#' Find strand of mutations
#'
#' @details
#' For transcription mode:
#' Definitions of gene bodies with strand (+/-) information should be defined
#' in a GRanges object.
#'
#' For the base substitutions that are within gene bodies, it is determined whether
#' the "C" or "T" base is on the same strand as the gene definition. (Since
#' by convention we regard base substitutions as C>X or T>X.)
#'
#' Base substitutions on the same strand as the gene definitions are considered
#' "untranscribed", and on the opposite strand of gene bodies as "transcribed",
#' since the gene definitions report the coding or sense strand, which is
#' untranscribed.
#'
#' No strand information "-" is returned for base substitutions outside gene
#' bodies, or base substitutions that overlap with more than one gene body on
#' the same strand.
#'
#' For replication mode:
#' Replication directions of genomic ranges should be defined in GRanges object.
#' The GRanges object should have a "strand_info" metadata column,
#' which contains only two different annotations, e.g. "left" and "right", or
#' "leading" and "lagging". The genomic ranges cannot overlap, to allow only one
#' annotation per location.
#'
#' For each base substitution it is determined on which strand it is located.
#' No strand information "-" is returned for base substitutions in un-annotated
#' genomic regions.
#'
#' With the package we provide an example dataset, see example code.
#'
#'
#' @param vcf GRanges containing the VCF object
#' @param ranges GRanges object with the genomic ranges of:
#' 1. (transcription mode) the gene bodies with strand (+/-) information, or
#' 2. (replication mode) the replication strand with 'strand_info' metadata
#' @param mode "transcription" or "replication", default = "transcription"
#'
#' @return Character vector with transcriptional strand information with
#' length of vcf: "-" for positions outside gene bodies, "U" for
#' untranscribed/sense/coding strand, "T" for
#' transcribed/anti-sense/non-coding strand.
#'
#' @examples
#' ## For this example we need our variants from the VCF samples, and
#' ## a known genes dataset.  See the 'read_vcfs_as_granges()' example
#' ## for how to load the VCF samples.
#' vcfs <- readRDS(system.file("states/read_vcfs_as_granges_output.rds",
#'   package = "MutationalPatterns"
#' ))
#'
#' ## For transcription strand:
#' ## You can obtain the known genes from the UCSC hg19 dataset using
#' ## Bioconductor:
#' # source("https://bioconductor.org/biocLite.R")
#' # biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
#' library("TxDb.Hsapiens.UCSC.hg19.knownGene")
#' genes_hg19 <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
#'
#' mut_strand(vcfs[[1]], genes_hg19, mode = "transcription")
#'
#' ## For replication strand:
#' ## Read example bed file with replication direction annotation
#' ## Read replistrand data
#' repli_file <- system.file("extdata/ReplicationDirectionRegions.bed",
#'   package = "MutationalPatterns"
#' )
#' repli_strand <- read.table(repli_file, header = TRUE)
#' repli_strand_granges <- GRanges(
#'   seqnames = repli_strand$Chr,
#'   ranges = IRanges(
#'     start = repli_strand$Start + 1,
#'     end = repli_strand$Stop
#'   ),
#'   strand_info = repli_strand$Class
#' )
#' ## UCSC seqlevelsstyle
#' seqlevelsStyle(repli_strand_granges) <- "UCSC"
#'
#' mut_strand(vcfs[[1]], repli_strand_granges, mode = "transcription")
#' @seealso
#' \code{\link{read_vcfs_as_granges}},
#'
#' @export

mut_strand <- function(vcf, ranges, mode = "transcription") {
  # Transcription mode
  if (mode == "transcription") {
    # Reduce gene object to merge gene definitions that overlap on the same strand
    genes <- GenomicRanges::reduce(ranges)

    # Check consistency of chromosome names.
    if (!(all(GenomeInfoDb::seqlevels(vcf) %in% GenomeInfoDb::seqlevels(genes)))) {
      stop(paste(
        "Chromosome names (seqlevels) of vcf and genes Granges",
        "object do not match. Use the seqlevelsStyle() function",
        "to rename chromosome names."
      ), call. = FALSE)
    }

    # Determine overlap between vcf positions and genes
    overlap <- findOverlaps(vcf, genes)
    overlap <- as.data.frame(as.matrix(overlap))
    colnames(overlap) <- c("vcf_id", "gene_body_id")

    # Remove mutations that overlap with multiple genes and therefore cannot
    # be determined whether they are on transcribed or untranscribed strand
    # duplicated mutations.
    dup_pos <- overlap$vcf_id[duplicated(overlap$vcf_id)]

    # Index of duplicated mutations
    dup_idx <- which(overlap$vcf_id %in% dup_pos)

    # Remove all duplicated (non-unique mapping) mutations.
    if (length(dup_idx) > 0) {
      overlap <- overlap[-dup_idx, ]
    }

    # Subset of mutations in genes
    vcf_overlap <- vcf[overlap$vcf_id]

    # Find reference allele of mutations (and strand of reference genome is
    # reported in vcf file).
    ref <- .get_ref(vcf_overlap)

    # Find the strand of C or T (since we regard base substitutions as
    # C>X or T>X) which mutations have ref allele C or T.
    i <- which(ref == "C" | ref == "T")

    # Store mutation strand info in vector.
    strand_muts <- rep(0, nrow(overlap))
    strand_muts[i] <- "+"
    strand_muts[-i] <- "-"

    # Find strand of gene bodies of overlaps.
    strand_genebodies <- as.character(strand(genes)[overlap$gene_body_id])

    # Find if mut and gene_bodies are on the same strand.
    same_strand <- (strand_muts == strand_genebodies)

    # Subset vcf object for both untranscribed and transcribed
    # gene definition represents the untranscribed/sense/coding strand
    # if mutation is on same strand as gene, than its untranscribed.
    U_index <- which(same_strand == TRUE)

    # If mutation is on different strand than gene, then its transcribed.
    T_index <- which(same_strand == FALSE)
    strand <- rep(0, nrow(overlap))
    strand[U_index] <- "untranscribed"
    strand[T_index] <- "transcribed"

    # Make vector with all positions in input vcf for positions that do
    # not overlap with gene bodies, report "-".
    strand2 <- rep("-", length(vcf))
    strand2[overlap$vcf_id] <- strand
    # make factor
    strand2 <- factor(strand2, levels = c("untranscribed", "transcribed", "-"))
  }

  # Replication mode
  if (mode == "replication") {
    if (is.null(ranges$strand_info)) {
      stop("GRanges object with genomic regions does not contain 'strand_info' factor as metadata.")
    }

    # Manually set the levels of the factor.
    levels(ranges$strand_info) <- unique(ranges$strand_info)

    # Check that only two different annotations
    if (length(levels(ranges$strand_info)) != 2) {
      stop("GRanges object metadata: 'strand_info' factor should contain exactly two different 
           levels, such as 'left' and 'right'.")
    }
    overlap <- findOverlaps(vcf, ranges)
    overlap <- as.data.frame(as.matrix(overlap))
    colnames(overlap) <- c("vcf_id", "region_id")
    dup_pos <- overlap$vcf_id[duplicated(overlap$vcf_id)]
    dup_idx <- which(overlap$vcf_id %in% dup_pos)
    if (length(dup_idx) > 0) {
      overlap <- overlap[-dup_idx, ]
      warning("Some variants overlap with multiple genomic regions in the GRanges object.\n
              These variants are assigned '-', as the strand cannot be determined.\n
              To avoid this, make sure no genomic regions are overlapping in your GRanges object.")
    }

    # Combine the strand info from the mutation and the ranges.
    strand_repli <- ranges[overlap$region_id]$strand_info
    match_f <- BiocGenerics::match(.get_ref(vcf)[overlap$vcf_id], c("C", "T"), nomatch = 0L) > 0L
    strand_mut <- ifelse(match_f, "+", "-")
    strand_levels <- levels(ranges$strand_info)
    strand_f <- (strand_mut == "+" & strand_repli == strand_levels[1]) | (strand_mut == "-" & strand_repli == strand_levels[2])
    strand <- ifelse(strand_f, strand_levels[1], strand_levels[2])

    # Fill in the strand info where possible
    strand2 <- rep("-", length(vcf))
    strand2[overlap$vcf_id] <- strand
    levels <- c(levels(ranges$strand_info), "-")
    strand2 <- factor(strand2, levels = levels)
  }
  return(strand2)
}
