R/gl.report.parent.offspring.r

In dartR.captive: Analysing 'SNP' Data to Support Captive Breeding

#' @name gl.report.parent.offspring
#' @title Identifies putative parent offspring within a population
#' @description
#' This script examines the frequency of pedigree inconsistent loci, that is,
#' those loci that are homozygotes in the parent for the reference allele, and
#' homozygous in the offspring for the alternate allele. This condition is not
#' consistent with any pedigree, regardless of the (unknown) genotype of the
#' other parent. The pedigree inconsistent loci are counted as an indication of
#' whether or not it is reasonable to propose the two individuals are in a
#' parent-offspring relationship.
#' @param x Name of the genlight object containing the SNP genotypes [required].
#' @param min.rdepth Minimum read depth to include in analysis [default 12].
#' @param min.reproducibility Minimum reproducibility to include in analysis
#' [default 1].
#' @param range Specifies the range to extend beyond the interquartile range for
#' delimiting outliers [default 1.5 interquartile ranges].
#' @param plot.filters Whether to show the plots of filters within the function 
#' [default FALSE].
#' @param plot_theme Theme for the plot. See Details for options
#'  [default theme_dartR()].
#' @param plot_colors List of two color names for the borders and fill of the
#'  plots [default gl.colors(2)].
#' @param plot.dir Directory to save the plot RDS files [default as specified
#' by the global working directory or tempdir()]
#' @param plot.file Name for the RDS binary file to save (base name only,
#' exclude extension) [default NULL] Creates a plot that shows the sex linked markers.
#' @param verbose Verbosity: 0, silent or fatal errors; 1, begin and end; 2,
#' progress log; 3, progress and results summary; 5, full report
#' [default 2, unless specified using gl.set.verbosity].
#' @details
#' If two individuals are in a parent offspring relationship, the true number of
#' pedigree inconsistent loci should be zero, but SNP calling is not infallible.
#' Some loci will be miss-called. The problem thus becomes one of determining
#' if the two focal individuals have a count of pedigree inconsistent loci less
#' than would be expected of typical unrelated individuals. There are some quite
#' sophisticated software packages available to formally apply likelihoods to
#' the decision, but we use a simple outlier comparison.

#' To reduce the frequency of miss-calls, and so emphasize the difference
#' between true parent-offspring pairs and unrelated pairs, the data can be
#' filtered on read depth.

#' Typically minimum read depth is set to 5x, but you can examine the
#' distribution of read depths with the function \code{\link[dartR.base]{gl.report.rdepth}}
#' and push this up with an acceptable loss of loci. 12x might be a good minimum
#' for this particular analysis. It is sensible also to push the minimum
#' reproducibility up to 1, if that does not result in an unacceptable loss of
#' loci. Reproducibility is stored in the slot \code{@other$loc.metrics$RepAvg}
#' and is defined as the proportion of technical replicate assay pairs for which
#' the marker score is consistent. You can examine the distribution of
#'  reproducibility with the function \code{\link[dartR.base]{gl.report.reproducibility}}.

#' Note that the null expectation is not well defined, and the power reduced, if
#' the population from which the putative parent-offspring pairs are drawn
#' contains many sibs. Note also that if an individual has been genotyped twice
#' in the dataset, the replicate pair will be assessed by this script as being
#' in a parent-offspring relationship.

#' The function \code{\link{gl.filter.parent.offspring}} will filter out those
#' individuals in a parent offspring relationship.

#' Note that if your dataset does not contain RepAvg or rdepth among the locus
#' metrics, the filters for reproducibility and read depth are no used.
#'  Examples of other themes that can be used can be consulted in \itemize{
#'  \item \url{https://ggplot2.tidyverse.org/reference/ggtheme.html} and \item
#'  \url{https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/}
#'  }
#' @return A set of individuals in parent-offspring relationship. NULL if no
#' parent-offspring relationships were found.
#' @author Custodian: Arthur Georges (Post to
#' \url{https://groups.google.com/d/forum/dartr})
#' @examples
#' out <- gl.report.parent.offspring(testset.gl[1:10, 1:100])
#' @seealso \code{\link[dartR.base]{gl.report.rdepth}} ,\code{\link[dartR.base]{gl.report.reproducibility}},
#'  \code{\link{gl.filter.parent.offspring}}
#' @family report functions
#' @importFrom stats median IQR
#' @import patchwork
#' @export

gl.report.parent.offspring <- function(x,
                                       min.rdepth = 12,
                                       min.reproducibility = 1,
                                       range = 1.5,
                                       plot.filters = FALSE,
                                       plot_theme = theme_dartR(),
                                       plot_colors = gl.colors(2),
                                       plot.dir = NULL,
                                       plot.file = NULL,
                                       verbose = NULL) {
  # SET VERBOSITY
  verbose <- gl.check.verbosity(verbose)

  # SET WORKING DIRECTORY
  plot.dir <- gl.check.wd(plot.dir, verbose = 0)

  # FLAG SCRIPT START
  funname <- match.call()[[1]]
  utils.flag.start(
    func = funname,
    build = "Jody",
    verbose = verbose
  )

  # CHECK DATATYPE
  datatype <- utils.check.datatype(x, verbose = verbose)

  # DO THE JOB

  # Generate null expectation for pedigree inconsistency, and outliers
  if (verbose >= 2) {
    cat(
      report(
        "  Generating null expectation for distribution of counts of
                pedigree incompatibility\n"
      )
    )
  }
  # Assign individuals as populations
  pop(x) <- x$ind.names
  # Filter stringently on reproducibility to minimize miscalls
  if (is.null(x@other$loc.metrics$RepAvg)) {
    if(verbose>0) cat(
      warn(
        "  Dataset does not include RepAvg among the locus metrics,
                therefore the reproducibility filter was not used\n"
      )
    )
  } else {
    x <-
      gl.filter.reproducibility(x,
        threshold = min.reproducibility,
        verbose = 0,
        plot.display = plot.filters
      )
  }
  # Filter stringently on read depth, to further minimize miscalls
  if (is.null(x@other$loc.metrics$rdepth)) {
    if(verbose>0) cat(
      warn(
        "  Dataset does not include rdepth among the locus metrics,
                therefore the read depth filter was not used\n"
      )
    )
  } else {
    x <- gl.filter.rdepth(x, lower = min.rdepth, verbose = 0,
                          plot.display = plot.filters)
  }
  
  pairwise_table <- function(x,
                             pw_fun,
                             dec = 4) {
    ind_names <- indNames(x)
    x <- as.matrix(x)
    ix <- setNames(seq_along(ind_names), ind_names)
    pp <- outer(ix[-1L], ix[-length(ix)],
                function(ivec, jvec) {
                  vapply(seq_along(ivec),
                         function(k) {
                           i <- ivec[k]
                           j <- jvec[k]
                           if (i > j) {
                             pw_fun(x = x[i, ], y = x[j, ])
                           } else{
                             NA_real_
                           }
                         }, numeric(1))
                })
    return(pp)
    
  }
  
  fun <- function(x, y) {
    vect <- (x * 10) + y
    homalts <- sum(vect == 2 | vect == 20, na.rm = T)
  }
  
  count <- pairwise_table(x = x, pw_fun = fun)
  
  # Prepare for plotting
  
  if (verbose >= 2) {
    cat(
      report(
        "  Identifying outliers with lower than expected counts of
                pedigree inconsistencies\n"
      )
    )
  }
  title <-
    paste0("SNP data (DArTSeq)\nCounts of pedigree incompatible loci per
               pair")
  
  counts_plot <- as.vector(unlist(unname(count)))
  counts_plot <- counts_plot[!is.na(counts_plot)]
  counts_plot <- data.frame(count = counts_plot)
  
  # Boxplot
  p1 <-
    ggplot(counts_plot, aes(y = count)) +
    geom_boxplot(color = plot_colors[1], fill = plot_colors[2]) +
    coord_flip() +
    plot_theme +
    xlim(range = c(-1, 1)) +
    ylim(min(count), max(count)) +
    ylab(" ") +
    theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) +
    ggtitle(title)
  
  outliers_temp <- ggplot_build(p1)$data[[1]]$outliers[[1]]
  
  lower.extremes <-
    outliers_temp[outliers_temp < stats::median(count,
                                                na.rm = T)]
  if (length(lower.extremes) == 0) {
    outliers <- NULL
  } else {
    outliers <- data.frame(Outlier = lower.extremes)
  }
  
  outliers <- unique(outliers)
  
  # Ascertain the identity of the pairs
  if (verbose >= 2) {
    cat(report("  Identifying outlying pairs\n"))
  }
  if (length(lower.extremes) > 0) {
    tmp <- count
    # tmp[lower.tri(tmp)] <- t(tmp)[lower.tri(tmp)]
    outliers_df <- NULL
    for (i in 1:length(outliers$Outlier)) {
      # Identify
      tmp2 <- which(tmp == outliers$Outlier[i],
                    arr.ind = T)
      ind1 <- rownames(count)[tmp2[, "row"]]
      ind2 <- colnames(count)[tmp2[, "col"]]
      # Z-scores
      zscore <-
        (mean(count, na.rm = TRUE) - outliers$Outlier[i]) /
        sd(count, na.rm = TRUE)
      zscore <- zscore * -1
      outliers_p <-
        round(pnorm(
          mean = mean(count, na.rm = TRUE),
          sd = sd(count, na.rm = TRUE),
          q = zscore
        ), 8)
      outliers_df_tmp <- data.frame(
        Outlier = rep(outliers$Outlier[i], length(ind1)),
        ind1 = ind1,
        ind2 = ind2,
        zscore = zscore,
        p = outliers_p
      )
      outliers_df <- rbind(outliers_df, outliers_df_tmp)
    }
    # ordering by number of outliers
    outliers_df <- outliers_df[order(outliers_df$Outlier,
                                     decreasing = T),]
  }
  
  # Extract the quantile threshold
  iqr <- stats::IQR(count, na.rm = TRUE)
  qth <- quantile(count, 0.25, na.rm = TRUE)
  cutoff <- qth - iqr * range
  
  # Histogram
  p2 <-
    ggplot(counts_plot, aes(x = count)) +
    geom_histogram(bins = 50,
                   color = plot_colors[1],
                   fill = plot_colors[2]) +
    geom_vline(xintercept = cutoff,
               color = "red",
               size = 1) +
    coord_cartesian(xlim = c(min(count), max(count))) +
    xlab("No. Pedigree incompatible") +
    ylab("Count") +
    plot_theme

  # Output the outlier loci
  if (length(lower.extremes) == 0) {
    df <- NULL
    if(verbose>0) cat(important("  No outliers detected\n"))
  }else{
    outliers_df <- outliers_df[order(outliers_df$Outlier), ]
    df <- outliers_df
    df <- df[which(df$Outlier<=cutoff),]
    if (verbose >= 3) {
      print(outliers_df)
    }
  }

  # PRINTING OUTPUTS
    # using package patchwork
    p3 <- (p1 / p2) + plot_layout(heights = c(1, 4))
    print(p3)

    # Optionally save the plot ---------------------
    if (!is.null(plot.file)) {
  tmp <- utils.plot.save(p3,
    dir = plot.dir,
    file = plot.file,
    verbose = verbose
  )
  }

  # FLAG SCRIPT END

  if (verbose >= 1) {
    cat(report("Completed:", funname, "\n"))
  }

  # RETURN
  return(df)
}