R/group.R

In mappoly: Genetic Linkage Maps in Autopolyploids

#' Assign markers to linkage groups
#'
#' Identifies linkage groups of markers using the results of two-point
#' (pairwise) analysis.
#'
#' @param input.mat an object of class \code{mappoly.rf.matrix}
#'
#' @param expected.groups when available, inform the number of expected 
#' linkage groups (i.e. chromosomes) for the species
#'
#' @param inter if \code{TRUE} (default), plots a dendrogram highlighting the
#'    expected groups before continue
#'
#' @param comp.mat if \code{TRUE}, shows a comparison between the reference
#'     based and the linkage based grouping, if the chromosome information is
#'     available (default = FALSE)
#'     
#' @param LODweight if \code{TRUE}, clusterization is weighted by the square of the 
#'     LOD Score
#'
#' @param verbose logical. If \code{TRUE} (default), current progress is shown;
#'     if \code{FALSE}, no output is produced
#'
#' @return Returns an object of class \code{mappoly.group}, which is a list
#'     containing the following components:
#'     \item{data.name}{the referred dataset name}
#'     \item{hc.snp}{a list containing information related to 
#'     the UPGMA grouping method}
#'     \item{expected.groups}{the number of expected linkage groups}
#'     \item{groups.snp}{the groups to which each of the markers belong}
#'     \item{seq.vs.grouped.snp}{comparison between the genomic group information
#'     (when available) and the groups provided by \code{group_mappoly}}
#'     \item{chisq.pval.thres}{the threshold used on the segregation test when reading the dataset}
#'     \item{chisq.pval}{the p-values associated with the segregation test for all markers in the sequence}
#'
#' @examples
#'     ## Getting first 20 markers from two linkage groups
#'     all.mrk <- make_seq_mappoly(hexafake, c(1:20,601:620))
#'     red.mrk <- elim_redundant(all.mrk)
#'     unique.mrks <- make_seq_mappoly(red.mrk)
#'     counts <- cache_counts_twopt(unique.mrks, cached = TRUE)
#'     all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
#'                                  count.cache = counts,
#'                                  ncpus = 1,
#'                                  verbose = TRUE)
#'
#'     ## Full recombination fraction matrix
#'     mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
#'     plot(mat.full, index = FALSE)
#'
#'     lgs <- group_mappoly(input.mat = mat.full,
#'                          expected.groups = 2,
#'                          inter = TRUE,
#'                          comp.mat = TRUE, #this data has physical information
#'                          verbose = TRUE)
#'     lgs
#'     plot(lgs)
#'    
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}
#'
#' @references
#'     Mollinari, M., and Garcia, A.  A. F. (2019) Linkage
#'     analysis and haplotype phasing in experimental autopolyploid
#'     populations with high ploidy level using hidden Markov
#'     models, _G3: Genes, Genomes, Genetics_. 
#'     \doi{10.1534/g3.119.400378} 
#'
#' @importFrom graphics abline pie
#' @importFrom stats as.dendrogram as.dist cutree hclust lm predict quantile rect.hclust
#' @importFrom dendextend color_branches
#' @export group_mappoly

group_mappoly <- function(input.mat, expected.groups = NULL,
                          inter = TRUE, comp.mat = FALSE, LODweight = FALSE, 
                          verbose = TRUE)
{
  ## checking for correct object
  input_classes <- c("mappoly.rf.matrix")
  if (!inherits(input.mat, input_classes)) {
    stop(deparse(substitute(input.mat)), " is not an object of class 'mappoly.rf.matrix'")
  }
  # if(is.null(input.seq))
  input.seq <- make_seq_mappoly(input.obj = get(input.mat$data.name, pos = 1), 
                                arg = rownames(input.mat$rec.mat), 
                                data.name = input.mat$data.name)
  if(!setequal(intersect(input.seq$seq.mrk.names,colnames(input.mat$rec.mat)), input.seq$seq.mrk.names)){
    stop(deparse(substitute(input.mat)), " does not contain all markers present in", deparse(substitute(input.seq)))
  }
  MSNP <- input.mat$rec.mat[input.seq$seq.mrk.names, input.seq$seq.mrk.names]
  mn <- input.seq$chrom
  mn[is.na(mn)] <- "NH"
  dimnames(MSNP) <- list(mn, mn)
  diag(MSNP) <- 0
  MSNP[is.na(MSNP)] <- .5
  if(LODweight){
    Mlod <- input.mat$lod.mat^2
    Mlod[is.na(Mlod)] <- 10e-5
    hc.snp <- hclust(as.dist(MSNP), method="ward.D2", members=apply(Mlod, 1, mean, na.rm = TRUE))      
  } else {
    hc.snp <- hclust(as.dist(MSNP), method = "average")
  }
  ANSWER <- "flag"
  if(interactive() && inter)
  {
    dend.snp <- as.dendrogram(hc.snp)
    while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER  != "")
    {
      dend1 <- dendextend::color_branches(dend.snp, k = expected.groups)
      plot(dend1, leaflab = "none")
      if(is.null(expected.groups))
        expected.group <- as.numeric(readline("Enter the number of expected groups: "))
      z <- rect.hclust(hc.snp, k = expected.groups, border = "red")
      groups.snp  <- cutree(tree = hc.snp, k = expected.groups)
      xy <- sapply(z, length)
      xt <- as.numeric(cumsum(xy)-ceiling(xy/2))
      yt <- .1
      points(x = xt, y = rep(yt, length(xt)), cex = 6, pch = 20, col = "lightgray")
      text(x = xt, y = yt, labels = pmatch(xy, table(groups.snp, useNA = "ifany")), adj = .5)
      ANSWER <- readline("Enter 'Y/n' to proceed or update the number of expected groups: ")
      if(substr(ANSWER, 1, 1)  ==  "n" | substr(ANSWER, 1, 1)  ==  "no" | substr(ANSWER, 1, 1)  ==  "N")
        stop("You decided to stop the function.")
      if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER  != "")
        expected.groups <- as.numeric(ANSWER)
    }
  }
  if(is.null(expected.groups))
    stop("Inform the 'expected.groups' or use 'inter = TRUE'")
  
  # Distribution of SNPs into linkage groups
  seq.vs.grouped.snp <- NULL
  if(all(unique(mn)  ==  "NH") & comp.mat)
  {
    comp.mat <- FALSE
    seq.vs.grouped.snp <- NULL
    warning("There is no physical reference to generate a comparison matrix")
  }
  groups.snp  <- cutree(tree = hc.snp, k = expected.groups)
  if(comp.mat){
    seq.vs.grouped.snp <- matrix(0, expected.groups, length(na.omit(unique(input.seq$chrom)))+1,
                                 dimnames = list(1:expected.groups, c(na.omit(unique(input.seq$chrom)),"NH")))
    for(i in 1:expected.groups)
    {
      x <- table(names(which(groups.snp == i)))
      seq.vs.grouped.snp[i,names(x)] <- x
    }
    idtemp2 <- unique(apply(seq.vs.grouped.snp, 1, which.max))
    idtemp2 <- c(idtemp2, setdiff(1:(ncol(seq.vs.grouped.snp)-1), idtemp2))
    seq.vs.grouped.snp <- cbind(seq.vs.grouped.snp[,idtemp2], seq.vs.grouped.snp[,"NH"])
    cnm <- colnames(seq.vs.grouped.snp)
    cnm[cnm == ""] <- "NoChr"
    colnames(seq.vs.grouped.snp) <- cnm
  } else {
    seq.vs.grouped.snp <- NULL
  }
  names(groups.snp) <- input.seq$seq.num
  structure(list(data.name = input.mat$data.name, 
                 hc.snp = hc.snp, 
                 expected.groups = expected.groups,
                 groups.snp = groups.snp, 
                 seq.vs.grouped.snp = seq.vs.grouped.snp, 
                 chisq.pval.thres = input.seq$chisq.pval.thres, 
                 chisq.pval = input.seq$chisq.pval),
            class = "mappoly.group")
}

#' @export
print.mappoly.group <- function(x, detailed = TRUE, ...) {
  cat("  This is an object of class 'mappoly.group'\n  ------------------------------------------\n")
  ## criteria
  cat("  Criteria used to assign markers to groups:\n\n")
  cat("    - Number of markers:         ", length(x$groups.snp), "\n")
  cat("    - Number of linkage groups:  ", length(unique(x$groups.snp)), "\n")
  cat("    - Number of markers per linkage groups: \n")
  w <- data.frame(table(x$groups.snp, useNA = "ifany"))
  colnames(w) = c("   group", "n.mrk")
  print (w, row.names = FALSE)
  cat("  ------------------------------------------\n")
  
  ## printing summary
  if(!is.null(x$seq.vs.grouped.snp)){
    print(x$seq.vs.grouped.snp)
    cat("  ------------------------------------------\n")
  }
}

#' @export
plot.mappoly.group <- function(x, ...) {
  dend <- as.dendrogram(x$hc.snp)
  dend1 <- dendextend::color_branches(dend, k = x$expected.groups)
  plot(dend1, leaflab = "none")
  z <- rect.hclust(x$hc.snp, k = x$expected.groups, border = "red")
  xy <- sapply(z, length)
  xt <- as.numeric(cumsum(xy)-ceiling(xy/2))
  yt <- .1
  points(x = xt, y = rep(yt, length(xt)), cex = 6, pch = 20, col = "lightgray")
  text(x = xt, y = yt, labels = pmatch(xy, table(x$groups.snp, useNA = "ifany")), adj = .5)
}