R/process_haib_caltech_wrap.R

In andreaskapou/BPRMeth-devel: Model higher-order methylation profiles

#' Wrapper method for processing ENCODE HAIB and Caltech HTS data
#'
#' \code{process_haib_caltech_wrap} is a wrapper method for processing HTS data
#' and returning the methylation promoter regions and the corresponding gene
#' expression data for those promoter regions. Note that the format of BS-Seq
#' data should be in the Encode Haib bed format and for the RNA-Seq data in
#' Encode Caltech bed format.
#'
#' @param bs_files Filename (or vector of filenames if there are replicates) of
#'   the BS-Seq '.bed' formatted data to read values from.
#' @param rna_files Filename of the RNA-Seq '.bed' formatted data to read values
#'   from. Currently, this version does not support pooling RNA-Seq replicates.
#' @param chrom_size_file Optional filename containing genome chromosome sizes.
#' @param chr_discarded A vector with chromosome names to be discarded.
#' @param upstream Integer defining the length of bp upstream of TSS for
#'   creating the promoter region.
#' @param downstream Integer defining the length of bp downstream of TSS for
#'   creating the promoter region.
#' @param min_bs_cov The minimum number of reads mapping to each CpG site. CpGs
#'   with less reads will be considered as noise and will be discarded.
#' @param max_bs_cov The maximum number of reads mapping to each CpG site. CpGs
#'   with more reads will be considered as noise and will be discarded.
#' @param gene_log2_transf Logical, whether or not to log2 transform the gene
#'   expression data.
#' @param gene_outl_thresh Logical, whehter or not to remove outlier gene
#'   expression data.
#' @param gex_outlier Numeric, denoting the threshold above of which the gene
#'   expression data (before the log2 transformation) are considered as noise.
#' @inheritParams create_methyl_region
#'
#' @return A \code{processHTS} object which contains following information:
#'   \itemize{ \item{ \code{methyl_region}: A list containing methylation data,
#'   where each entry in the list is an \eqn{L_{i} X 3} dimensional matrix,
#'   where \eqn{L_{i}} denotes the number of CpGs found in region \code{i}. The
#'   columns contain the following information: \enumerate{ \item{ 1st column:
#'   Contains the locations of CpGs relative to TSS. Note that the actual
#'   locations are scaled to the (fmin, fmax) region. } \item{ 2nd column:
#'   Contains the total reads of each CpG in the corresponding location.} \item{
#'   3rd column: Contains the methylated reads each CpG in the corresponding
#'   location.} } } \item{\code{gex}: A vector containing the corresponding gene
#'   expression levels for each entry of the \code{methyl_region} list.} \item{
#'   \code{prom_region}: A \code{\link[GenomicRanges]{GRanges}} object
#'   containing corresponding annotated promoter regions for each entry of the
#'   \code{methyl_region} list. The GRanges object has one additional metadata
#'   column named \code{tss}, which stores the TSS of each promoter. } \item{
#'   \code{rna_data}: A \code{\link[GenomicRanges]{GRanges}} object containing
#'   the corresponding RNA-Seq data for each entry of the \code{methyl_region}
#'   list. The GRanges object has three additional metadata columns which are
#'   explained in \code{\link{read_rna_encode_caltech}}} \item{ \code{upstream}:
#'   Integer defining the length of bp upstream of TSS.} \item{
#'   \code{downstream}: Integer defining the length of bp downstream of TSS.}
#'   \item{ \code{cpg_density}: Integer defining the minimum number of CpGs that
#'   have to be in a methylated region. Regions with less than \code{n} CpGs are
#'   discarded.} \item{ \code{sd_thresh}: Numeric defining the minimum standard
#'   deviation of the methylation change in a region. This is used to filter
#'   regions with no methylation change.} \item{ \code{fmin}: Minimum range
#'   value for region location scaling.} \item{ \code{fmax}: Maximum range value
#'   for region location scaling.} }
#'
#' @author C.A.Kapourani \email{C.A.Kapourani@@ed.ac.uk}
#'
#' @examples
#' # Obtain the path to the files
#' rrbs_file <- system.file("extdata", "rrbs.bed", package = "BPRMeth")
#' rnaseq_file <- system.file("extdata", "rnaseq.bed", package = "BPRMeth")
#' proc_data <- process_haib_caltech_wrap(rrbs_file, rnaseq_file)
#'
#' @export
process_haib_caltech_wrap <- function(bs_files, rna_files,
                                      chrom_size_file = NULL,
                                      chr_discarded = NULL, upstream = -7000,
                                      downstream = 7000, min_bs_cov = 4,
                                      max_bs_cov = 1000, cpg_density = 10,
                                      sd_thresh = 10e-02, ignore_strand = TRUE,
                                      gene_log2_transf = TRUE,
                                      gene_outl_thresh = TRUE,
                                      gex_outlier = 300,
                                      fmin = -1, fmax = 1){


    # Process BS-Seq file and return data in the required format
    bs_data <- preprocess_bs_seq(files         = bs_files,
                                 file_format   = "encode_rrbs",
                                 chr_discarded = chr_discarded,
                                 min_bs_cov    = min_bs_cov,
                                 max_bs_cov    = max_bs_cov)

    # Read the chromosome size file, if it is supplied
    if (!is.null(chrom_size_file)){
        chrom_size <- read_chrom_size(file = chrom_size_file)
    }else{
        chrom_size <- NULL
    }

    # Read RNA-Seq BED file
    rna_data <- read_rna_encode_caltech(file          = rna_files,
                                        chr_discarded = chr_discarded,
                                        is_GRanges    = TRUE)

    # Create promoter regions
    prom_reg <- create_prom_region(annot_data = rna_data,
                                   chrom_size = chrom_size,
                                   upstream   = upstream,
                                   downstream = downstream)

    # Create methylation regions data
    methyl_reg <- create_methyl_region(bs_data       = bs_data,
                                       prom_region   = prom_reg,
                                       cpg_density   = cpg_density,
                                       sd_thresh     = sd_thresh,
                                       ignore_strand = ignore_strand,
                                       fmin          = fmin,
                                       fmax          = fmax)

#     # Keep only the corresponding gene expression data
#     rna_data <- rna_data[methyl_reg$prom_ind]
#     # Keep only the corresponding gene annotation data
#     prom_reg <- prom_reg[methyl_reg$prom_ind]

    proc_data <- preprocess_final_HTS_data(methyl_region = methyl_reg,
                                           prom_reg = prom_reg,
                                           rna_data = rna_data,
                                           gene_log2_transf = gene_log2_transf,
                                           gene_outl_thresh = gene_outl_thresh,
                                           gex_outlier = gex_outlier)

    # Create object
    obj <- structure(list(methyl_region = proc_data$methyl_region,
                          gex           = proc_data$gex,
                          prom_region   = proc_data$prom_reg,
                          rna_data      = proc_data$rna_data,
                          upstream      = upstream,
                          downstream    = downstream,
                          cpg_density   = cpg_density,
                          sd_thresh     = sd_thresh,
                          fmin          = fmin,
                          fmax          = fmax),
                     class = "processHTS")
    return(obj)
}