bedr (version 1.0.0)

test.region.similarity: Compare sets of regions via jaccard and relative distance using permutation

Description

Compare sets of regions via jaccard and relative distance using permutation to get an empirical p-value.

Usage

test.region.similarity(
	x,
	y,
	n = 1000,
	stratify.by.chr = FALSE,
	species = "human",
	build = "hg19",
	mask.gaps = FALSE,
	mask.repeats = FALSE,
	check.zero.based = TRUE,
	check.chr = TRUE,
	check.valid = TRUE,
	verbose = TRUE
	)

Arguments

x

first region to be compared. this is the region that is permuted

y

second region to be compared

n

the number of iterations to permute

stratify.by.chr

Should the permutation be happen separetely for each chromosome. That is are chromosomes exchangeable.

species

species

build

the build of the reference

mask.gaps

should the gaps (Ns) in the human reference be ignored as potential start points. This drammatically increases memory and run time but is more appropriate in almost all settings. By default it's off.

mask.repeats

should the repeats from repeatMasker be ignored as potential start points. This drammatically increases memory and run time but is more appropriate in almost all settings. By default it's off.

check.zero.based

should 0 based coordinates be checked

check.chr

should chr prefix be checked

check.valid

should the region be checkded for integerity

verbose

should log messages and checking take place

Value

• A list of results

Details

Iteratively permutes intervals and recalculates jaccard and reldist statistics.

Examples

if (check.binary("bedtools")) {

index <- get.example.regions();

a <- index[[1]];
b <- index[[2]];
a <- bedr(engine = "bedtools", input = list(i = a), method = "sort", params = "");
b <- bedr(engine = "bedtools", input = list(i = b), method = "sort", params = "");

# a simple example
test.region.similarity(a, b, n = 8)

# note you can set the cores available to parallelize
options(cores = 4);
system.time(test.region.similarity(a, b, n = 8));

# a real example comparing the distribution of mRNA vs ncRNA genes in RefSeq
# more core
options(cores = 8);

# load refgene
refgene <- query.ucsc("refGene")
refgene <- refgene[,c("chrom","txStart","txEnd","name","name2","strand")]

# only include canonical chr
chr <- paste0("chr", c(1:22,"X","Y")); 
refgene <- refgene[refgene$chrom
# remove genes with multiple positions
duplicated.gene <- duplicated(refgene$name2) | duplicated(rev(refgene$name2));
refgene <- refgene[!duplicated.gene,];

# only select pr coding genes
refgene.nm <- refgene[grepl("^NM",refgene$name),];
# only select non protein coding rna genes	
refgene.nr <- refgene[grepl("^NR",refgene$name),];

# sort and merge
refgene.nm <- snm(refgene.nm,check.chr = FALSE);
refgene.nr <- snm(refgene.nr,check.chr = FALSE);

test.region.similarity(refgene.nm, refgene.nr, mask.unmapped = TRUE );

option(core = 1)

}