R/gl.report.reproducibility.r

In dartR: Importing and Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis

#' @name gl.report.reproducibility
#' @title Reports summary of RepAvg (repeatability averaged over both alleles 
#' for
#' each locus) or reproducibility (repeatability of the scores for fragment
#' presence/absence)
#' @description
#' SNP datasets generated by DArT have an index, RepAvg, generated by
#' reproducing the data independently for 30% of loci. RepAvg is the proportion
#' of alleles that give a repeatable result, averaged over both alleles for each
#' locus.
#'
#' In the case of fragment presence/absence data (SilicoDArT), repeatability is
#' the percentage of scores that are repeated in the technical replicate
#'  dataset.
#'
#' @param x Name of the genlight object containing the SNP or presence/absence
#'  (SilicoDArT) data [required].
#' @param plot.out If TRUE, displays a plot to guide the decision on a filter
#' threshold [default TRUE].
#' @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 two_colors].
#' @param save2tmp If TRUE, saves any ggplots and listings to the session
#' temporary directory (tempdir) [default FALSE].
#' @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
#'  The function displays a table of minimum, maximum, mean and quantiles for
#'  repeatbility against possible thresholds that might subsequently be
#'  specified in \code{\link{gl.filter.reproducibility}}.
#'
#'  If plot.out=TRUE, display also includes a boxplot and a histogram to guide
#'  in the selection of a threshold for filtering on repeatability.
#'
#'  If save2tmp=TRUE, ggplots and relevant tabulations are saved to the
#'  session's temp directory (tempdir)
#'
#'  For examples of themes, see:
#'    \itemize{
#'  \item \url{https://ggplot2.tidyverse.org/reference/ggtheme.html} and 
#'  \item
#'   \url{https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/}
#'  }
#'
#' @return An unaltered genlight object
#' @author Custodian: Arthur Georges -- Post to
#' \url{https://groups.google.com/d/forum/dartr}
#'
#' @examples
#'  \donttest{
#' # SNP data
#'   out <- gl.report.reproducibility(testset.gl)
#'   }
#' # Tag P/A data
#'   out <- gl.report.reproducibility(testset.gs)
#'
#' @seealso \code{\link{gl.filter.reproducibility}}
#' @family report functions
#' @import patchwork
#' @export

gl.report.reproducibility <- function(x,
                                      plot.out = TRUE,
                                      plot_theme = theme_dartR(),
                                      plot_colors = two_colors,
                                      save2tmp = FALSE,
                                      verbose = NULL) {
    # SET VERBOSITY
    verbose <- gl.check.verbosity(verbose)
    
    # FLAG SCRIPT START
    funname <- match.call()[[1]]
    utils.flag.start(func = funname,
                     build = "Jody",
                     verbosity = verbose)
    
    # CHECK DATATYPE
    datatype <- utils.check.datatype(x, verbose = verbose)
    
    # DO THE JOB
    
    ########### FOR METHOD BASED ON LOCUS
    
    # get title for plots
    if (datatype == "SNP") {
        title <- paste0("SNP data (DArTSeq)\nRepeatability by Locus")
    } else {
        title <-
            paste0("Fragment P/A data (SilicoDArT)\nRepeatability by Locus")
    }
    
    if (datatype == "SNP") {
        repeatability <- x@other$loc.metrics$RepAvg
    } else {
        repeatability <- x@other$loc.metrics$Reproducibility
    }
    
    repeatability_plot <- data.frame(repeatability)
    colnames(repeatability_plot) <- "repeatability"
    
    # Boxplot
    if (plot.out) {
        p1 <-
            ggplot(repeatability_plot, aes(y = repeatability)) +
            geom_boxplot(color = plot_colors[1], fill = plot_colors[2]) + 
            coord_flip() +
            plot_theme + 
            ylim(c(min(repeatability), 1)) + 
            ylab(" ") + 
            theme(axis.text.y = element_blank(),axis.ticks.y=element_blank()) +
            ggtitle(title)
        
        # Histogram
        p2 <-
            ggplot(repeatability_plot, aes(x = repeatability)) +
            geom_histogram(bins = 50,color=plot_colors[1],fill=plot_colors[2]) +
            coord_cartesian(xlim = c(min(repeatability), 1)) + 
            xlab("Repeatability") + 
            ylab("Count") + 
            plot_theme
    }
    
    # Print out some statistics
    stats <- summary(repeatability)
    cat(report("  Reporting Repeatability by Locus\n"))
    cat("  No. of loci =", nLoc(x), "\n")
    cat("  No. of individuals =", nInd(x), "\n")
    cat("    Minimum      : ", stats[1], "\n")
    cat("    1st quartile : ", stats[2], "\n")
    cat("    Median       : ", stats[3], "\n")
    cat("    Mean         : ", stats[4], "\n")
    cat("    3r quartile  : ", stats[5], "\n")
    cat("    Maximum      : ", stats[6], "\n")
    cat("    Missing Rate Overall: ", round(sum(is.na(as.matrix(
        x
    ))) / (nLoc(x) * nInd(x)), 2), "\n\n")
    
    # Determine the loss of loci for a given threshold using quantiles
    quantile_res <- quantile(repeatability, probs = seq(0, 1, 1 / 20),type=1)
    retained <- unlist(lapply(quantile_res, function(y) {
        res <- length(repeatability[repeatability >= y])
    }))
    pc.retained <- round(retained * 100 / nLoc(x), 1)
    filtered <- nLoc(x) - retained
    pc.filtered <- 100 - pc.retained
    df <-
        data.frame(as.numeric(sub("%", "", names(quantile_res))),
                   quantile_res,
                   retained,
                   pc.retained,
                   filtered,
                   pc.filtered)
    colnames(df) <-
        c("Quantile",
          "Threshold",
          "Retained",
          "Percent",
          "Filtered",
          "Percent")
    df <- df[order(-df$Quantile), ]
    df$Quantile <- paste0(df$Quantile, "%")
    rownames(df) <- NULL
    
    # PRINTING OUTPUTS
    if (plot.out) {
        # using package patchwork
        p3 <- (p1 / p2) + plot_layout(heights = c(1, 4))
        print(p3)
    }
    print(df)
    
    # SAVE INTERMEDIATES TO TEMPDIR
    
    # creating temp file names
    if (save2tmp) {
        if (plot.out) {
            temp_plot <- tempfile(pattern = "Plot_")
            match_call <-
                paste0(names(match.call()),
                       "_",
                       as.character(match.call()),
                       collapse = "_")
            # saving to tempdir
            saveRDS(list(match_call, p3), file = temp_plot)
            if (verbose >= 2) {
                cat(report("  Saving the ggplot to session tempfile\n"))
            }
        }
        temp_table <- tempfile(pattern = "Table_")
        saveRDS(list(match_call, df), file = temp_table)
        if (verbose >= 2) {
            cat(report("  Saving tabulation to session tempfile\n"))
            cat(
                report(
                    "  NOTE: Retrieve output files from tempdir using 
                    gl.list.reports() and gl.print.reports()\n"
                )
            )
        }
    }
    
    # FLAG SCRIPT END
    
    if (verbose >= 1) {
        cat(report("Completed:", funname, "\n"))
    }
    
    # RETURN
    invisible(x)
    
}