Nothing
R/DMRcaller.R
In DMRcaller: Differentially Methylated Regions caller
#' Call Differentially Methylated Regions (DMRs) between two samples
#'
#' Uses bisulfite sequencing data in two conditions and identifies
#' differentially methylated regions between the conditions in CG and
#' non-CG context. The input is the CX report files produced by Bismark
#' and the output is a list of DMRs stored as GRanges objects.
#'
#' The most important functions in the \pkg{DMRcaller} are:
#' \describe{
#' \item{\code{\link{readBismark}}}{reads the Bismark CX report files in a
#' \code{\link{GRanges}} object.}
#' \item{\code{\link{readBismarkPool}}}{Reads multiple CX report files and
#' pools them together.}
#' \item{\code{\link{saveBismark}}}{saves the methylation data stored in a
#' \code{\link{GRanges}} object into a Bismark CX report file.}
#' \item{\code{\link{poolMethylationDatasets}}}{pools together multiple
#' methylation datasets.}
#' \item{\code{\link{poolTwoMethylationDatasets}}}{pools together two
#' methylation datasets.}
#' \item{\code{\link{computeMethylationDataCoverage}}}{Computes the coverage
#' for the bisulfite sequencing data.}
#' \item{\code{\link{plotMethylationDataCoverage}}}{Plots the coverage for the
#' bisulfite sequencing data.}
#' \item{\code{\link{computeMethylationDataSpatialCorrelation}}}{Computes the
#' correlation between methylation levels as a function of the distances
#' between the Cytosines.}
#' \item{\code{\link{plotMethylationDataSpatialCorrelation}}}{Plots the
#' correlation of methylation levels for Cytosines located at a certain
#' distance apart.}
#' \item{\code{\link{computeMethylationProfile}}}{Computes the low resolution
#' profiles for the bisulfite sequencing data at certain locations.}
#' \item{\code{\link{plotMethylationProfile}}}{Plots the low resolution
#' profiles for the bisulfite sequencing data at certain locations.}
#' \item{\code{\link{plotMethylationProfileFromData}}}{Plots the low resolution
#' profiles for the loaded bisulfite sequencing data.}
#' \item{\code{\link{computeDMRs}}}{Computes the differentially methylated
#' regions between two conditions.}
#' \item{\code{\link{filterDMRs}}}{Filters a list of (potential) differentially
#' methylated regions.}
#' \item{\code{\link{mergeDMRsIteratively}}}{Merge DMRs iteratively.}
#' \item{\code{\link{analyseReadsInsideRegionsForCondition}}}{Analyse reads
#' inside regions for condition.}
#' \item{\code{\link{plotLocalMethylationProfile}}}{Plots the methylation
#' profile at one locus for the bisulfite sequencing data.}
#' \item{\code{\link{computeOverlapProfile}}}{Computes the distribution of a
#' set of subregions on a large region.}
#' \item{\code{\link{plotOverlapProfile}}}{Plots the distribution of a set of
#' subregions on a large region.}
#' \item{\code{\link{getWholeChromosomes}}}{Computes the GRanges objects with
#' each chromosome as an element from the methylationData.}
#' \item{\code{\link{joinReplicates}}}{Merges two GRanges objects with single
#' reads columns. It is necessary to start the analysis of DMRs with
#' biological replicates.}
#' \item{\code{\link{computeDMRsReplicates}}}{Computes the differentially
#' methylated regions between two conditions with multiple
#' biological replicates.}
#' }
#'
#' @author
#' Nicolae Radu Zabet \email{n.r.zabet@@gen.cam.ac.uk},
#' Jonathan Michael Foonlan Tsang \email{jmft2@@cam.ac.uk}
#' Alessandro Pio Greco \email{apgrec@@essex.ac.uk}
#'
#' Maintainer: Nicolae Radu Zabet \email{n.r.zabet@@gen.cam.ac.uk}
#' @name DMRcaller
#' @docType package
#' @import GenomicRanges
#' @import IRanges
#' @import S4Vectors
#' @import Rcpp
#' @importFrom RcppRoll roll_sum
#' @importFrom parallel mclapply
#' @examples
#' \dontrun{
#' # load the methylation data
#' data(methylationDataList)
#'
#' #plot the low resolution profile at 5 Kb resolution
#' par(mar=c(4, 4, 3, 1)+0.1)
#' plotMethylationProfileFromData(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' conditionsNames=c("WT", "met1-3"),
#' windowSize = 5000, autoscale = TRUE,
#' context = c("CG", "CHG", "CHH"),
#' labels = LETTERS)
#'
#' # compute low resolution profile in 10 Kb windows in CG context
#' lowResProfileWTCG <- computeMethylationProfile(methylationDataList[["WT"]],
#' region, windowSize = 10000, context = "CG")
#'
#' lowResProfileMet13CG <- computeMethylationProfile(
#' methylationDataList[["met1-3"]], region,
#' windowSize = 10000, context = "CG")
#'
#' lowResProfileCG <- GRangesList("WT" = lowResProfileWTCG,
#' "met1-3" = lowResProfileMet13CG)
#'
#' # compute low resolution profile in 10 Kb windows in CHG context
#' lowResProfileWTCHG <- computeMethylationProfile(methylationDataList[["WT"]],
#' region, windowSize = 10000, context = "CHG")
#'
#' lowResProfileMet13CHG <- computeMethylationProfile(
#' methylationDataList[["met1-3"]], region,
#' windowSize = 10000, context = "CHG")
#'
#' lowResProfileCHG <- GRangesList("WT" = lowResProfileWTCHG,
#' "met1-3" = lowResProfileMet13CHG)
#'
#' # plot the low resolution profile
#' par(mar=c(4, 4, 3, 1)+0.1)
#' par(mfrow=c(2,1))
#' plotMethylationProfile(lowResProfileCG, autoscale = FALSE,
#' labels = LETTERS[1],
#' title="CG methylation on Chromosome 3",
#' col=c("#D55E00","#E69F00"), pch = c(1,0),
#' lty = c(4,1))
#' plotMethylationProfile(lowResProfileCHG, autoscale = FALSE,
#' labels = LETTERS[2],
#' title="CHG methylation on Chromosome 3",
#' col=c("#0072B2", "#56B4E9"), pch = c(16,2),
#' lty = c(3,2))
#'
#' # plot the coverage in all three contexts
#' plotMethylationDataCoverage(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' breaks = 1:15, regions = NULL,
#' conditionsNames = c("WT","met1-3"),
#' context = c("CG", "CHG", "CHH"),
#' proportion = TRUE, labels = LETTERS, col = NULL,
#' pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
#' contextPerRow = FALSE)
#'
#' # plot the correlation of methylation levels as a function of distance
#' plotMethylationDataSpatialCorrelation(methylationDataList[["WT"]],
#' distances = c(1,5,10,15), regions = NULL,
#' conditionsNames = c("WT","met1-3"),
#' context = c("CG"),
#' labels = LETTERS, col = NULL,
#' pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
#' contextPerRow = FALSE)
#'
#' # the regions where to compute the DMRs
#' regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E6))
#'
#' # compute the DMRs in CG context with noise_filter method
#' DMRsNoiseFilterCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "noise_filter",
#' windowSize = 100, kernelFunction = "triangular",
#' test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#' cores = 1)
#'
#' # compute the DMRs in CG context with neighbourhood method
#' DMRsNeighbourhoodCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "neighbourhood",
#' test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#' cores = 1)
#'
#' # compute the DMRs in CG context with bins method
#' DMRsBinsCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "bins", binSize = 100,
#' test = "score", pValueThreshold = 0.01, minCytosinesCount = 4,
#' minProportionDifference = 0.4, minGap = 200, minSize = 50,
#' minReadsPerCytosine = 4, cores = 1)
#'
#' # load the gene annotation data
#' data(GEs)
#'
#' #select the genes
#' genes <- GEs[which(GEs$type == "gene")]
#'
#' # the regions where to compute the DMRs
#' genes <- genes[overlapsAny(genes, regions)]
#'
#' # filter genes that are differntially methylated in the two conditions
#' DMRsGenesCG <- filterDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], potentialDMRs = genes,
#' context = "CG", test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minReadsPerCytosine = 3, cores = 1)
#'
#' #merge the DMRs
#' DMRsNoiseFilterCGLarger <- mergeDMRsIteratively(DMRsNoiseFilterCG,
#' minGap = 500, respectSigns = TRUE,
#' methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' context = "CG", minProportionDifference=0.4,
#' minReadsPerCytosine = 1, pValueThreshold=0.01,
#' test="score",alternative = "two.sided")
#'
#' #select the genes
#' genes <- GEs[which(GEs$type == "gene")]
#'
#' # the coordinates of the area to be plotted
#' chr3Reg <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(510000,530000))
#'
#' # load the DMRs in CG context
#' data(DMRsNoiseFilterCG)
#'
#' DMRsCGlist <- list("noise filter"=DMRsNoiseFilterCG,
#' "neighbourhood"=DMRsNeighbourhoodCG,
#' "bins"=DMRsBinsCG,
#' "genes"=DMRsGenesCG)
#'
#'
#' # plot the CG methylation
#' par(mar=c(4, 4, 3, 1)+0.1)
#' par(mfrow=c(1,1))
#' plotLocalMethylationProfile(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], chr3Reg,
#' DMRsCGlist, c("WT", "met1-3"), GEs,
#' windowSize=100, main="CG methylation")
#'
#'
#' hotspotsHypo <- computeOverlapProfile(
#' DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "loss")],
#' region, windowSize=2000, binary=TRUE, cores=1)
#'
#' hotspotsHyper <- computeOverlapProfile(
#' DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "gain")],
#' region, windowSize=2000, binary=TRUE, cores=1)
#'
#' plotOverlapProfile(GRangesList("Chr3"=hotspotsHypo),
#' GRangesList("Chr3"=hotspotsHyper),
#' names=c("loss", "gain"), title="CG methylation")
#' # loading synthetic data
#' data("syntheticDataReplicates")
#'
#' # creating condition vector
#' condition <- c("a", "a", "b", "b")
#'
#' # computing DMRs using the neighbourhood method
#' DMRsReplicatesNeighbourhood <- computeDMRsReplicates(methylationData = methylationData,
#' condition = condition,
#' regions = NULL,
#' context = "CHH",
#' method = "neighbourhood",
#' test = "betareg",
#' pseudocountM = 1,
#' pseudocountN = 2,
#' pValueThreshold = 0.01,
#' minCytosinesCount = 4,
#' minProportionDifference = 0.4,
#' minGap = 200,
#' minSize = 50,
#' minReadsPerCytosine = 4,
#' cores = 1)
#' }
#'
#'
#' @seealso See \code{vignette("rd", package = "DMRcaller")} for an overview
#' of the package.
NULL
#' @name syntheticDataReplicates
#' @title Simulated data for biological replicates
#' @docType data
#' @description
#' A \code{GRanges} object containing simulated date for methylation in four
#' samples. The conditions assciated witch each sample are a, a, b and b.
#'
#' @format A \code{\link{GRanges}} object containing multiple metadata
#' columns with the reads from each object passed as parameter
#'
#' @source The object was created by calling \code{\link{joinReplicates}}
#' function.
NULL
#' @name GEs
#' @title The genetic elements data
#' @docType data
#' @description
#' A \code{GRanges} object containing the annotation of the Arabidopsis thaliana
#'
#' @format A \code{GRanges} object
#'
#' @source The object was created by calling \code{import.gff3} function
#' from \code{rtracklayer} package for
#' \url{ftp://ftp.arabidopsis.org/Maps/gbrowse_data/TAIR10/TAIR10_GFF3_genes_transposons.gff}
NULL
#' @name methylationDataList
#' @title The methylation data list
#' @description
#' A \code{GRangesList} object containing the methylation data at each cytosine
#' location in the genome in Wild Type (WT) and met1-3 mutant (met1-3) in
#' Arabidopsis thaliana. The data only contains the first 1 Mbp from Chromosome 3.
#'
#' @format The \code{GRanges} elements contain four metadata columns
#' \describe{
#' \item{context}{the context in which the DMRs are computed (\code{"CG"},
#' \code{"CHG"} or \code{"CHH"}).}
#' \item{readsM}{the number of methylated reads.}
#' \item{readsN}{the total number of reads.}
#' \item{trinucleotide_context}{the specific context of the cytosine (H is
#' replaced by the actual nucleotide).}
#' }
#'
#' @source Each element was created by by calling \code{\link{readBismark}}
#' function on the CX report files generated by Bismark
#' \url{http://www.bioinformatics.babraham.ac.uk/projects/bismark/}
#' for \url{http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM980986} dataset
#' in the case of Wild Type (WT) and
#' \url{http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM981032}
#' dataset in the case of met1-3 mutant (met1-3).
NULL
#' @name DMRsNoiseFilterCG
#' @title The DMRs between WT and met1-3 in CG context
#' @description
#' A \code{GRangesList} object containing the DMRs between Wild Type (WT) and
#' met1-3 mutant (met1-3) in Arabidopsis thaliana
#' (see \code{\link{methylationDataList}}). The DMRs were computed on the first
#' 1 Mbp from Chromosome 3 with noise filter method using a triangular kernel
#' and a windowSize of 100 bp
#'
#' @format The \code{\link{GRanges}} element contain 11 metadata columns;
#' see \code{\link{computeDMRs}}
#' @seealso \code{\link{filterDMRs}}, \code{\link{computeDMRs}},
#' \code{\link{analyseReadsInsideRegionsForCondition}}
#' and \code{\link{mergeDMRsIteratively}}
#'
NULL
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#' Call Differentially Methylated Regions (DMRs) between two samples
#'
#' Uses bisulfite sequencing data in two conditions and identifies
#' differentially methylated regions between the conditions in CG and
#' non-CG context. The input is the CX report files produced by Bismark
#' and the output is a list of DMRs stored as GRanges objects.
#'
#' The most important functions in the \pkg{DMRcaller} are:
#' \describe{
#' \item{\code{\link{readBismark}}}{reads the Bismark CX report files in a
#' \code{\link{GRanges}} object.}
#' \item{\code{\link{readBismarkPool}}}{Reads multiple CX report files and
#' pools them together.}
#' \item{\code{\link{saveBismark}}}{saves the methylation data stored in a
#' \code{\link{GRanges}} object into a Bismark CX report file.}
#' \item{\code{\link{poolMethylationDatasets}}}{pools together multiple
#' methylation datasets.}
#' \item{\code{\link{poolTwoMethylationDatasets}}}{pools together two
#' methylation datasets.}
#' \item{\code{\link{computeMethylationDataCoverage}}}{Computes the coverage
#' for the bisulfite sequencing data.}
#' \item{\code{\link{plotMethylationDataCoverage}}}{Plots the coverage for the
#' bisulfite sequencing data.}
#' \item{\code{\link{computeMethylationDataSpatialCorrelation}}}{Computes the
#' correlation between methylation levels as a function of the distances
#' between the Cytosines.}
#' \item{\code{\link{plotMethylationDataSpatialCorrelation}}}{Plots the
#' correlation of methylation levels for Cytosines located at a certain
#' distance apart.}
#' \item{\code{\link{computeMethylationProfile}}}{Computes the low resolution
#' profiles for the bisulfite sequencing data at certain locations.}
#' \item{\code{\link{plotMethylationProfile}}}{Plots the low resolution
#' profiles for the bisulfite sequencing data at certain locations.}
#' \item{\code{\link{plotMethylationProfileFromData}}}{Plots the low resolution
#' profiles for the loaded bisulfite sequencing data.}
#' \item{\code{\link{computeDMRs}}}{Computes the differentially methylated
#' regions between two conditions.}
#' \item{\code{\link{filterDMRs}}}{Filters a list of (potential) differentially
#' methylated regions.}
#' \item{\code{\link{mergeDMRsIteratively}}}{Merge DMRs iteratively.}
#' \item{\code{\link{analyseReadsInsideRegionsForCondition}}}{Analyse reads
#' inside regions for condition.}
#' \item{\code{\link{plotLocalMethylationProfile}}}{Plots the methylation
#' profile at one locus for the bisulfite sequencing data.}
#' \item{\code{\link{computeOverlapProfile}}}{Computes the distribution of a
#' set of subregions on a large region.}
#' \item{\code{\link{plotOverlapProfile}}}{Plots the distribution of a set of
#' subregions on a large region.}
#' \item{\code{\link{getWholeChromosomes}}}{Computes the GRanges objects with
#' each chromosome as an element from the methylationData.}
#' \item{\code{\link{joinReplicates}}}{Merges two GRanges objects with single
#' reads columns. It is necessary to start the analysis of DMRs with
#' biological replicates.}
#' \item{\code{\link{computeDMRsReplicates}}}{Computes the differentially
#' methylated regions between two conditions with multiple
#' biological replicates.}
#' }
#'
#' @author
#' Nicolae Radu Zabet \email{n.r.zabet@@gen.cam.ac.uk},
#' Jonathan Michael Foonlan Tsang \email{jmft2@@cam.ac.uk}
#' Alessandro Pio Greco \email{apgrec@@essex.ac.uk}
#'
#' Maintainer: Nicolae Radu Zabet \email{n.r.zabet@@gen.cam.ac.uk}
#' @name DMRcaller
#' @docType package
#' @import GenomicRanges
#' @import IRanges
#' @import S4Vectors
#' @import Rcpp
#' @importFrom RcppRoll roll_sum
#' @importFrom parallel mclapply
#' @examples
#' \dontrun{
#' # load the methylation data
#' data(methylationDataList)
#'
#' #plot the low resolution profile at 5 Kb resolution
#' par(mar=c(4, 4, 3, 1)+0.1)
#' plotMethylationProfileFromData(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' conditionsNames=c("WT", "met1-3"),
#' windowSize = 5000, autoscale = TRUE,
#' context = c("CG", "CHG", "CHH"),
#' labels = LETTERS)
#'
#' # compute low resolution profile in 10 Kb windows in CG context
#' lowResProfileWTCG <- computeMethylationProfile(methylationDataList[["WT"]],
#' region, windowSize = 10000, context = "CG")
#'
#' lowResProfileMet13CG <- computeMethylationProfile(
#' methylationDataList[["met1-3"]], region,
#' windowSize = 10000, context = "CG")
#'
#' lowResProfileCG <- GRangesList("WT" = lowResProfileWTCG,
#' "met1-3" = lowResProfileMet13CG)
#'
#' # compute low resolution profile in 10 Kb windows in CHG context
#' lowResProfileWTCHG <- computeMethylationProfile(methylationDataList[["WT"]],
#' region, windowSize = 10000, context = "CHG")
#'
#' lowResProfileMet13CHG <- computeMethylationProfile(
#' methylationDataList[["met1-3"]], region,
#' windowSize = 10000, context = "CHG")
#'
#' lowResProfileCHG <- GRangesList("WT" = lowResProfileWTCHG,
#' "met1-3" = lowResProfileMet13CHG)
#'
#' # plot the low resolution profile
#' par(mar=c(4, 4, 3, 1)+0.1)
#' par(mfrow=c(2,1))
#' plotMethylationProfile(lowResProfileCG, autoscale = FALSE,
#' labels = LETTERS[1],
#' title="CG methylation on Chromosome 3",
#' col=c("#D55E00","#E69F00"), pch = c(1,0),
#' lty = c(4,1))
#' plotMethylationProfile(lowResProfileCHG, autoscale = FALSE,
#' labels = LETTERS[2],
#' title="CHG methylation on Chromosome 3",
#' col=c("#0072B2", "#56B4E9"), pch = c(16,2),
#' lty = c(3,2))
#'
#' # plot the coverage in all three contexts
#' plotMethylationDataCoverage(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' breaks = 1:15, regions = NULL,
#' conditionsNames = c("WT","met1-3"),
#' context = c("CG", "CHG", "CHH"),
#' proportion = TRUE, labels = LETTERS, col = NULL,
#' pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
#' contextPerRow = FALSE)
#'
#' # plot the correlation of methylation levels as a function of distance
#' plotMethylationDataSpatialCorrelation(methylationDataList[["WT"]],
#' distances = c(1,5,10,15), regions = NULL,
#' conditionsNames = c("WT","met1-3"),
#' context = c("CG"),
#' labels = LETTERS, col = NULL,
#' pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
#' contextPerRow = FALSE)
#'
#' # the regions where to compute the DMRs
#' regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E6))
#'
#' # compute the DMRs in CG context with noise_filter method
#' DMRsNoiseFilterCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "noise_filter",
#' windowSize = 100, kernelFunction = "triangular",
#' test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#' cores = 1)
#'
#' # compute the DMRs in CG context with neighbourhood method
#' DMRsNeighbourhoodCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "neighbourhood",
#' test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#' cores = 1)
#'
#' # compute the DMRs in CG context with bins method
#' DMRsBinsCG <- computeDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], regions = regions,
#' context = "CG", method = "bins", binSize = 100,
#' test = "score", pValueThreshold = 0.01, minCytosinesCount = 4,
#' minProportionDifference = 0.4, minGap = 200, minSize = 50,
#' minReadsPerCytosine = 4, cores = 1)
#'
#' # load the gene annotation data
#' data(GEs)
#'
#' #select the genes
#' genes <- GEs[which(GEs$type == "gene")]
#'
#' # the regions where to compute the DMRs
#' genes <- genes[overlapsAny(genes, regions)]
#'
#' # filter genes that are differntially methylated in the two conditions
#' DMRsGenesCG <- filterDMRs(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], potentialDMRs = genes,
#' context = "CG", test = "score", pValueThreshold = 0.01,
#' minCytosinesCount = 4, minProportionDifference = 0.4,
#' minReadsPerCytosine = 3, cores = 1)
#'
#' #merge the DMRs
#' DMRsNoiseFilterCGLarger <- mergeDMRsIteratively(DMRsNoiseFilterCG,
#' minGap = 500, respectSigns = TRUE,
#' methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]],
#' context = "CG", minProportionDifference=0.4,
#' minReadsPerCytosine = 1, pValueThreshold=0.01,
#' test="score",alternative = "two.sided")
#'
#' #select the genes
#' genes <- GEs[which(GEs$type == "gene")]
#'
#' # the coordinates of the area to be plotted
#' chr3Reg <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(510000,530000))
#'
#' # load the DMRs in CG context
#' data(DMRsNoiseFilterCG)
#'
#' DMRsCGlist <- list("noise filter"=DMRsNoiseFilterCG,
#' "neighbourhood"=DMRsNeighbourhoodCG,
#' "bins"=DMRsBinsCG,
#' "genes"=DMRsGenesCG)
#'
#'
#' # plot the CG methylation
#' par(mar=c(4, 4, 3, 1)+0.1)
#' par(mfrow=c(1,1))
#' plotLocalMethylationProfile(methylationDataList[["WT"]],
#' methylationDataList[["met1-3"]], chr3Reg,
#' DMRsCGlist, c("WT", "met1-3"), GEs,
#' windowSize=100, main="CG methylation")
#'
#'
#' hotspotsHypo <- computeOverlapProfile(
#' DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "loss")],
#' region, windowSize=2000, binary=TRUE, cores=1)
#'
#' hotspotsHyper <- computeOverlapProfile(
#' DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "gain")],
#' region, windowSize=2000, binary=TRUE, cores=1)
#'
#' plotOverlapProfile(GRangesList("Chr3"=hotspotsHypo),
#' GRangesList("Chr3"=hotspotsHyper),
#' names=c("loss", "gain"), title="CG methylation")
#' # loading synthetic data
#' data("syntheticDataReplicates")
#'
#' # creating condition vector
#' condition <- c("a", "a", "b", "b")
#'
#' # computing DMRs using the neighbourhood method
#' DMRsReplicatesNeighbourhood <- computeDMRsReplicates(methylationData = methylationData,
#' condition = condition,
#' regions = NULL,
#' context = "CHH",
#' method = "neighbourhood",
#' test = "betareg",
#' pseudocountM = 1,
#' pseudocountN = 2,
#' pValueThreshold = 0.01,
#' minCytosinesCount = 4,
#' minProportionDifference = 0.4,
#' minGap = 200,
#' minSize = 50,
#' minReadsPerCytosine = 4,
#' cores = 1)
#' }
#'
#'
#' @seealso See \code{vignette("rd", package = "DMRcaller")} for an overview
#' of the package.
NULL
#' @name syntheticDataReplicates
#' @title Simulated data for biological replicates
#' @docType data
#' @description
#' A \code{GRanges} object containing simulated date for methylation in four
#' samples. The conditions assciated witch each sample are a, a, b and b.
#'
#' @format A \code{\link{GRanges}} object containing multiple metadata
#' columns with the reads from each object passed as parameter
#'
#' @source The object was created by calling \code{\link{joinReplicates}}
#' function.
NULL
#' @name GEs
#' @title The genetic elements data
#' @docType data
#' @description
#' A \code{GRanges} object containing the annotation of the Arabidopsis thaliana
#'
#' @format A \code{GRanges} object
#'
#' @source The object was created by calling \code{import.gff3} function
#' from \code{rtracklayer} package for
#' \url{ftp://ftp.arabidopsis.org/Maps/gbrowse_data/TAIR10/TAIR10_GFF3_genes_transposons.gff}
NULL
#' @name methylationDataList
#' @title The methylation data list
#' @description
#' A \code{GRangesList} object containing the methylation data at each cytosine
#' location in the genome in Wild Type (WT) and met1-3 mutant (met1-3) in
#' Arabidopsis thaliana. The data only contains the first 1 Mbp from Chromosome 3.
#'
#' @format The \code{GRanges} elements contain four metadata columns
#' \describe{
#' \item{context}{the context in which the DMRs are computed (\code{"CG"},
#' \code{"CHG"} or \code{"CHH"}).}
#' \item{readsM}{the number of methylated reads.}
#' \item{readsN}{the total number of reads.}
#' \item{trinucleotide_context}{the specific context of the cytosine (H is
#' replaced by the actual nucleotide).}
#' }
#'
#' @source Each element was created by by calling \code{\link{readBismark}}
#' function on the CX report files generated by Bismark
#' \url{http://www.bioinformatics.babraham.ac.uk/projects/bismark/}
#' for \url{http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM980986} dataset
#' in the case of Wild Type (WT) and
#' \url{http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM981032}
#' dataset in the case of met1-3 mutant (met1-3).
NULL
#' @name DMRsNoiseFilterCG
#' @title The DMRs between WT and met1-3 in CG context
#' @description
#' A \code{GRangesList} object containing the DMRs between Wild Type (WT) and
#' met1-3 mutant (met1-3) in Arabidopsis thaliana
#' (see \code{\link{methylationDataList}}). The DMRs were computed on the first
#' 1 Mbp from Chromosome 3 with noise filter method using a triangular kernel
#' and a windowSize of 100 bp
#'
#' @format The \code{\link{GRanges}} element contain 11 metadata columns;
#' see \code{\link{computeDMRs}}
#' @seealso \code{\link{filterDMRs}}, \code{\link{computeDMRs}},
#' \code{\link{analyseReadsInsideRegionsForCondition}}
#' and \code{\link{mergeDMRsIteratively}}
#'
NULL
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