1")
A <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=20000, genome=genome, non.overlapping=FALSE) 
B <- c(A, createRandomRegions(nregions=10, length.mean=10000, length.sd=20000, genome=genome, non.overlapping=FALSE))

pt <- overlapPermTest(A=A, B=B, ntimes=10, genome=genome, non.overlapping=FALSE, verbose=TRUE)
summary(pt)
plot(pt)
plot(pt, plotType="Tailed")  
 

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(regioneR)
Loading required package: memoise
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: BSgenome
Loading required package: Biostrings
Loading required package: XVector
Loading required package: rtracklayer
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/regioneR/overlapPermTest.Rd_%03d_medium.png", width=480, height=480)
> ### Name: overlapPermTest
> ### Title: Permutation Test for Overlap
> ### Aliases: overlapPermTest
> 
> ### ** Examples
> 
> genome <- filterChromosomes(getGenome("hg19"), keep.chr="chr1")
> A <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=20000, genome=genome, non.overlapping=FALSE) 
> B <- c(A, createRandomRegions(nregions=10, length.mean=10000, length.sd=20000, genome=genome, non.overlapping=FALSE))
> 
> pt <- overlapPermTest(A=A, B=B, ntimes=10, genome=genome, non.overlapping=FALSE, verbose=TRUE)
[1] "Note: The minimum p-value with only 10 permutations is 0.0909090909090909. You should consider increasing the number of permutations."
   |                                                                               |                                                                      |   0%   |                                                                               |=======                                                               |  10%   |                                                                               |==============                                                        |  20%   |                                                                               |=====================                                                 |  30%   |                                                                               |============================                                          |  40%   |                                                                               |===================================                                   |  50%   |                                                                               |==========================================                            |  60%   |                                                                               |=================================================                     |  70%   |                                                                               |========================================================              |  80%   |                                                                               |===============================================================       |  90%   |                                                                               |======================================================================| 100%> summary(pt)
Permutation tests: 1
Significant permutation tests: 0
Iterations: 10
Randomization Function: randomizeRegions
Tests Results:
               pvalue zscore    test
numOverlaps 0.2727273 1.1177 greater
> plot(pt)
> plot(pt, plotType="Tailed")  
>  
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>