R/gl.fixed.diff.r

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

#' @name gl.fixed.diff
#' @title Generates a matrix of fixed differences and associated statistics for
#'  populations taken pairwise
#' @description
#' This script takes SNP data or sequence tag P/A data grouped into populations
#' in a genlight object (DArTSeq) and generates a matrix of fixed differences
#' between populations taken pairwise
#' @details
#' A fixed difference at a locus occurs when two populations share no alleles or
#' where all members of one population has a sequence tag scored, and all
#' members of the other population has the sequence tag absent. The challenge
#' with this approach is that when sample sizes are finite, fixed differences
#' will occur through sampling error, compounded when many loci are examined.
#' Simulations suggest that sample sizes of n1=5 and n2=5 are adequate to reduce
#' the probability of [experiment-wide] type 1 error to negligible levels
#' [ploidy=2]. A warning is issued if comparison between two populations
#' involves sample sizes less than 5, taking into account allele drop-out.
#'
#' Optionally, if test=TRUE, the script will test the fixed differences between
#' final OTUs for statistical significance, using simulation, and then further
#' amalgamate populations that for which there are no significant fixed
#' differences at a specified level of significance (alpha). To avoid conflation
#' of true fixed differences with false positives in the simulations, it is
#' necessary to decide a threshold value (delta) for extreme true allele
#' frequencies that will be considered fixed for practical purposes. That is,
#' fixed differences in the sample set will be considered to be positives (not
#' false positives) if they arise from true allele frequencies of less than
#' 1-delta in one or both populations.  The parameter delta is typically set to
#' be small (e.g. delta = 0.02).
#'
#' NOTE: The above test will only be calculated if tloc=0, that is, for analyses
#' of absolute fixed differences. The test applies in comparisons of allopatric
#'  populations only. For sympatric populations, use gl.pval.sympatry().
#'
#' An absolute fixed difference is as defined above. However, one might wish to
#' score fixed differences at some lower level of allele frequency difference,
#' say where percent allele frequencies are 95,5 and 5,95 rather than 100:0 and
#' 0:100. This adjustment can be done with the tloc parameter. For example,
#' tloc=0.05 means that SNP allele frequencies of 95,5 and 5,95 percent will be
#' regarded as fixed when comparing two populations at a locus.
#'
#' @param x Name of the genlight object containing SNP genotypes or tag P/A data
#' (SilicoDArT) or an object of class 'fd' [required].
#' @param tloc Threshold defining a fixed difference (e.g. 0.05 implies 95:5 vs
#'  5:95 is fixed) [default 0].
#' @param test If TRUE, calculate p values for the observed fixed differences
#' [default FALSE].
#' @param reps Number of replications to undertake in the simulation to estimate
#' probability of false positives [default 1000].
#' @param delta Threshold value for the true population minor allele frequency
#' (MAF) from which resultant sample fixed differences are considered true
#' positives [default 0.02].
#' @param alpha Level of significance used to display non-significant
#' differences between populations as they are compared pairwise [default 0.05].
#' @param mono.rm If TRUE, loci that are monomorphic across all individuals are
#' removed before beginning computations [default TRUE].
#' @param pb If TRUE, show a progress bar on time consuming loops
#' [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 or as specified using gl.set.verbosity].
#' @return A list of Class 'fd' containing the gl object and square matrices,
#' as follows:
#' \enumerate{
#'        \item $gl -- the output genlight object;
#'        \item $fd -- raw fixed differences;
#'        \item $pcfd -- percent fixed differences;
#'        \item $nobs -- mean no. of individuals used in each comparison;
#'        \item $nloc -- total number of loci used in each comparison;
#'        \item $expfpos -- if test=TRUE, the expected count of false positives
#'        for each comparison [by simulation];
#'        \item $sdfpos -- if test=TRUE, the standard deviation of the count of
#'        false positives for each comparison [by simulation];
#'        \item $prob -- if test=TRUE, the significance of the count of fixed
#'        differences [by simulation])
#'         }
#' @import utils
#' @export
#' @author Custodian: Arthur Georges -- Post to
#' \url{https://groups.google.com/d/forum/dartr}
#' @examples
#' \donttest{
#' fd <- gl.fixed.diff(testset.gl, tloc=0, verbose=3 )
#' fd <- gl.fixed.diff(testset.gl, tloc=0, test=TRUE, delta=0.02, reps=100, verbose=3 )
#' }
#' @seealso \code{\link{is.fixed}}

gl.fixed.diff <- function(x,
                          tloc = 0,
                          test = FALSE,
                          delta = 0.02,
                          alpha = 0.05,
                          reps = 1000,
                          mono.rm = TRUE,
                          pb = 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,
                             accept = c("genlight", "SNP", "SilicoDArT", "fd"),
                             verbose = verbose)
    if (datatype == "fd") {
        x <- x$gl
    }
    
    # FUNCTION SPECIFIC ERROR CHECKING
    
    if (tloc > 0.5 || tloc < 0) {
        stop(error(
            "Fatal Error: Parameter tloc should be positive in the range 0 to 0.5\n"
        ))
    }
    if (!(tloc == 0) & test == TRUE) {
        cat(
            warn(
                "  Warning: false positives can only be simulated for tloc=0, setting tloc to zero\n"
            )
        )
        tloc <- 0
    }
    
    if (verbose >= 2) {
        if (tloc > 0) {
            cat(
                report(
                    "  Comparing populations for fixed differences with tolerance",
                    tloc,
                    "\n"
                )
            )
        }
        if (tloc == 0) {
            cat(report(
                "  Comparing populations for absolute fixed differences\n"
            ))
        }
    }
    
    # Checking count of populations
    if (nPop(x) < 2) {
        stop(
            error(
                "Fatal Error: Distance calculation requires at least two populations, one or none present\n"
            )
        )
    }
    
    # Checking for and removing monomorphic loci
    if (!x@other$loc.metrics.flags$monomorphs) {
        if (verbose >= 2) {
            cat(warn(
                "  Warning: Monomorphic loci retained, used in calculations\n"
            ))
        }
    } else {
        if (verbose >= 2) {
            cat(report("  Monomorphic loci removed\n"))
        }
        x <- gl.filter.monomorphs(x, verbose = 0)
    }
    # define dist2list function
    
    dist2list <- function(dist) {
        if (!is(dist,"dist")) {
            stop(error("the input data must be a dist object."))
        }
        dat <- as.data.frame(as.matrix(dist))
        if (is.null(names(dat))) {
            rownames(dat) <- paste(1:nrow(dat))
        }
        value <- stack(dat)$values
        rnames <- rownames(dat)
        namecol <- expand.grid(rnames, rnames)
        colnames(namecol) <- c("col", "row")
        res <- data.frame(namecol, value)
        return(res)
    }
    
    # DO THE JOB
    
    # Calculate percent allele frequencies
    ftable <- gl.percent.freq(x, verbose = 0)
    
    # GENERATE A MATRIX OF PAIRWISE FIXED DIFFERENCES
    
    # Report samples sizes for each population
    if (verbose >= 3) {
        cat("  Populations, aggregations and sample sizes")
        print(table(pop(x)))
    }
    if (min(table(pop(x))) < 10 && verbose >= 3) {
        cat(
            warn(
                "  Warning: Fixed differences can arise through sampling error if sample sizes are small\n"
            )
        )
        cat(warn(
            "    Some sample sizes are small (N < 10, minimum in dataset =",
            min(table(pop(x))),
            ")\n"
        ))
        if (!test) {
            cat(
                warn(
                    "    Recommend manually amalgamating populations or setting test=TRUE to allow evaluation of statistical significance\n"
                )
            )
        }
    }
    
    # Establish an array to hold the fixed differences and sample sizes
    npops <- nlevels(ftable$popn)
    nloci <- nlevels(as.factor(ftable$locus))
    fixed.matrix <- array(-1, c(npops, npops))
    exp.matrix <- array(NA, c(npops, npops))
    sd.matrix <- array(NA, c(npops, npops))
    pcfixed.matrix <- array(-1, c(npops, npops))
    ind.count.matrix <- array(-1, c(npops, npops))
    loc.count.matrix <- array(-1, c(npops, npops))
    p.false.pos.matrix <- array(NA, c(npops, npops))
    
    # Set up the progress counter
    if (verbose >= 2 & pb) {
        progress <-
            txtProgressBar(
                min = 0,
                max = 1,
                style = 3,
                initial = 0,
                label = "Working ...."
            )
        getTxtProgressBar(progress)
        cat("\n\n")
    }
    
    # Cycle through the data to sum the fixed differences into a square matrix
    if (verbose >= 2) {
        cat(
            report(
                "  Comparing populations pairwise -- this may take time. Please be patient\n"
            )
        )
    }
    for (popi in 1:(npops - 1)) {
        # For each population For each other population Pull out the data for each population
        for (popj in (popi + 1):npops) {
            p1 <- ftable[ftable$popn == levels(pop(x))[popi],]
            p2 <- ftable[ftable$popn == levels(pop(x))[popj],]
            # Calculate fixed differences
            fixed <- c(rep(0, nloci))
            for (i in 1:nloci) {
                # For each locus
                fixed[i] <-
                    is.fixed(p1$frequency[i], p2$frequency[i], tloc = tloc)
            }
            # Calculate stats across loci
            fixed.matrix[popi, popj] <- sum(fixed, na.rm = TRUE)
            loc.count.matrix[popi, popj] <- sum(!is.na(fixed))
            pcfixed.matrix[popi, popj] <-
                round(fixed.matrix[popi, popj] * 100 / sum(!is.na(fixed)), 0)
            ind.count.matrix[popi, popj] <-
                round(mean(p1$nobs[p1$nobs > 0]) + mean(p2$nobs[p2$nobs > 0]), 1)
            # Make full matrix and add row and column names
            fixed.matrix[popj, popi] <- fixed.matrix[popi, popj]
            fixed.matrix[popi, popi] <- 0
            fixed.matrix[npops, npops] <- 0
            rownames(fixed.matrix) <- levels(pop(x))
            colnames(fixed.matrix) <- levels(pop(x))
            
            loc.count.matrix[popj, popi] <-
                loc.count.matrix[popi, popj]
            loc.count.matrix[popi, popi] <- NA
            loc.count.matrix[npops, npops] <- NA
            rownames(loc.count.matrix) <- levels(pop(x))
            colnames(loc.count.matrix) <- levels(pop(x))
            
            pcfixed.matrix[popj, popi] <-
                pcfixed.matrix[popi, popj]
            pcfixed.matrix[popi, popi] <- 0
            pcfixed.matrix[npops, npops] <- 0
            rownames(pcfixed.matrix) <- levels(pop(x))
            colnames(pcfixed.matrix) <- levels(pop(x))
            
            ind.count.matrix[popj, popi] <-
                ind.count.matrix[popi, popj]
            ind.count.matrix[popi, popi] <- NA
            ind.count.matrix[npops, npops] <- NA
            rownames(ind.count.matrix) <- levels(pop(x))
            colnames(ind.count.matrix) <- levels(pop(x))
            
            # Calculate the probability that the observed differences are false positives
            if (test) {
                if (tloc != 0) {
                    cat(report("  Setting tloc to zero\n"))
                    tloc.hold <- tloc
                    tloc <- 0
                }
                outlist <-
                    gl.fdsim(
                        x,
                        c(levels(pop(x))[popi], levels(pop(x))[popj]),
                        obs = fixed.matrix[popi, popj],
                        delta = delta,
                        reps = reps,
                        verbose = 0
                    )
                p.false.pos.matrix[popi, popj] <-
                    round(outlist$prob, 4)
                exp.matrix[popi, popj] <-
                    round(outlist$mnexpected, 1)
                sd.matrix[popi, popj] <-
                    round(outlist$sdexpected, 4)
                if (verbose >= 3) {
                    if (p.false.pos.matrix[popi, popj] > alpha) {
                        cat(
                            "    ",
                            popNames(x)[popi],
                            "vs",
                            popNames(x)[popj],
                            " [p =",
                            p.false.pos.matrix[popi, popj],
                            ",ns]\n"
                        )
                    }
                }
                # Make full matrix and add row and column names
                exp.matrix[popj, popi] <- exp.matrix[popi, popj]
                exp.matrix[popi, popi] <- 0
                exp.matrix[npops, npops] <- 0
                rownames(exp.matrix) <- levels(pop(x))
                colnames(exp.matrix) <- levels(pop(x))
                
                sd.matrix[popj, popi] <- sd.matrix[popi, popj]
                sd.matrix[popi, popi] <- 0
                sd.matrix[npops, npops] <- 0
                rownames(sd.matrix) <- levels(pop(x))
                colnames(sd.matrix) <- levels(pop(x))
                
                p.false.pos.matrix[popj, popi] <-
                    p.false.pos.matrix[popi, popj]
                p.false.pos.matrix[popi, popi] <- 0
                p.false.pos.matrix[npops, npops] <- 0
                rownames(p.false.pos.matrix) <- levels(pop(x))
                colnames(p.false.pos.matrix) <- levels(pop(x))
            }
        }
        if (verbose >= 2 & pb) {
            setTxtProgressBar(progress, popi / (npops - 1))
        }
    }
    
    # Return the extreme low distances -- candidates for lack of significance
    if (verbose >= 5) {
        cat(
            report(
                "\nDisplaying a list of 5% of pairs with smallest non-zero differences\n"
            )
        )
        
        df <- dist2list(as.dist(fixed.matrix))
        
        df <- df[!df$col == df$row,]
        # df <- df[!df$value<=tpop,]
        pctile <- quantile(df$value[df$value > 0], probs = 0.05)
        df <- df[df$value <= pctile,]
        df <- df[order(df$value),]
        print(df)
        cat("\n")
    }
    
    # Return the matricies
    if (verbose >= 4) {
        if (pb) {
            cat("\n")
        }
        cat(
            report(
                "Returning a list containing the gl object and square matricies, as follows:\n",
                "         [[1]] $gl -- input genlight object;\n",
                "         [[2]] $fd -- raw fixed differences;\n",
                "         [[3]] $pcfd -- percent fixed differences;\n",
                "         [[4]] $nobs -- mean no. of individuals used in each comparison;\n",
                "         [[5]] $nloc -- total number of loci used in each comparison;\n",
                "         [[6]] $expfpos -- if test=TRUE, the expected count of false positives for each comparison [by simulation]\n",
                "         [[7]] $sdfpos -- if test=TRUE, the expected count of false positives for each comparison [by simulation]\n",
                "         [[8]] $prob -- if test=TRUE, the significance of the count of fixed differences [by simulation]\n"
            )
        )
    }
    
    fixed.matrix <- as.dist(fixed.matrix)
    pcfixed.matrix <- as.dist(pcfixed.matrix)
    l <-
        list(
            gl = x,
            fd = fixed.matrix,
            pcfd = pcfixed.matrix,
            nobs = ind.count.matrix,
            nloc = loc.count.matrix,
            expfpos = exp.matrix,
            sdfpos = sd.matrix,
            pval = p.false.pos.matrix
        )
    class(l) <- "fd"
    
    # FLAG SCRIPT END
    
    if (verbose > 0) {
        cat(report("Completed:", funname, "\n"))
    }
    
    return(l)
}