R/regionalize.R
In vd4mmind/methylkit: DNA methylation analysis from high-throughput bisulfite sequencing results
# functions to get regional counts
# get counts for a given regions
#
# S4 FUNCTIONS
#
#' Get regional counts for given GRanges or GRangesList object
#'
#' Convert \code{\link{methylRaw}}, \code{\link{methylRawList}} or \code{\link{methylBase}} object into
#' regional counts for a given \code{\link{GRanges}} or \code{\link{GRangesList}} object.
#' @param object a \code{methylRaw} or \code{methlRawList} object
#' @param regions a GRanges or GRangesList object.
#' @param cov.bases number minimum bases covered per region (Default:0).
#' Only regions with base coverage above this threshold are returned.
#' @param strand.aware if set to TRUE only CpGs that match the strand of
#' the region will be summarized. (default:FALSE)
#'
#' @return a new methylRaw,methylBase or methylRawList object. If \code{strand.aware} is
#' set to FALSE (default). Even though the resulting object will have
#' the strand information of \code{regions} it will still contain
#' methylation information from both strands.
#'
#' @usage regionCounts(object,regions,cov.bases=0,strand.aware=FALSE)
#' @examples
#' data(methylKit)
#'
#' # get the windows of interest as a GRanges object, this can be any set
#' # of genomic locations
#' library(GenomicRanges)
#' my.win=GRanges(seqnames="chr21",
#' ranges=IRanges(start=seq(from=9764513,by=10000,length.out=20),width=5000) )
#'
#' # getting counts per region
#' regional.methylRaw=regionCounts(object=methylRawList.obj, regions=my.win,
#' cov.bases=0,strand.aware=FALSE)
#'
#'
#' @export
#' @docType methods
#' @rdname regionCounts
setGeneric("regionCounts",
function(object,regions,cov.bases=0,strand.aware=FALSE)
standardGeneric("regionCounts") )
# GETs regional counts for given GRanges object
# RETURNS a new methylRaw object
# @param object a \code{methylRaw} object
# @param regions a GRanges object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRaw,GRanges-method
setMethod("regionCounts", signature(object="methylRaw",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
df=data.frame(id = mat[, 1], getData(object)[mat[, 2], c(5, 6, 7)])
dt=data.table::data.table(df)
#dt=data.table(id=mat[,1],object[mat[,2],c(6,7,8)] ) worked with data.table 1.7.7
coverage=NULL
numCs=NULL
numTs=NULL
id=NULL
# use data.table to sum up counts per region
sum.dt=dt[,list(coverage=sum(coverage),
numCs =sum(numCs),
numTs =sum(numTs),covered=length(numTs)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
temp.df=as.data.frame(regions) # get regions to a dataframe
# look for values with "name" in it, eg. "tx_name" or "name"
# valuesList = names(values(regions))
# nameid = valuesList[grep (valuesList, pattern="name")]
#create id string for the new object to be returned
#ids have to be unique and we can not assume GRanges objects will
#have a name attribute
if("name" %in% names(temp.df))
{
new.ids=paste(temp.df[sum.dt$id,"seqnames"],temp.df[sum.dt$id,"start"],
temp.df[sum.dt$id,"end"],temp.df[sum.dt$id,"name"],sep=".")
}else{
new.ids=paste(temp.df[sum.dt$id,"seqnames"],temp.df[sum.dt$id,"start"],
temp.df[sum.dt$id,"end"],sep=".")
}
#create a new methylRaw object to return
new.data=data.frame(#id =new.ids,
chr =temp.df[sum.dt$id,"seqnames"],
start =temp.df[sum.dt$id,"start"],
end =temp.df[sum.dt$id,"end"],
strand =temp.df[sum.dt$id,"strand"],
coverage=sum.dt$coverage,
numCs =sum.dt$numCs,
numTs =sum.dt$numTs)
new("methylRaw",new.data,sample.id=object@sample.id,
assembly=object@assembly,context=object@context,
resolution="region")
}
)
#' @rdname regionCounts
#' @aliases regionCounts,methylBase,GRanges-method
setMethod("regionCounts", signature(object="methylBase",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
df=data.frame(id = mat[, 1], getData(object)[mat[, 2], 5:ncol(object) ])
dt=data.table::data.table(df)
#dt=data.table(id=mat[,1],object[mat[,2],c(6,7,8)] ) worked with data.table 1.7.7
# use data.table to sum up counts per region
sum.dt=dt[,c(lapply(.SD,sum),covered=length(numTs1)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
temp.df=as.data.frame(regions) # get regions to a dataframe
# look for values with "name" in it, eg. "tx_name" or "name"
# valuesList = names(values(regions))
# nameid = valuesList[grep (valuesList, pattern="name")]
#create a new methylRaw object to return
new.data=data.frame(#id =new.ids,
chr =temp.df[sum.dt$id,"seqnames"],
start =temp.df[sum.dt$id,"start"],
end =temp.df[sum.dt$id,"end"],
strand =temp.df[sum.dt$id,"strand"],
as.data.frame(sum.dt[,,c(2:(ncol(sum.dt)-1))]),stringsAsFactors=FALSE)
if(strand.aware & !(object@destranded) ){destranded=FALSE}else{destranded=TRUE}
new("methylBase",new.data,sample.ids=object@sample.ids,
assembly=object@assembly,context=object@context,treatment=object@treatment,
coverage.index=object@coverage.index,numCs.index=object@numCs.index,
numTs.index=object@numTs.index,destranded=destranded,
resolution="region")
}
)
# RETURNS a new methylRawList object
# gets regional counts for all elements in methylRawList for given regions
# MAKE SURE an element of the list, which will be a set of GRanges rows,
# are not on different chromsomes and strands
# Also, make sure id column of returned methylRaw object is unique
# you can add refseq id to the id column: chr.start.end.refseqid
# @param object a \code{methylRaw} object
# @param regions a GRangesList object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRaw,GRangesList-method
# assume that each name of the element in the GRangesList is unique and
setMethod("regionCounts", signature(object="methylRaw",regions="GRangesList"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
dt=data.table::data.table(id=mat[,1],getData(object)[mat[,2],c(5,6,7)] )
# use data.table to sum up counts per region
sum.dt=dt[,list(coverage=sum(coverage),
numCs =sum(numCs),
numTs =sum(numTs),covered=length(numTs)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
#temp.df=as.data.frame(regions) # get regions to a dataframe
temp.dt=data.table(as.data.frame(regions))
first.element = function(x) x[1]
sum.temp.dt=temp.dt[, list(chr = first.element(seqnames),
start = min(start),
end = max(end),
strand = first.element(strand)),
by=element]
# if it is intron, some of the symbols may not have any intron GRanges
sum.id=names(regions)[sum.dt$id]
#create a new methylRaw object to return
new.data=data.frame(
#id =sum.id,
chr =sum.temp.dt$chr[sum.temp.dt$element %in% sum.id],
start =sum.temp.dt$start[sum.temp.dt$element %in% sum.id],
end =sum.temp.dt$end[sum.temp.dt$element %in% sum.id],
strand =sum.temp.dt$strand[sum.temp.dt$element %in% sum.id],
coverage=sum.dt$coverage,
numCs =sum.dt$numCs,
numTs =sum.dt$numTs)
new("methylRaw",new.data,sample.id=object@sample.id,
assembly=object@assembly,context=object@context,
resolution="region")
}
)
# Note: some genes do not have intron, need to take care of it.
# GETs regional counts for given GRanges object
# RETURNS a new methylRawList object
# @param object a \code{methylRawList} object
# @param regions a GRanges object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRawList,GRanges-method
setMethod("regionCounts", signature(object="methylRawList",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
outList=list()
for(i in 1:length(object))
{
obj = regionCounts(object = object[[i]],
regions=regions,
cov.bases,strand.aware)
outList[[i]] = obj
}
myobj=new("methylRawList", outList,treatment=object@treatment)
myobj
}
)
# GETs regional counts for given GRangesList object
# RETURNS a new methylRawList object
# @param object a \code{methylRawList} object
# @param regions a GRangesList object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRawList,GRangesList-method
setMethod("regionCounts", signature(object="methylRawList",
regions="GRangesList"),
function(object,regions,cov.bases,strand.aware){
outList=list()
for(i in 1:length(object))
{
obj = regionCounts(object = object[[i]],regions=regions,
cov.bases,strand.aware)
outList[[i]] = obj
}
myobj=new("methylRawList", outList,treatment=object@treatment)
myobj
}
)
#' Get methylated/unmethylated base counts for tilling windows
#'
#' The function summarizes methylated/unmethylated base counts over tilling
#' windows accross genome. This function can be used when differential
#' methylated analysis is preferable to tilling windows instead of base pairs.
#'
#' @param object \code{\link{methylRaw}}, \code{\link{methylRawList}} or \code{\link{methylBase}}
#' object containing base pair resolution methylation information
#' @param win.size an integer for the size of the tiling windows
#' @param step.size an integer for the step size of tiling windows
#' @param cov.bases minimum number of bases to be covered in a given window
#' @usage tileMethylCounts(object,win.size=1000,step.size=1000,cov.bases=0)
#' @return \code{methylRaw},\code{methylBase} or \code{methylRawList} object
#' @export
#' @examples
#' data(methylKit)
#'
#' tiled.methylRaw=tileMethylCounts(object=methylRawList.obj,win.size=1000,
#' step.size=1000,cov.bases=0)
#'
#'
#' @docType methods
#' @rdname tileMethylCounts-methods
setGeneric("tileMethylCounts",
function(object,win.size=1000,step.size=1000,cov.bases=0)
standardGeneric("tileMethylCounts") )
#' @aliases tileMethylCounts,methylRaw-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylRaw"),
function(object,win.size,step.size,cov.bases){
g.meth =as(object,"GRanges")
chrs =IRanges::levels(seqnames(g.meth))
widths =seqlengths(g.meth)
all.wins=GRanges()
for(i in 1:length(chrs))
{
# get max length of feature covered chromosome
max.length=max(IRanges::end(g.meth[seqnames(g.meth)==chrs[i],]))
#get sliding windows with covered CpGs
numTiles=floor( (max.length-(win.size-step.size) )/step.size )+1
temp.wins=GRanges(seqnames=rep(chrs[i],numTiles),
ranges=IRanges(start=1+0:(numTiles-1)*step.size,
width=rep(win.size,numTiles)) )
all.wins=suppressWarnings(c(all.wins,temp.wins))
}
regionCounts(object,all.wins,cov.bases,strand.aware=FALSE)
}
)
#' @aliases tileMethylCounts,methylRawList-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylRawList"),
function(object,win.size,step.size,cov.bases){
new.list=lapply(object,tileMethylCounts,win.size,step.size,cov.bases)
new("methylRawList", new.list,treatment=object@treatment)
})
#' @aliases tileMethylCounts,methylBase-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylBase"),
function(object,win.size,step.size,cov.bases){
g.meth =as(object,"GRanges")
chrs =IRanges::levels(seqnames(g.meth))
widths =seqlengths(g.meth)
all.wins=GRanges()
for(i in 1:length(chrs))
{
# get max length of feature covered chromosome
max.length=max(IRanges::end(g.meth[seqnames(g.meth)==chrs[i],]))
#get sliding windows with covered CpGs
numTiles=floor( (max.length-(win.size-step.size) )/step.size )+1
temp.wins=GRanges(seqnames=rep(chrs[i],numTiles),
ranges=IRanges(start=1+0:(numTiles-1)*step.size,
width=rep(win.size,numTiles)) )
all.wins=suppressWarnings(c(all.wins,temp.wins))
}
regionCounts(object,all.wins,cov.bases,strand.aware=FALSE)
}
)
# back up code:
# annotation of methylation states by genes
# including promoter, intron, exon, nearest cpgi and cpgi shore.
# valPerIndex=function(query, subject, col="cov"){
# cov.per=value.per.location(query,subject,col=col)
# all.ind=1:length(query)
# rm=data.frame(ind=all.ind[!all.ind %in% (cov.per[,1])],
# val=rep(0,length(all.ind[!all.ind %in% (cov.per[,1])]) ),
# numRows=rep(0,length(all.ind[!all.ind %in% (cov.per[,1])]) ) )
# cov.per2=rbind(cov.per,rm)
# cov.per3=data.frame(cov.per2, tx_id=names(query)[cov.per2[,1]])
# return(cov.per3)
# }
#
# meth.ratio=function(meth.idx, cov.idx){
# if(identical(meth.idx[,1], cov.idx[,1])){
# ratio = meth.idx[,2]/cov.idx[,2]
# names(ratio) = meth.idx[,4]
# return(ratio)
# }
# else stop("index does not match")
# }
#
# # using exons
# meth.idx.exons=valPerIndex(exonRanges, g.meth, col="methCs")
# cov.idx.exons=valPerIndex(exonRanges, g.meth, col="cov")
#
# exon.meth.ratio=meth.ratio(meth.idx.exons, cov.idx.exons)
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# functions to get regional counts
# get counts for a given regions
#
# S4 FUNCTIONS
#
#' Get regional counts for given GRanges or GRangesList object
#'
#' Convert \code{\link{methylRaw}}, \code{\link{methylRawList}} or \code{\link{methylBase}} object into
#' regional counts for a given \code{\link{GRanges}} or \code{\link{GRangesList}} object.
#' @param object a \code{methylRaw} or \code{methlRawList} object
#' @param regions a GRanges or GRangesList object.
#' @param cov.bases number minimum bases covered per region (Default:0).
#' Only regions with base coverage above this threshold are returned.
#' @param strand.aware if set to TRUE only CpGs that match the strand of
#' the region will be summarized. (default:FALSE)
#'
#' @return a new methylRaw,methylBase or methylRawList object. If \code{strand.aware} is
#' set to FALSE (default). Even though the resulting object will have
#' the strand information of \code{regions} it will still contain
#' methylation information from both strands.
#'
#' @usage regionCounts(object,regions,cov.bases=0,strand.aware=FALSE)
#' @examples
#' data(methylKit)
#'
#' # get the windows of interest as a GRanges object, this can be any set
#' # of genomic locations
#' library(GenomicRanges)
#' my.win=GRanges(seqnames="chr21",
#' ranges=IRanges(start=seq(from=9764513,by=10000,length.out=20),width=5000) )
#'
#' # getting counts per region
#' regional.methylRaw=regionCounts(object=methylRawList.obj, regions=my.win,
#' cov.bases=0,strand.aware=FALSE)
#'
#'
#' @export
#' @docType methods
#' @rdname regionCounts
setGeneric("regionCounts",
function(object,regions,cov.bases=0,strand.aware=FALSE)
standardGeneric("regionCounts") )
# GETs regional counts for given GRanges object
# RETURNS a new methylRaw object
# @param object a \code{methylRaw} object
# @param regions a GRanges object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRaw,GRanges-method
setMethod("regionCounts", signature(object="methylRaw",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
df=data.frame(id = mat[, 1], getData(object)[mat[, 2], c(5, 6, 7)])
dt=data.table::data.table(df)
#dt=data.table(id=mat[,1],object[mat[,2],c(6,7,8)] ) worked with data.table 1.7.7
coverage=NULL
numCs=NULL
numTs=NULL
id=NULL
# use data.table to sum up counts per region
sum.dt=dt[,list(coverage=sum(coverage),
numCs =sum(numCs),
numTs =sum(numTs),covered=length(numTs)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
temp.df=as.data.frame(regions) # get regions to a dataframe
# look for values with "name" in it, eg. "tx_name" or "name"
# valuesList = names(values(regions))
# nameid = valuesList[grep (valuesList, pattern="name")]
#create id string for the new object to be returned
#ids have to be unique and we can not assume GRanges objects will
#have a name attribute
if("name" %in% names(temp.df))
{
new.ids=paste(temp.df[sum.dt$id,"seqnames"],temp.df[sum.dt$id,"start"],
temp.df[sum.dt$id,"end"],temp.df[sum.dt$id,"name"],sep=".")
}else{
new.ids=paste(temp.df[sum.dt$id,"seqnames"],temp.df[sum.dt$id,"start"],
temp.df[sum.dt$id,"end"],sep=".")
}
#create a new methylRaw object to return
new.data=data.frame(#id =new.ids,
chr =temp.df[sum.dt$id,"seqnames"],
start =temp.df[sum.dt$id,"start"],
end =temp.df[sum.dt$id,"end"],
strand =temp.df[sum.dt$id,"strand"],
coverage=sum.dt$coverage,
numCs =sum.dt$numCs,
numTs =sum.dt$numTs)
new("methylRaw",new.data,sample.id=object@sample.id,
assembly=object@assembly,context=object@context,
resolution="region")
}
)
#' @rdname regionCounts
#' @aliases regionCounts,methylBase,GRanges-method
setMethod("regionCounts", signature(object="methylBase",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
df=data.frame(id = mat[, 1], getData(object)[mat[, 2], 5:ncol(object) ])
dt=data.table::data.table(df)
#dt=data.table(id=mat[,1],object[mat[,2],c(6,7,8)] ) worked with data.table 1.7.7
# use data.table to sum up counts per region
sum.dt=dt[,c(lapply(.SD,sum),covered=length(numTs1)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
temp.df=as.data.frame(regions) # get regions to a dataframe
# look for values with "name" in it, eg. "tx_name" or "name"
# valuesList = names(values(regions))
# nameid = valuesList[grep (valuesList, pattern="name")]
#create a new methylRaw object to return
new.data=data.frame(#id =new.ids,
chr =temp.df[sum.dt$id,"seqnames"],
start =temp.df[sum.dt$id,"start"],
end =temp.df[sum.dt$id,"end"],
strand =temp.df[sum.dt$id,"strand"],
as.data.frame(sum.dt[,,c(2:(ncol(sum.dt)-1))]),stringsAsFactors=FALSE)
if(strand.aware & !(object@destranded) ){destranded=FALSE}else{destranded=TRUE}
new("methylBase",new.data,sample.ids=object@sample.ids,
assembly=object@assembly,context=object@context,treatment=object@treatment,
coverage.index=object@coverage.index,numCs.index=object@numCs.index,
numTs.index=object@numTs.index,destranded=destranded,
resolution="region")
}
)
# RETURNS a new methylRawList object
# gets regional counts for all elements in methylRawList for given regions
# MAKE SURE an element of the list, which will be a set of GRanges rows,
# are not on different chromsomes and strands
# Also, make sure id column of returned methylRaw object is unique
# you can add refseq id to the id column: chr.start.end.refseqid
# @param object a \code{methylRaw} object
# @param regions a GRangesList object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRaw,GRangesList-method
# assume that each name of the element in the GRangesList is unique and
setMethod("regionCounts", signature(object="methylRaw",regions="GRangesList"),
function(object,regions,cov.bases,strand.aware){
#require(GenomicRanges)
# overlap object with regions
# convert object to GRanges
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
if(strand.aware){
g.meth=as(object,"GRanges")
strand(g.meth)="*"
mat=IRanges::as.matrix( findOverlaps(regions,g.meth ) )
#mat=matchMatrix( findOverlaps(regions,g.meth ) )
}else{
mat=IRanges::as.matrix( findOverlaps(regions,as(object,"GRanges")) )
#mat=matchMatrix( findOverlaps(regions,as(object,"GRanges")) )
}
#require(data.table)
# create a temporary data.table row ids from regions and counts from object
dt=data.table::data.table(id=mat[,1],getData(object)[mat[,2],c(5,6,7)] )
# use data.table to sum up counts per region
sum.dt=dt[,list(coverage=sum(coverage),
numCs =sum(numCs),
numTs =sum(numTs),covered=length(numTs)),by=id]
sum.dt=sum.dt[sum.dt$covered>=cov.bases,]
#temp.df=as.data.frame(regions) # get regions to a dataframe
temp.dt=data.table(as.data.frame(regions))
first.element = function(x) x[1]
sum.temp.dt=temp.dt[, list(chr = first.element(seqnames),
start = min(start),
end = max(end),
strand = first.element(strand)),
by=element]
# if it is intron, some of the symbols may not have any intron GRanges
sum.id=names(regions)[sum.dt$id]
#create a new methylRaw object to return
new.data=data.frame(
#id =sum.id,
chr =sum.temp.dt$chr[sum.temp.dt$element %in% sum.id],
start =sum.temp.dt$start[sum.temp.dt$element %in% sum.id],
end =sum.temp.dt$end[sum.temp.dt$element %in% sum.id],
strand =sum.temp.dt$strand[sum.temp.dt$element %in% sum.id],
coverage=sum.dt$coverage,
numCs =sum.dt$numCs,
numTs =sum.dt$numTs)
new("methylRaw",new.data,sample.id=object@sample.id,
assembly=object@assembly,context=object@context,
resolution="region")
}
)
# Note: some genes do not have intron, need to take care of it.
# GETs regional counts for given GRanges object
# RETURNS a new methylRawList object
# @param object a \code{methylRawList} object
# @param regions a GRanges object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRawList,GRanges-method
setMethod("regionCounts", signature(object="methylRawList",regions="GRanges"),
function(object,regions,cov.bases,strand.aware){
outList=list()
for(i in 1:length(object))
{
obj = regionCounts(object = object[[i]],
regions=regions,
cov.bases,strand.aware)
outList[[i]] = obj
}
myobj=new("methylRawList", outList,treatment=object@treatment)
myobj
}
)
# GETs regional counts for given GRangesList object
# RETURNS a new methylRawList object
# @param object a \code{methylRawList} object
# @param regions a GRangesList object.
#' @rdname regionCounts
#' @aliases regionCounts,methylRawList,GRangesList-method
setMethod("regionCounts", signature(object="methylRawList",
regions="GRangesList"),
function(object,regions,cov.bases,strand.aware){
outList=list()
for(i in 1:length(object))
{
obj = regionCounts(object = object[[i]],regions=regions,
cov.bases,strand.aware)
outList[[i]] = obj
}
myobj=new("methylRawList", outList,treatment=object@treatment)
myobj
}
)
#' Get methylated/unmethylated base counts for tilling windows
#'
#' The function summarizes methylated/unmethylated base counts over tilling
#' windows accross genome. This function can be used when differential
#' methylated analysis is preferable to tilling windows instead of base pairs.
#'
#' @param object \code{\link{methylRaw}}, \code{\link{methylRawList}} or \code{\link{methylBase}}
#' object containing base pair resolution methylation information
#' @param win.size an integer for the size of the tiling windows
#' @param step.size an integer for the step size of tiling windows
#' @param cov.bases minimum number of bases to be covered in a given window
#' @usage tileMethylCounts(object,win.size=1000,step.size=1000,cov.bases=0)
#' @return \code{methylRaw},\code{methylBase} or \code{methylRawList} object
#' @export
#' @examples
#' data(methylKit)
#'
#' tiled.methylRaw=tileMethylCounts(object=methylRawList.obj,win.size=1000,
#' step.size=1000,cov.bases=0)
#'
#'
#' @docType methods
#' @rdname tileMethylCounts-methods
setGeneric("tileMethylCounts",
function(object,win.size=1000,step.size=1000,cov.bases=0)
standardGeneric("tileMethylCounts") )
#' @aliases tileMethylCounts,methylRaw-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylRaw"),
function(object,win.size,step.size,cov.bases){
g.meth =as(object,"GRanges")
chrs =IRanges::levels(seqnames(g.meth))
widths =seqlengths(g.meth)
all.wins=GRanges()
for(i in 1:length(chrs))
{
# get max length of feature covered chromosome
max.length=max(IRanges::end(g.meth[seqnames(g.meth)==chrs[i],]))
#get sliding windows with covered CpGs
numTiles=floor( (max.length-(win.size-step.size) )/step.size )+1
temp.wins=GRanges(seqnames=rep(chrs[i],numTiles),
ranges=IRanges(start=1+0:(numTiles-1)*step.size,
width=rep(win.size,numTiles)) )
all.wins=suppressWarnings(c(all.wins,temp.wins))
}
regionCounts(object,all.wins,cov.bases,strand.aware=FALSE)
}
)
#' @aliases tileMethylCounts,methylRawList-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylRawList"),
function(object,win.size,step.size,cov.bases){
new.list=lapply(object,tileMethylCounts,win.size,step.size,cov.bases)
new("methylRawList", new.list,treatment=object@treatment)
})
#' @aliases tileMethylCounts,methylBase-method
#' @rdname tileMethylCounts-methods
setMethod("tileMethylCounts", signature(object="methylBase"),
function(object,win.size,step.size,cov.bases){
g.meth =as(object,"GRanges")
chrs =IRanges::levels(seqnames(g.meth))
widths =seqlengths(g.meth)
all.wins=GRanges()
for(i in 1:length(chrs))
{
# get max length of feature covered chromosome
max.length=max(IRanges::end(g.meth[seqnames(g.meth)==chrs[i],]))
#get sliding windows with covered CpGs
numTiles=floor( (max.length-(win.size-step.size) )/step.size )+1
temp.wins=GRanges(seqnames=rep(chrs[i],numTiles),
ranges=IRanges(start=1+0:(numTiles-1)*step.size,
width=rep(win.size,numTiles)) )
all.wins=suppressWarnings(c(all.wins,temp.wins))
}
regionCounts(object,all.wins,cov.bases,strand.aware=FALSE)
}
)
# back up code:
# annotation of methylation states by genes
# including promoter, intron, exon, nearest cpgi and cpgi shore.
# valPerIndex=function(query, subject, col="cov"){
# cov.per=value.per.location(query,subject,col=col)
# all.ind=1:length(query)
# rm=data.frame(ind=all.ind[!all.ind %in% (cov.per[,1])],
# val=rep(0,length(all.ind[!all.ind %in% (cov.per[,1])]) ),
# numRows=rep(0,length(all.ind[!all.ind %in% (cov.per[,1])]) ) )
# cov.per2=rbind(cov.per,rm)
# cov.per3=data.frame(cov.per2, tx_id=names(query)[cov.per2[,1]])
# return(cov.per3)
# }
#
# meth.ratio=function(meth.idx, cov.idx){
# if(identical(meth.idx[,1], cov.idx[,1])){
# ratio = meth.idx[,2]/cov.idx[,2]
# names(ratio) = meth.idx[,4]
# return(ratio)
# }
# else stop("index does not match")
# }
#
# # using exons
# meth.idx.exons=valPerIndex(exonRanges, g.meth, col="methCs")
# cov.idx.exons=valPerIndex(exonRanges, g.meth, col="cov")
#
# exon.meth.ratio=meth.ratio(meth.idx.exons, cov.idx.exons)
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