| Title: | Association analysis of genomic regions based on permutation tests |
|---|---|
| Description: | regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. |
| Authors: | Anna Diez-Villanueva <[email protected]>, Roberto Malinverni <[email protected]> and Bernat Gel <[email protected]> |
| Maintainer: | Bernat Gel <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.37.0 |
| Built: | 2024-09-21 04:56:39 UTC |
| Source: | https://github.com/bioc/regioneR |
Given a character string with the "name" of a genome, it returns a BSgenome object if available.
characterToBSGenome(genome.name)characterToBSGenome(genome.name)
genome.name |
a character string uniquely identifying a |
A BSgenome object
This function is memoised (cached) using the memoise package. To empty the cache, use forget(charecterToBSGenome)
getGenomeAndMask, maskFromBSGenome
g <- characterToBSGenome("hg19")g <- characterToBSGenome("hg19")
Given a set of regions A and a genome, this function returns a new set of regions created by applying a random spin to each chromosome.
circularRandomizeRegions(A, genome="hg19", mask=NULL, max.mask.overlap=NULL, max.retries=10, verbose=TRUE, ...)circularRandomizeRegions(A, genome="hg19", mask=NULL, max.mask.overlap=NULL, max.retries=10, verbose=TRUE, ...)
A |
The set of regions to randomize. A region set in any of the accepted formats by |
genome |
The reference genome to use. A valid genome object. Either a |
mask |
The set of regions specifying where a random region can not be (centromeres, repetitive regions, unmappable regions...). A region set in any of the accepted formats by |
max.mask.overlap |
numeric value |
max.retries |
numeric value |
verbose |
a boolean. |
... |
further arguments to be passed to or from methods. |
This randomization strategy is useful when the spatial relation between the regions in the RS is important and has to be conserved.
It returns a GenomicRanges object with the regions resulting from the randomization process.
randomizeRegions, toDataframe, toGRanges, getGenome, getMask, getGenomeAndMask, characterToBSGenome, maskFromBSGenome, resampleRegions, createRandomRegions
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) circularRandomizeRegions(A) circularRandomizeRegions(A, genome=genome, mask=mask, per.chromosome=TRUE, non.overlapping=TRUE)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) circularRandomizeRegions(A) circularRandomizeRegions(A, genome=genome, mask=mask, per.chromosome=TRUE, non.overlapping=TRUE)
Returns the regions that are common in two region sets, its intersection.
commonRegions(A, B)commonRegions(A, B)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
It returns a GenomicRanges object with the regions present in both region sets.
All metadata (additional columns in the region set in addition to chromosome, start and end) will be ignored and not present in the returned region set.
plotRegions, toDataframe, toGRanges, subtractRegions, splitRegions, extendRegions, joinRegions, mergeRegions, overlapRegions
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) commons <- commonRegions(A, B) plotRegions(list(A, B, commons), chromosome="chr1", regions.labels=c("A", "B", "common"), regions.colors=3:1)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) commons <- commonRegions(A, B) plotRegions(list(A, B, commons), chromosome="chr1", regions.labels=c("A", "B", "common"), regions.colors=3:1)
Partially applies (the standard Curry function in functional programming) a list of arguments to a function and returns a list of preapplied functions. The result of this function is a list of functions suitable for the multiple evaluation functions in permTest.
createFunctionsList(FUN, param.name, values, func.names)createFunctionsList(FUN, param.name, values, func.names)
FUN |
Function. the function to be partially applied |
param.name |
Character. The name of the parameter to pre-set. |
values |
A list or vector of values to preassign. A function will be created for each of the values in values. If present, the names of the list will be the names of the functions. |
func.names |
Character. The names of the functions created. Useful to identify the functions created. Defaults to the names of the values list or to Function1, Function2... if the values list has no names. |
It returns a list of functions with parameter param.value pre-set to values.
It uses the code posted by "hadley" at http://stackoverflow.com/questions/6547219/how-to-bind-function-arguments
f <- function(a, b) { return(a+b) } funcs <- createFunctionsList(FUN=f, param.name="b", values=c(1,2,3), func.names=c("plusone", "plustwo", "plusthree")) funcs$plusone(2) funcs$plusone(10) funcs$plusthree(2) A <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=0, mask=NA) B <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=0, mask=NA) overlapsWith <- createFunctionsList(FUN=numOverlaps, param.name="B", values=list(a=A, b=B)) overlapsWith$a(A=A) overlapsWith$b(A=A)f <- function(a, b) { return(a+b) } funcs <- createFunctionsList(FUN=f, param.name="b", values=c(1,2,3), func.names=c("plusone", "plustwo", "plusthree")) funcs$plusone(2) funcs$plusone(10) funcs$plusthree(2) A <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=0, mask=NA) B <- createRandomRegions(nregions=20, length.mean=10000000, length.sd=0, mask=NA) overlapsWith <- createFunctionsList(FUN=numOverlaps, param.name="B", values=list(a=A, b=B)) overlapsWith$a(A=A) overlapsWith$b(A=A)
Creates a set of random regions with a given mean size and standard deviation.
createRandomRegions(nregions=100, length.mean=250, length.sd=20, genome="hg19", mask=NULL, non.overlapping=TRUE)createRandomRegions(nregions=100, length.mean=250, length.sd=20, genome="hg19", mask=NULL, non.overlapping=TRUE)
nregions |
The number of regions to be created. |
length.mean |
The mean size of the regions created. This is not guaranteed to be the mean of the final region set. See note. |
length.sd |
The standard deviation of the region size. This is not guaranteed to be the standard deviation of the final region set. See note. |
genome |
The reference genome to use. A valid genome object. Either a |
mask |
The set of regions specifying where a random region can not be (centromeres, repetitive regions, unmappable regions...). A region set in any of the accepted formats ( |
non.overlapping |
A boolean stating whether the random regions can overlap (FALSE) or not (TRUE). |
A set of nregions will be created and randomly placed over the genome. The lengths of the region set will follow a normal distribution with a mean size length.mean and a standard deviation length.sd. The new regions can be made explicitly non overlapping by setting non.overlapping to TRUE.
A mask can be provided so no regions fall in a forbidden part of the genome.
It returns a GenomicRanges object with the regions resulting from the randomization process.
If the standard deviation of the length is large with respect to the mean, negative lengths might be created. These region lengths will be transfromed to into a 1 and so the, for large standard deviations the mean and sd of the lengths are not guaranteed to be the ones in the parameters.
getGenome, getMask, getGenomeAndMask, characterToBSGenome, maskFromBSGenome, randomizeRegions, resampleRegions
genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) createRandomRegions(nregions=10, length.mean=1000, length.sd=500) createRandomRegions(nregions=10, genome=genome, mask=mask, non.overlapping=TRUE)genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) createRandomRegions(nregions=10, length.mean=1000, length.sd=500) createRandomRegions(nregions=10, genome=genome, mask=mask, non.overlapping=TRUE)
Empties the caches used by the memoised functions in the regioneR package.
emptyCacheRegioneR()emptyCacheRegioneR()
The cache is emptied
emptyCacheRegioneR()emptyCacheRegioneR()
Extends the regions a number of bases at each end. Negative numbers will reduce the region instead of enlarging it.
extendRegions(A, extend.start=0, extend.end=0)extendRegions(A, extend.start=0, extend.end=0)
A |
a region set in any of the accepted formats by |
extend.start |
an integer. The number of bases to be subtracted from the start of the region. |
extend.end |
an integer. The number of bases to be added at the end of the region. |
a GenomicRanges object with the extended regions.
If negative values are provided and the new extremes are "flipped", the function will fail. It does not check if the extended regions fit into the genome.
plotRegions, toDataframe, toGRanges, subtractRegions, splitRegions, overlapRegions, commonRegions, mergeRegions, joinRegions
A <- data.frame("chr1", c(10, 20, 30), c(13, 28, 40)) extend1 <- extendRegions(A, extend.start=5, extend.end=2) extend2 <- extendRegions(A, extend.start=15) extend3 <- extendRegions(A, extend.start=-1) plotRegions(list(A, extend1, extend2, extend3), chromosome="chr1", regions.labels=c("A", "extend1", "extend2", "extend3"), regions.colors=4:1)A <- data.frame("chr1", c(10, 20, 30), c(13, 28, 40)) extend1 <- extendRegions(A, extend.start=5, extend.end=2) extend2 <- extendRegions(A, extend.start=15) extend3 <- extendRegions(A, extend.start=-1) plotRegions(list(A, extend1, extend2, extend3), chromosome="chr1", regions.labels=c("A", "extend1", "extend2", "extend3"), regions.colors=4:1)
Filters the chromosomes in a region set. It can either filter using a predefined chromosome set (e.g. "autosomal chromosomes in Homo sapiens") or using a custom chromosome set (e.g. only chromosomes "chr22" and "chrX")
filterChromosomes(A, organism="hg", chr.type="canonical", keep.chr=NULL)filterChromosomes(A, organism="hg", chr.type="canonical", keep.chr=NULL)
A |
a region set in any of the formats accepted by |
organism |
a character indicating the organism from which to get the predefined chromosome sets. It can be the organism code as used in |
chr.type |
a character indicating the specific chromosome set to be used. Usually "autosomal" or "canonical", althought other values could be available for certain organisms. |
keep.chr |
is a character vector stating the names of the chromosomes to keep. Any chromosome not in the vector will be filtered out. If keep.chr is supplied, organism and chr.type are ignored. |
A GRanges object containing only the regions in the original region set belonging to the selected chromosomes. All regions in non selected chromosomes are removed.
getGenomeAndMask, listChrTypes getChromosomesByOrganism
g <- getGenomeAndMask("hg19")$genome listChrTypes() g <- filterChromosomes(g, chr.type="autosomal", organism="hg19") g <- filterChromosomes(g, keep.chr=c("chr1", "chr2", "chr3"))g <- getGenomeAndMask("hg19")$genome listChrTypes() g <- filterChromosomes(g, chr.type="autosomal", organism="hg19") g <- filterChromosomes(g, keep.chr=c("chr1", "chr2", "chr3"))
Function to obtain a list of organisms with their canonical and (when applicable) the autosomal chromosome names. This function is not usually used by the end user directly but through the filterChromosomes function.
getChromosomesByOrganism()getChromosomesByOrganism()
a list with the organism as keys and the list of available chromosome sets as values
chrsByOrg <- getChromosomesByOrganism() chrsByOrg[["hg"]] chrsByOrg[["hg"]][["autosomal"]]chrsByOrg <- getChromosomesByOrganism() chrsByOrg[["hg"]] chrsByOrg[["hg"]][["autosomal"]]
Function to obtain a genome
getGenome(genome)getGenome(genome)
genome |
The genome object or genome identifier. |
If genome is a BSgenome (from the package BioStrings), it will transform it into a GRanges with chromosomes and chromosome lengths.
If genome is a data.frame with 3 columns, it will transform it into a GRanges.
If genome is a data.frame with 2 columns, it will assume the first is the chromosome, the second is the length of the chromosomes and will add 1 as start.
If genome is a character string uniquely identifying a BSgenome installed in the system (e.g. "hg19", "mm10",... but not "hg"), it will create a genome based on the BSgenome object identified by the character string.
If genome is a GRanges object, it will return it as is.
If genome is non of the above, it will give a warning and try to transform it into a GRanges using toGRanges. This can be helpful if genome is a connection to a file.
A GRanges object with the "genome" data c(Chromosome, Start (by default, 1), Chromosome Length) given a BSgenome, a genome name, a data.frame or a GRanges.
A GRanges representing the genome with one region per chromosome.
This function is memoised (cached) using the memoise package. To empty the cache, use forget(getGenome)
Please note that passing this function the path to a file will not work, since it will assume the character is the identifier of a genome. To read the genome
from a file, please use getGenome(toGRanges("path/to/file"))
getMask, getGenomeAndMask, characterToBSGenome, maskFromBSGenome, emptyCacheRegioneR
getGenome("hg19") getGenome(data.frame(c("chrA", "chrB"), c(15000000, 10000000)))getGenome("hg19") getGenome(data.frame(c("chrA", "chrB"), c(15000000, 10000000)))
Function to obtain a valid genome and mask pair given a valid genome identifier and optionally a mask.
If the genome is not a BSgenome object or a character string uniquely identifying a BSgenome package installed, it will return the genome "as is". If a mask is provided, it will simply return it. Otherwise it will return the mask returned by getMask(genome) or an empty mask if genome is not a valid BSgenome or BSgenome identifier.
getGenomeAndMask(genome, mask=NULL)getGenomeAndMask(genome, mask=NULL)
genome |
the genome object or genome identifier. |
mask |
the mask of the genome in a valid RS format (data.frame, GRanges, BED-like file...). If mask is |
A list with two elements: genome and mask. Genome and mask are GRanges objects.
This function is memoised (cached) using the memoise package. To empty the cache, use forget(getGenomeAndMask)
getMask, getGenome, characterToBSGenome, maskFromBSGenome, emptyCacheRegioneR
getGenomeAndMask("hg19", mask=NA) getGenomeAndMask(genome=data.frame(c("chrA", "chrB"), c(15000000, 10000000)), mask=NA)getGenomeAndMask("hg19", mask=NA) getGenomeAndMask(genome=data.frame(c("chrA", "chrB"), c(15000000, 10000000)), mask=NA)
Function to obtain a mask given a genome available as a BSgenome. The mask returned is the merge of all the active masks in the BSgenome.
Since it uses characterToBSGenome, the genome can be either a BSgenome object or a character string uniquely identifying the a BSgenome object installed.
getMask(genome)getMask(genome)
genome |
the genome from where the mask will be extracted. It can be either a |
A GRanges object with the genomic regions to be masked out
This function is memoised (cached) using the memoise package. To empty the cache, use forget(getMask)
getGenome, getGenomeAndMask, characterToBSGenome, maskFromBSGenome, emptyCacheRegioneR
hg19.mask <- getMask("hg19") hg19.maskhg19.mask <- getMask("hg19") hg19.mask
Joins the regions from a region set A that are less than min.dist bases apart.
joinRegions(A, min.dist=1)joinRegions(A, min.dist=1)
A |
a region set in any of the accepted formats by |
min.dist |
an integer indicating the minimum distance required between two regions in order to not fuse them. Any pair of regions closer than |
It returns a GenomicRanges object with the regions resulting from the joining process.
All metadata (additional columns in the region set in addition to chromosome, start and end) will be ignored and not present in the returned region set.
The implementation relies completely in the reduce function from IRanges package.
plotRegions, toDataframe, toGRanges, subtractRegions, splitRegions, extendRegions, commonRegions, mergeRegions, overlapRegions
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) join1 <- joinRegions(A) join2 <- joinRegions(A, min.dist=3) join3 <- joinRegions(A, min.dist=10) plotRegions(list(A, join1, join2, join3), chromosome="chr1", regions.labels=c("A", "join1", "join2", "join3"), regions.colors=4:1)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) join1 <- joinRegions(A) join2 <- joinRegions(A, min.dist=3) join3 <- joinRegions(A, min.dist=10) plotRegions(list(A, join1, join2, join3), chromosome="chr1", regions.labels=c("A", "join1", "join2", "join3"), regions.colors=4:1)
Prints a list of the available organisms and chromosomes sets in the predefined chromosomes sets information.
listChrTypes()listChrTypes()
the list of available chrs and organisms is printed
filterChromosomes, getChromosomesByOrganism
g <- getGenomeAndMask("hg19")$genome listChrTypes() g <- filterChromosomes(g, chr.type="autosomal", organism="hg19")g <- getGenomeAndMask("hg19")$genome listChrTypes() g <- filterChromosomes(g, chr.type="autosomal", organism="hg19")
Evaluates tthe variation of the z-score in the vicinty of the original region set
localZScore(A, pt, window, step, ...)localZScore(A, pt, window, step, ...)
A |
a region set in any of the formats accepted by |
pt |
a permTestResult object |
window |
a window in wich the local Z-score will be calculated (bp) |
step |
the number of bp that divide each Z-score evaluation |
... |
further arguments to be passed to other methods. |
It returns a local z-score object
genome <- filterChromosomes(getGenome("hg19"), keep.chr="chr1") A <- createRandomRegions(nregions=20, length.mean=10000, 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) plot(pt) lz <- localZScore(A=A, B=B, pt=pt) plot(lz) pt2 <- permTest(A=A, B=B, ntimes=10, randomize.function=randomizeRegions, evaluate.function=list(overlap=numOverlaps, distance=meanDistance), genome=genome, non.overlapping=FALSE) plot(pt2) lz2 <- localZScore(A=A, B=B, pt2) plot(lz2)genome <- filterChromosomes(getGenome("hg19"), keep.chr="chr1") A <- createRandomRegions(nregions=20, length.mean=10000, 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) plot(pt) lz <- localZScore(A=A, B=B, pt=pt) plot(lz) pt2 <- permTest(A=A, B=B, ntimes=10, randomize.function=randomizeRegions, evaluate.function=list(overlap=numOverlaps, distance=meanDistance), genome=genome, non.overlapping=FALSE) plot(pt2) lz2 <- localZScore(A=A, B=B, pt2) plot(lz2)
Extracts the merge of all the active masks from a BSgenome
maskFromBSGenome(bsgenome)maskFromBSGenome(bsgenome)
bsgenome |
a |
A GRanges object with the active mask in the BSgenome
This function is memoised (cached) using the memoise package. To empty the cache, use forget(maskFromBSGenome)
getGenomeAndMask, characterToBSGenome, emptyCacheRegioneR
g <- characterToBSGenome("hg19") maskFromBSGenome(g)g <- characterToBSGenome("hg19") maskFromBSGenome(g)
Computes the mean distance of regions in A to the nearest element in B
meanDistance(A, B, ...)meanDistance(A, B, ...)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
... |
any additional parameter needed |
The mean of the distances of each region in A to the nearest region in B.
If a region in A is in a chromosome where no B region is, it will be ignored and removed from the mean computation.
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) meanDistance(A, B)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) meanDistance(A, B)
Returns the mean of a value defined by a region set over another set of regions.
meanInRegions(A, x, col.name=NULL, ...)meanInRegions(A, x, col.name=NULL, ...)
A |
a region set in any of the accepted formats by |
x |
a region set in any of the accepted formats with an additional column with a value associated to every region. Regions in |
col.name |
character indicating the name of the column. If NULL and if a column with the name "value" exist, it will be used. The 4th column will be used otherwise (or the 5th if 4th is the strand). |
... |
any additional parameter needed |
It returns a numeric value that is the weighted mean of "value" defined in x over the regions in A. That is, the mean of the value of all
regions in x overlapping each region in A weighted according to the number of bases overlapping.
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) positions <- sample(1:40,30) x <- data.frame("chr1", positions, positions, rnorm(30,4,1)) meanInRegions(A, x) x <- GRanges(seqnames=x[,1],ranges=IRanges(x[,2],end=x[,2]),mcols=x[,3]) meanInRegions(A, x)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) positions <- sample(1:40,30) x <- data.frame("chr1", positions, positions, rnorm(30,4,1)) meanInRegions(A, x) x <- GRanges(seqnames=x[,1],ranges=IRanges(x[,2],end=x[,2]),mcols=x[,3]) meanInRegions(A, x)
Merges the overlapping regions from two region sets. The two region sets are first merged into one and then overlapping regions are fused.
mergeRegions(A, B)mergeRegions(A, B)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
It returns a GenomicRanges object with the regions resulting from the merging process. Any two overlapping regions from any of the two sets will be fused into one.
All metadata (additional columns in the region set in addition to chromosome, start and end) will be ignored and not present in the returned region set.
The implementation relies completely in the reduce function from IRanges package.
plotRegions, toDataframe, toGRanges, subtractRegions, splitRegions, extendRegions, joinRegions, commonRegions, overlapRegions
A <- data.frame("chr1", c(1, 5, 20, 30), c(8, 13, 28, 40), x=c(1,2,3,4), y=c("a", "b", "c", "d")) B <- data.frame("chr1", 25, 35) merges <- mergeRegions(A, B) plotRegions(list(A, B, merges), chromosome="chr1", regions.labels=c("A", "B", "merges"), regions.colors=3:1)A <- data.frame("chr1", c(1, 5, 20, 30), c(8, 13, 28, 40), x=c(1,2,3,4), y=c("a", "b", "c", "d")) B <- data.frame("chr1", 25, 35) merges <- mergeRegions(A, B) plotRegions(list(A, B, merges), chromosome="chr1", regions.labels=c("A", "B", "merges"), regions.colors=3:1)
Returns the number of regions in A overlapping any region in B
numOverlaps(A, B, count.once=FALSE, ...)numOverlaps(A, B, count.once=FALSE, ...)
A |
a region set in any of the formats accepted by |
B |
a region set in any of the formats accepted by |
count.once |
boolean indicating whether the overlap of multiple B regions with a single A region should be counted once or multiple times |
... |
any additional parameters needed |
It returns a numeric value that is the number of regions in A overlapping at least one region in B.
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)) numOverlaps(A, B) numOverlaps(A, B, count.once=TRUE)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)) numOverlaps(A, B) numOverlaps(A, B, count.once=TRUE)
Graphical summary of the overlap between two set of regions.
overlapGraphicalSummary(A, B, regions.labels=c("A","B"), regions.colors=c("black","forestgreen","darkred"), ...)overlapGraphicalSummary(A, B, regions.labels=c("A","B"), regions.colors=c("black","forestgreen","darkred"), ...)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
regions.labels |
vector indicating the labels for the y axes. |
regions.colors |
character vector indicating the colors for the regions. |
... |
Arguments to be passed to methods, such as graphical parameters (see @return A plot is created on the current graphics device. |
overlapPermTest, overlapRegions
A <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) B <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) overlapGraphicalSummary(A, B, regions.labels=c("A","B"), regions.colors=c(4,5,6))A <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) B <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) overlapGraphicalSummary(A, B, regions.labels=c("A","B"), regions.colors=c(4,5,6))
Performs a permutation test to see if the overlap between two sets of regions
A and B is higher (or lower) than expected by chance. It will internally
call permTest with the appropiate parameters to perform the
permutation test. If B is a list or a GRangesList, it will perform one
permutation test per element of the list, testing the overlap between
A and each element of B independently.
overlapPermTest (A, B, alternative="auto", ...)overlapPermTest (A, B, alternative="auto", ...)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
alternative |
the alternative hypothesis must be one of |
... |
further arguments to be passed to or from methods. |
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)
IMPORTANT: Since it uses link{permTest} internally, it
is possible to use most of the parameters of that function in
overlapPermTest, including: ntimes, force.parallel,
min.parallel and verbose. In addition, this function
accepts most parameters of the randomizeRegions function
including genome, mask, allow.overlaps and
per.chromosome and the parameters of numOverlaps such
as count.once.
overlapGraphicalSummary, overlapRegions, toDataframe, toGRanges, permTest
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") C <- c(B, createRandomRegions(nregions=10, length.mean=10000, length.sd=20000, genome=genome, non.overlapping=FALSE)) pt <- overlapPermTest(A=A, B=list(B=B, C=C), ntimes=10, genome=genome, non.overlapping=FALSE, verbose=TRUE) summary(pt) plot(pt)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") C <- c(B, createRandomRegions(nregions=10, length.mean=10000, length.sd=20000, genome=genome, non.overlapping=FALSE)) pt <- overlapPermTest(A=A, B=list(B=B, C=C), ntimes=10, genome=genome, non.overlapping=FALSE, verbose=TRUE) summary(pt) plot(pt)
return overlap between 2 regios set A and B
overlapRegions(A, B, colA=NULL, colB=NULL, type="any", min.bases=1, min.pctA=NULL, min.pctB=NULL, get.pctA=FALSE, get.pctB=FALSE, get.bases=FALSE, only.boolean=FALSE, only.count=FALSE, ...)overlapRegions(A, B, colA=NULL, colB=NULL, type="any", min.bases=1, min.pctA=NULL, min.pctB=NULL, get.pctA=FALSE, get.pctB=FALSE, get.bases=FALSE, only.boolean=FALSE, only.count=FALSE, ...)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
colA |
numeric vector indicating which columns of A the results will contain (default NULL) |
colB |
numeric vector indicating which columns of B the results will contain (default NULL) |
type |
|
min.bases |
numeric minimun number of bp accepted to define a overlap (default 1) |
min.pctA |
numeric minimun percentage of bases of A accepted to define a overlap (default NULL) |
min.pctB |
numeric minimun percentage of bases of B accepted to define a overlap (default NULL) |
get.pctA |
boolean if TRUE add a column in the results indicating the number percentage of A are involved in the overlap (default FALSE) |
get.pctB |
boolean if TRUE add a column in the results indicating the number percentage of B are involved in the overlap (default FALSE) |
get.bases |
boolean if TRUE add in the results the number of overlapped bases (default FALSE) |
only.boolean |
boolean if TRUE devolve as result a boolean vector containing the overlap state of each regions of A (default FALSE) |
only.count |
boolean if TRUE devolve as result the number of regions of A overlapping with B |
... |
any additional parameter (are there any left?) |
the default results is a data.frame with at least 5 columns "chr" indicating the chromosome of the appartenence of each overlap, "startA", "endA", "startB", "endB", indicating the coordinates of the region A and B for each overlap
"type" that describe the nature of the overlap (see arguments "type") eventually other columns can be added (see see arguments "colA", "colB", "get.pctA", "get.pctB", "get.bases")
The implementation uses when possible the countOverlaps function from IRanges package.
plotRegions, toDataframe, toGRanges, subtractRegions, splitRegions, extendRegions, commonRegions, mergeRegions, joinRegions
A <- data.frame("chr1", c(1, 5, 20, 30), c(8, 13, 28, 40), x=c(1,2,3,4), y=c("a", "b", "c", "d")) B <- data.frame("chr1", 25, 35) overlapRegions(A, B)A <- data.frame("chr1", c(1, 5, 20, 30), c(8, 13, 28, 40), x=c(1,2,3,4), y=c("a", "b", "c", "d")) B <- data.frame("chr1", 25, 35) overlapRegions(A, B)
Performs a permutation test to see if there is an association between a region set and some other feature using an evaluation function.
permTest(A, ntimes=100, randomize.function, evaluate.function, alternative="auto", min.parallel=1000, force.parallel=NULL, randomize.function.name=NULL, evaluate.function.name=NULL, verbose=FALSE, ...)permTest(A, ntimes=100, randomize.function, evaluate.function, alternative="auto", min.parallel=1000, force.parallel=NULL, randomize.function.name=NULL, evaluate.function.name=NULL, verbose=FALSE, ...)
A |
a region set in any of the accepted formats by |
ntimes |
number of permutations |
randomize.function |
function to create random regions. It must return a set of regions. |
evaluate.function |
function to search for association. It must return a numeric value. |
alternative |
the alternative hypothesis must be one of |
min.parallel |
if force.parallel is not specified, this will be used to determine the threshold for parallel computation. If |
force.parallel |
logical indicating if the computation must be paralelized. |
randomize.function.name |
character. If specified, the permTestResults object will have this name instead of the name of the randomization function used. Useful specially when using unnamed anonymous functions. |
evaluate.function.name |
character. If specified, the permTestResults object will have this name instead of the name of the evaluation function used. Useful specially when using unnamed anonymous functions. |
verbose |
a boolean. If verbose=TRUE it creates a progress bar to show the computation progress. When combined with parallel computation, it might have an impact in the total computation time. |
... |
further arguments to be passed to other methods. |
permTest performs a permutation test of the regions in RS to test the association with the feature evaluated with the evaluation function. The regions are randomized using the randomization.function and the evaluation.function is used to evaluate them. More information can be found in the vignette.
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)
randomize.function the randomization function used.
randomize.function.name the name of the randomization used.
evaluate.function the evaluation function used.
evaluate.function.name the name of the evaluation function used.
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap methods and their application, Cambridge University Press, United Kingdom, 156-160
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)) pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", verbose=TRUE, genome=genome, evaluate.function=meanDistance, randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")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)) pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", verbose=TRUE, genome=genome, evaluate.function=meanDistance, randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")
Function for plotting the a localZScoreResults object.
## S3 method for class 'localZScoreResults' plot(x, main = "", num.x.labels = 5, ...)## S3 method for class 'localZScoreResults' plot(x, main = "", num.x.labels = 5, ...)
x |
an object of class |
main |
a character specifying the main title of the plot. Defaults to no title. |
num.x.labels |
a numeric specifying the number of ticks to label the x axis. The total number will be 2*num.x.labels + 1. Defaults to 5. |
... |
further arguments to be passed to or from methods. |
A plot is created on the current graphics device.
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=100000, length.sd=20000, genome=genome, non.overlapping=FALSE)) pt <- overlapPermTest(A=A, B=B, ntimes=10, genome=genome, non.overlapping=FALSE) lz <- localZScore(A=A, B=B, pt=pt) plot(lz)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=100000, length.sd=20000, genome=genome, non.overlapping=FALSE)) pt <- overlapPermTest(A=A, B=B, ntimes=10, genome=genome, non.overlapping=FALSE) lz <- localZScore(A=A, B=B, pt=pt) plot(lz)
permTestResults object.Function for plotting the results from a permTestResults object.
## S3 method for class 'permTestResults' plot( x, pvalthres = 0.05, plotType = "Tailed", main = "", xlab = NULL, ylab = "", ylim = NULL, xlim = NULL, ... )## S3 method for class 'permTestResults' plot( x, pvalthres = 0.05, plotType = "Tailed", main = "", xlab = NULL, ylab = "", ylim = NULL, xlim = NULL, ... )
x |
an object of class |
pvalthres |
p-value threshold for significance. Default is 0.05. |
plotType |
the type of plot to display. This must be one of |
main |
a character specifying the title of the plot. Defaults to "". |
xlab |
a character specifying the label of the x axis. Defaults to NULL, which produces a plot with the evaluation function name as the x axis label. |
ylab |
a character specifying the label of the y axis. Defaults to "". |
ylim |
defines the y limits of the plot. Passed to the underlying |
xlim |
defines the x limits of the plot. Passed to the underlying |
... |
further arguments to be passed to or from methods. |
A plot is created on the current graphics device.
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) summary(pt) plot(pt) plot(pt, plotType="Tailed") pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", genome=genome, evaluate.function=meanDistance, randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")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) summary(pt) plot(pt) plot(pt, plotType="Tailed") pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", genome=genome, evaluate.function=meanDistance, randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")
permTestResultsList object when more than one evaluation function was used.Function for plotting the results from a permTestResultsList object when more than one evaluation function was used.
## S3 method for class 'permTestResultsList' plot( x, ncol = NA, pvalthres = 0.05, plotType = "Tailed", main = "", xlab = NULL, ylab = "", ... )## S3 method for class 'permTestResultsList' plot( x, ncol = NA, pvalthres = 0.05, plotType = "Tailed", main = "", xlab = NULL, ylab = "", ... )
x |
an object of class |
ncol |
number of plots per row. ncol=NA means ncol=floor(sqrt(length(x)))so the plot is more or less square (default=NA) |
pvalthres |
p-value threshold for significance. Default is 0.05. |
plotType |
the type of plot to display. This must be one of |
main |
a character specifying the title of the plot. Defaults to "". |
xlab |
a character specifying the label of the x axis. Defaults to NULL, which produces a plot with the evaluation function name as the x axis label. |
ylab |
a character specifying the label of the y axis. Defaults to "". |
... |
further arguments to be passed to or from methods. |
A plot is created on the current graphics device.
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) summary(pt) plot(pt) plot(pt, plotType="Tailed") pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", genome=genome, evaluate.function=list(distance=meanDistance, numberOfOverlaps=numOverlaps), randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")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) summary(pt) plot(pt) plot(pt, plotType="Tailed") pt2 <- permTest(A=A, B=B, ntimes=10, alternative="auto", genome=genome, evaluate.function=list(distance=meanDistance, numberOfOverlaps=numOverlaps), randomize.function=randomizeRegions, non.overlapping=FALSE) summary(pt2) plot(pt2) plot(pt2, plotType="Tailed")
Plots sets of regions
plotRegions(x, chromosome, start=NULL, end=NULL, regions.labels=NULL, regions.colors=NULL, ...)plotRegions(x, chromosome, start=NULL, end=NULL, regions.labels=NULL, regions.colors=NULL, ...)
x |
list of objects to be ploted. |
chromosome |
character or numeric value indicating which chromosome you want to plot. |
start |
numeric value indicating from which position you want to plot. |
end |
numeric value indicating to which position you want to plot. |
regions.labels |
vector indicating the labels for the y axes. It must have the same length as x. |
regions.colors |
character vector indicating the colors for the plotted regions. It must have the same length as x. |
... |
Arguments to be passed to methods, such as graphical parameters (see |
A plot is created on the current graphics device.
A <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) B <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) plotRegions(list(A,B), chromosome=1, regions.labels=c("A","B"), regions.colors=3:2)A <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) B <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) plotRegions(list(A,B), chromosome=1, regions.labels=c("A","B"), regions.colors=3:2)
Given a set of regions A and a genome, this function returns a new set of regions randomly distributted in the genome.
randomizeRegions(A, genome="hg19", mask=NULL, allow.overlaps=TRUE, per.chromosome=FALSE, ...)randomizeRegions(A, genome="hg19", mask=NULL, allow.overlaps=TRUE, per.chromosome=FALSE, ...)
A |
The set of regions to randomize. A region set in any of the accepted formats by |
genome |
The reference genome to use. A valid genome object. Either a |
mask |
The set of regions specifying where a random region can not be (centromeres, repetitive regions, unmappable regions...). A region set in any of the accepted formats by |
allow.overlaps |
A boolean stating whether the random regions can overlap (FALSE) or not (TRUE). |
per.chromosome |
Boolean. If TRUE, the regions will be created in a per chromosome maner -every region in A will be moved into a random position at the same chromosome where it was originally-. |
... |
further arguments to be passed to or from methods. |
The new set of regions will be created with the same sizes of the original ones, and optionally placed in the same chromosomes.
In addition, they can be made explicitly non overlapping and a mask can be provided so no regions fall in an undesirable part of the genome.
It returns a GenomicRanges object with the regions resulting from the randomization process.
randomizeRegions assumes that chromosomes start at base 1. If a chromosome starts at another base number, for example at base 1000, random regions might appear in the [1:1000] interval. This should not affect most uses of randomizeRegions, but might be important in some advanced analysis involving artificially contructed genomes.
toDataframe, toGRanges, getGenome, getMask, getGenomeAndMask, characterToBSGenome, maskFromBSGenome, resampleRegions, createRandomRegions, circularRandomizeRegions
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) randomizeRegions(A) randomizeRegions(A, genome=genome, mask=mask, per.chromosome=TRUE, allow.overlaps=FALSE)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) mask <- data.frame("chr1", c(20000000, 100000000), c(22000000, 130000000)) genome <- data.frame(c("chr1", "chr2"), c(1, 1), c(180000000, 20000000)) randomizeRegions(A) randomizeRegions(A, genome=genome, mask=mask, per.chromosome=TRUE, allow.overlaps=FALSE)
Recomputes the permutation test changing the alternative hypotesis
recomputePermTest(ptr)recomputePermTest(ptr)
ptr |
an object of class |
A list of class permTestResults containing the same components as permTest results.
A <- createRandomRegions(nregions=10, length.mean=1000000) B <- createRandomRegions(nregions=10, length.mean=1000000) resPerm <- permTest(A=A, B=B, ntimes=5, alternative="less", genome="hg19", evaluate.function=meanDistance, randomize.function=randomizeRegions) plot(resPerm)A <- createRandomRegions(nregions=10, length.mean=1000000) B <- createRandomRegions(nregions=10, length.mean=1000000) resPerm <- permTest(A=A, B=B, ntimes=5, alternative="less", genome="hg19", evaluate.function=meanDistance, randomize.function=randomizeRegions) plot(resPerm)
Fast alternative to randomizeRegions. It creates a tiling (binning) of the whole genome with tiles the mean size of the regions in A and then places the regions by sampling a length(A) number of tiles and placing the resampled regions there.
resampleGenome(A, simple = FALSE, per.chromosome = FALSE, genome="hg19", min.tile.width=1000, ...)resampleGenome(A, simple = FALSE, per.chromosome = FALSE, genome="hg19", min.tile.width=1000, ...)
A |
an object of class GenomigRanges |
simple |
logical, if TRUE the randomization process will not take into account the specific width of each region in A. (defalut = FALSE) |
per.chromosome |
logical, if TRUE the randomization will be perform by chromosome. (default = TRUE) |
genome |
character or GenomicRanges, genome using for the randomization |
min.tile.width |
integer, the minimum size of the genome tiles. If they are too small, the functions gets very slow and may even fail to work. (default = 1000, 1kb tiles) |
... |
further arguments to be passed to other methods. |
a GenomicRanges object. A sample from the universe with the same length as A.
toDataframe, toGRanges, randomizeRegions, createRandomRegions
A <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) B <- resampleGenome(A) B width(B) B2 <- resampleGenome(A, simple=TRUE) B2 width(B2) resampleGenome(A, per.chromosome=TRUE)A <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) B <- resampleGenome(A) B width(B) B2 <- resampleGenome(A, simple=TRUE) B2 width(B2) resampleGenome(A, per.chromosome=TRUE)
Function for sampling a region set from a universe of region sets.
resampleRegions(A, universe, per.chromosome=FALSE, ...)resampleRegions(A, universe, per.chromosome=FALSE, ...)
A |
a region set in any of the formats accepted by |
universe |
a region set in any of the formats accepted by |
per.chromosome |
boolean indicating if sample must be by chromosome. |
... |
further arguments to be passed to or from methods. |
a GenomicRanges object. A sample from the univers with the same length as A.
toDataframe, toGRanges, randomizeRegions, createRandomRegions
universe <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) A <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) resampleRegions(A, universe, per.chromosome=TRUE)universe <- data.frame(chr=1, start=c(1,15,24,40,50), end=c(10,20,30,45,55)) A <- data.frame(chr=1, start=c(2,12,28,35), end=c(5,25,33,43)) resampleRegions(A, universe, per.chromosome=TRUE)
Splits a region set A by both ends of the regions in a second region set B.
splitRegions(A, B, min.size=1, track.original=TRUE)splitRegions(A, B, min.size=1, track.original=TRUE)
A |
a region set in any of the formats accepted by |
B |
a region set in any of the formats accepted by |
min.size |
numeric value, minimal size of the new regions |
track.original |
logical indicating if you want to keep the original regions and additional information in the output |
A GRanges with the splitted regions.
toDataframe, toGRanges, subtractRegions, commonRegions, extendRegions, joinRegions, mergeRegions, overlapRegions
A <- data.frame(chr=1, start=c(1, 15, 24, 40, 50), end=c(10, 20, 30, 45, 55)) B <- data.frame(chr=1, start=c(2, 12, 28, 35), end=c(5, 25, 33, 43)) splits <- splitRegions(A, B) plotRegions(list(A, B, splits), chromosome=1, regions.labels=c("A", "B", "splits"), regions.colors=3:1)A <- data.frame(chr=1, start=c(1, 15, 24, 40, 50), end=c(10, 20, 30, 45, 55)) B <- data.frame(chr=1, start=c(2, 12, 28, 35), end=c(5, 25, 33, 43)) splits <- splitRegions(A, B) plotRegions(list(A, B, splits), chromosome=1, regions.labels=c("A", "B", "splits"), regions.colors=3:1)
Function for subtracting a region set from another region set.
subtractRegions(A, B)subtractRegions(A, B)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
This function returns the regions in A minus the parts of them overlapping the regions in B. Overlapping regions in the result will be fused.
The implementation relies completely in the setdiff function from IRanges package.
A GenomicRanges object
A <- data.frame(chr=1, start=c(1, 15, 24, 31), end=c(10, 20, 30, 35)) B <- data.frame(chr=1, start=c(2, 12, 24, 35), end=c(5, 25, 29, 40)) subtract <- subtractRegions(A, B) plotRegions(list(A, B, subtract), chromosome=1, regions.labels=c("A", "B", "subtract"), regions.colors=3:1)A <- data.frame(chr=1, start=c(1, 15, 24, 31), end=c(10, 20, 30, 35)) B <- data.frame(chr=1, start=c(2, 12, 24, 35), end=c(5, 25, 29, 40)) subtract <- subtractRegions(A, B) plotRegions(list(A, B, subtract), chromosome=1, regions.labels=c("A", "B", "subtract"), regions.colors=3:1)
Transforms a GRanges object or a data.framecontaining a region set into a data.frame.
toDataframe(A, stranded=FALSE)toDataframe(A, stranded=FALSE)
A |
a |
stranded |
(only used when A is a |
If the oject is of class data.frame, it will be returned untouched.
A data.frame with the regions in A. If A was a GRanges object, the output will include any metadata present in A.
A <- data.frame(chr=1, start=c(1, 15, 24), end=c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) A2 <- toGRanges(A) toDataframe(A2)A <- data.frame(chr=1, start=c(1, 15, 24), end=c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) A2 <- toGRanges(A) toDataframe(A2)
Transforms a file or an object containing a region set into a
GRanges object.
toGRanges(A, ..., genome=NULL, sep=NULL, comment.char="#")toGRanges(A, ..., genome=NULL, sep=NULL, comment.char="#")
A |
a |
... |
further arguments to be passed to other methods. |
genome |
(character or BSgenome) The genome info to be attached to the created GRanges. If NULL no genome info will be attached. (defaults to NULL) |
sep |
(character) The field separator in the text file. If NULL it will be automatically guessed. Only used when reading some file formats. (Defaults to NULL) |
comment.char |
(character) The character marking comment lines. Only used when reading some file formats. (Defaults to "#") |
If A is already a GRanges object, it will be returned untouched.
If A is a data frame, the function will assume the first three columns are
chromosome, start and end and create a GRanges object. Any
additional column will be considered metadata and stored as such in the
GRanges object. There are 2 special cases: 1) if A is a
data.frame with only 2 columns, it will assume the first one is the
chromosome and the second one the position and it will create a GRanges with
single base regions and 2) if the data.frame has the first 3 columns named
"SNP", "CHR" and "BP" it will shuffle the columns and repeat "BP" to build
a GRanges of single base regions (this is the standard ouput format of plink).
If A is not a data.frame and there are more parameters, it will try to build
a data.frame with all parameters and use that data.frame to build the
GRanges. This allows the user to call it like
toGRanges("chr1", 10, 20).
If A is a character or a character vector and it's not a file or a URL, it assumes it's a genomic position description in the form used by UCSC or IGV, "chr2:1000-2000". It will try to parse the character strings into chromosome, start and end and create a GRanges. The parser can deal with commas separating thousands (e.g. "chr2:1,000-2,000") and with the comma used as a start/end separator (e.g. "chr2:1000,2000"). These different variants can be mixed in the same character vector.
If A is a "SimpleRleList" it will be interpreted as the result from GenomicRanges::coverage and the function will return a GRanges with a single metadata column named "coverage".
If A is a file name (local or remote) or a connection to a file, it will try
to load it in different ways:
* BED files (identified by a "bed" extension): will be loaded using
rtracklayer::import function. Coordinates are 0 based as
described in the BED specification (https://genome.ucsc.edu/FAQ/FAQformat.html#format1).
* PLINK assoc files (identified by ".assoc", ".assoc.fisher",
".assoc.dosage", ".assoc.linear", ".assoc.logistic"): will be loaded
as single-base ranges with all original columns present and the SNPs ids
as the ranges names
* Any other file: It assumes the file is a "generic" tabular file. To load
it it will ignore any header line starting with comment.char,
autodetect the field separator (if not provided by the user),
autodetect if it has a header and read it accordingly.
The genome parameter can be used to set the genome information of
the created GRanges. It can be either a BSgenome object or a
character string defining a genome (e.g. "hg19", "mm10"...) as accepted
by the BSgenome::getBSgenome function. If a valid genome is
given and the corresponding BSgenome package is installed, the genome
information will be attached to the GRanges. If the chromosome naming style
from the GRanges and the genome object are different, it will try to change
the GRanges styles to match those of the genome using
GenomeInfoDb::seqlevelsStyle.
A GRanges object with the regions in A
**IMPORTANT:** Regarding the coordinates, BED files are 0 based
while data.frames and generic files are treated as 1 based. Therefore
reading a line "chr9 100 200" from a BED file will create a 99 bases wide
interval starting at base 101 and ending at 200 but reading it from a txt
file or from a data.frame will create a 100 bases wide interval
starting at 100 and ending at 200. This is specially relevant in 1bp
intervals. For example, the 10th base of chromosome 1 would be
"chr1 9 10" in a BED file and "chr1 10 10" in a txt file.
A <- data.frame(chr=1, start=c(1, 15, 24), end=c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) gr1 <- toGRanges(A) #No need to give the data.frame columns any specific name A <- data.frame(1, c(1, 15, 24), c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) gr2 <- toGRanges(A) #We can pass the data without building the data.frame gr3 <- toGRanges("chr9", 34229289, 34982376, x="X") #And each argument can be a vector (they will be recycled as needed) gr4 <- toGRanges("chr9", c(34229289, 40000000), c(34982376, 50000000), x="X", y=c("a", "b")) #toGRanges will automatically convert the second and third argument into numerics gr5 <- toGRanges("chr9", "34229289", "34982376") #It can be a file from disk bed.file <- system.file("extdata", "my.special.genes.txt", package="regioneR") gr6 <- toGRanges(bed.file) #Or a URL to a valid file #gr7 <- toGRanges("http://path.to/myfile.bed") #It can also parse genomic location strings gr8 <- toGRanges("chr9:34229289-34982376") #more than one gr9 <- toGRanges(c("chr9:34229289-34982376", "chr10:1000-2000")) #even with strange and mixed syntaxes gr10 <- toGRanges(c("chr4:3873-92928", "chr4:3873,92928", "chr5:33,444-45,555")) #if the genome is given it is used to annotate the resulting GRanges gr11 <- toGRanges(c("chr9:34229289-34982376", "chr10:1000-2000"), genome="hg19") #and the genome is added to the GRanges even if A is a GRanges gr12 <- toGRanges(gr6, genome="hg19") #And it will change the chromosome naming of the GRanges to match that of the #genome if it is possible (using GenomeInfoDb::seqlevelsStyle) gr2 gr13 <- toGRanges(gr2, genome="hg19") #in addition, it can convert other objects into GRanges such as the #result of GenomicRanges::coverage gr14 <- toGRanges(c("1:1-20", "1:5-25", "1:18-40")) cover <- GenomicRanges::coverage(gr14) gr15 <- toGRanges(cover)A <- data.frame(chr=1, start=c(1, 15, 24), end=c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) gr1 <- toGRanges(A) #No need to give the data.frame columns any specific name A <- data.frame(1, c(1, 15, 24), c(10, 20, 30), x=c(1,2,3), y=c("a", "b", "c")) gr2 <- toGRanges(A) #We can pass the data without building the data.frame gr3 <- toGRanges("chr9", 34229289, 34982376, x="X") #And each argument can be a vector (they will be recycled as needed) gr4 <- toGRanges("chr9", c(34229289, 40000000), c(34982376, 50000000), x="X", y=c("a", "b")) #toGRanges will automatically convert the second and third argument into numerics gr5 <- toGRanges("chr9", "34229289", "34982376") #It can be a file from disk bed.file <- system.file("extdata", "my.special.genes.txt", package="regioneR") gr6 <- toGRanges(bed.file) #Or a URL to a valid file #gr7 <- toGRanges("http://path.to/myfile.bed") #It can also parse genomic location strings gr8 <- toGRanges("chr9:34229289-34982376") #more than one gr9 <- toGRanges(c("chr9:34229289-34982376", "chr10:1000-2000")) #even with strange and mixed syntaxes gr10 <- toGRanges(c("chr4:3873-92928", "chr4:3873,92928", "chr5:33,444-45,555")) #if the genome is given it is used to annotate the resulting GRanges gr11 <- toGRanges(c("chr9:34229289-34982376", "chr10:1000-2000"), genome="hg19") #and the genome is added to the GRanges even if A is a GRanges gr12 <- toGRanges(gr6, genome="hg19") #And it will change the chromosome naming of the GRanges to match that of the #genome if it is possible (using GenomeInfoDb::seqlevelsStyle) gr2 gr13 <- toGRanges(gr2, genome="hg19") #in addition, it can convert other objects into GRanges such as the #result of GenomicRanges::coverage gr14 <- toGRanges(c("1:1-20", "1:5-25", "1:18-40")) cover <- GenomicRanges::coverage(gr14) gr15 <- toGRanges(cover)
Returns the regions unique to only one of the two region sets, that is, all parts of the genome covered by only one of the two region sets.
uniqueRegions(A, B)uniqueRegions(A, B)
A |
a region set in any of the accepted formats by |
B |
a region set in any of the accepted formats by |
It returns a GenomicRanges object with the regions unique to one of the region sets.
All metadata (additional columns in the region set in addition to chromosome, start and end) will be ignored and not present in the returned region set.
toGRanges, subtractRegions, commonRegions, mergeRegions
A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) uniques <- uniqueRegions(A, B) plotRegions(list(A, B, uniques), chromosome="chr1", regions.labels=c("A", "B", "uniques"), regions.colors=3:1)A <- data.frame("chr1", c(1, 10, 20, 30), c(12, 13, 28, 40)) B <- data.frame("chr1", 25, 35) uniques <- uniqueRegions(A, B) plotRegions(list(A, B, uniques), chromosome="chr1", regions.labels=c("A", "B", "uniques"), regions.colors=3:1)