R/amova.r

Defines functions poppr.amova

Documented in poppr.amova

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#
# This software was authored by Zhian N. Kamvar and Javier F. Tabima, graduate 
# students at Oregon State University; Jonah C. Brooks, undergraduate student at
# Oregon State University; and Dr. Nik Grünwald, an employee of USDA-ARS.
#
# Permission to use, copy, modify, and distribute this software and its
# documentation for educational, research and non-profit purposes, without fee, 
# and without a written agreement is hereby granted, provided that the statement
# above is incorporated into the material, giving appropriate attribution to the
# authors.
#
# Permission to incorporate this software into commercial products may be
# obtained by contacting USDA ARS and OREGON STATE UNIVERSITY Office for 
# Commercialization and Corporate Development.
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# The software program and documentation are supplied "as is", without any
# accompanying services from the USDA or the University. USDA ARS or the 
# University do not warrant that the operation of the program will be 
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# developed for research purposes and is advised not to rely exclusively on the 
# program for any reason.
#
# IN NO EVENT SHALL USDA ARS OR OREGON STATE UNIVERSITY BE LIABLE TO ANY PARTY 
# FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING
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# WARRANTY OF NON-INFRINGEMENT. THE SOFTWARE PROVIDED HEREUNDER IS ON AN "AS IS"
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#==============================================================================#
#' Perform Analysis of Molecular Variance (AMOVA) on genind or genclone objects.
#' 
#' This function simplifies the process necessary for performing AMOVA in R. It
#' gives user the choice of utilizing either the \pkg{ade4} or the \pkg{pegas}
#' implementation of AMOVA. See [ade4::amova()] (ade4) and [pegas::amova()]
#' (pegas) for details on the specific implementation.
#' 
#' @param x a [genind][genind-class], [genclone][genclone-class], [genlight][genlight-class], or [snpclone][snpclone-class] object
#'
#' @param hier a hierarchical [formula][formula()] that defines your population
#'   hierarchy. (e.g.: `~Population/Subpopulation`). **See Details below**.
#'
#' @param clonecorrect `logical` if `TRUE`, the data set will be clone corrected
#'   with respect to the lowest level of the hierarchy. The default is set to
#'   `FALSE`. See [clonecorrect()] for details.
#'
#' @param within `logical`. When this is set to `TRUE` (Default), variance
#'   within individuals are calculated as well. If this is set to `FALSE`, The
#'   lowest level of the hierarchy will be the sample level. See Details below.
#'
#' @param freq `logical`. If `within = FALSE`, the parameter rho is calculated
#'   (Ronfort et al. 1998; Meirmans and Liu 2018). By setting `freq = TRUE`,
#'   (default) allele counts will be converted to frequencies before the
#'   distance is calculated, otherwise, the distance will be calculated on
#'   allele counts, which can bias results in mixed-ploidy data sets. Note that
#'   this option has no effect for haploid or presence/absence data sets.
#'
#' @param dist an optional distance matrix calculated on your data. If this is
#'   set to `NULL` (default), the raw pairwise distances will be calculated via
#'   [dist()].
#'
#' @param squared if a distance matrix is supplied, this indicates whether or
#'   not it represents squared distances.
#'
#' @param correction a `character` defining the correction method for
#'   non-euclidean distances. Options are [ade4::quasieuclid()] (Default),
#'   [ade4::lingoes()], and [ade4::cailliez()]. See Details below.
#'
#' @param sep Deprecated. As of poppr version 2, this argument serves no
#'   purpose.
#'
#' @param filter `logical` When set to `TRUE`, mlg.filter will be run to
#'   determine genotypes from the distance matrix. It defaults to `FALSE`. You
#'   can set the parameters with `algorithm` and `threshold` arguments. Note
#'   that this will not be performed when `within = TRUE`. Note that the
#'   threshold should be the number of allowable substitutions if you don't
#'   supply a distance matrix.
#'
#' @param threads `integer` When using filtering or genlight objects, this 
#'   parameter specifies the number of parallel processes passed to 
#'   [mlg.filter()] and/or [bitwise.dist()]. 
#'
#' @param missing specify method of correcting for missing data utilizing
#'   options given in the function [missingno()]. Default is `"loci"`. This only
#'   applies to genind or genclone objects. 
#'
#' @param cutoff specify the level at which missing data should be
#'   removed/modified. See [missingno()] for details. This only applies to
#'   genind or genclone objects.
#'
#' @param quiet `logical` If `FALSE` (Default), messages regarding any
#'   corrections will be printed to the screen. If `TRUE`, no messages will be
#'   printed.
#'
#' @param method Which method for calculating AMOVA should be used? Choices
#'   refer to package implementations: "ade4" (default) or "pegas". See details
#'   for differences.
#'
#' @param nperm the number of permutations passed to the pegas implementation of
#'   amova.
#'   
#' @inheritParams mlg.filter
#'   
#' @return a list of class `amova` from the ade4 or pegas package. See 
#'   [ade4::amova()] or [pegas::amova()] for details.
#' 
#' @details The poppr implementation of AMOVA is a very detailed wrapper for the
#'   ade4 implementation. The output is an [ade4::amova()] class list that
#'   contains the results in the first four elements. The inputs are contained
#'   in the last three elements. The inputs required for the ade4 implementation
#'   are:
#'   
#'   1. a distance matrix on all unique genotypes (haplotypes)
#'   2. a data frame defining the hierarchy of the distance matrix 
#'   3. a genotype (haplotype) frequency table.
#'
#'   All of this data can be constructed from a [genind][genind-class] or
#'   [genlight][genlight-class] object, but can be daunting for a novice R user.
#'   *This function automates the entire process*. Since there are many
#'   variables regarding genetic data, some points need to be highlighted:
#'   
#'   \subsection{On Hierarchies:}{The hierarchy is defined by different
#'   population strata that separate your data hierarchically. These strata are
#'   defined in the \strong{strata} slot of [genind][genind-class],
#'   [genlight][genlight-class], [genclone][genclone-class], and
#'   [snpclone][snpclone-class] objects. They are useful for defining the
#'   population factor for your data. See the function [strata()] for details on
#'   how to properly define these strata.}
#'
#'   \subsection{On Within Individual Variance:}{ Heterozygosities within
#'   genotypes are sources of variation from within individuals and can be
#'   quantified in AMOVA. When `within = TRUE`, poppr will split genotypes into
#'   haplotypes with the function [make_haplotypes()] and use those to calculate
#'   within-individual variance. No estimation of phase is made. This acts much
#'   like the default settings for AMOVA in the Arlequin software package.
#'   Within individual variance will not be calculated for haploid individuals
#'   or dominant markers as the haplotypes cannot be split further. Setting
#'   `within = FALSE` uses the euclidean distance of the allele frequencies
#'   within each individual. **Note:** `within = TRUE` is incompatible with
#'   `filter = TRUE`. In this case, `within` will be set to `FALSE`}
#'
#'   \subsection{On Euclidean Distances:}{ With the \pkg{ade4} implementation of
#'   AMOVA (utilized by \pkg{poppr}), distances must be Euclidean (due to the
#'   nature of the calculations). Unfortunately, many genetic distance measures
#'   are not always euclidean and must be corrected for before being analyzed.
#'   Poppr automates this with three methods implemented in \pkg{ade4},
#'   [quasieuclid()], [lingoes()], and [cailliez()]. The correction of these
#'   distances should not adversely affect the outcome of the analysis.}
#'   
#'   \subsection{On Filtering:}{ Filtering multilocus genotypes is performed by
#'   [mlg.filter()]. This can necessarily only be done AMOVA tests that do not
#'   account for within-individual variance. The distance matrix used to
#'   calculate the amova is derived from using [mlg.filter()] with the option
#'   `stats = "distance"`, which reports the distance between multilocus
#'   genotype clusters. One useful way to utilize this feature is to correct for
#'   genotypes that have equivalent distance due to missing data. (See example
#'   below.)}
#'   
#'   \subsection{On Methods:}{ Both \pkg{ade4} and \pkg{pegas} have
#'   implementations of AMOVA, both of which are appropriately called "amova".
#'   The ade4 version is faster, but there have been questions raised as to the
#'   validity of the code utilized. The pegas version is slower, but careful
#'   measures have been implemented as to the accuracy of the method. It must be
#'   noted that there appears to be a bug regarding permuting analyses where
#'   within individual variance is accounted for (`within = TRUE`) in the pegas
#'   implementation. If you want to perform permutation analyses on the pegas
#'   implementation, you must set `within = FALSE`. In addition, while clone
#'   correction is implemented for both methods, filtering is only implemented
#'   for the ade4 version.}
#'   
#'   \subsection{On Polyploids:}{ As of \pkg{poppr} version 2.7.0, this
#'   function is able to calculate phi statistics for within-individual variance
#'   for polyploid data with **full dosage information**. When a data set does
#'   not contain full dosage information for all samples, then the resulting 
#'   pseudo-haplotypes will contain missing data, which would result in an 
#'   incorrect estimate of variance. 
#'   
#'   Instead, the AMOVA will be performed on the distance matrix derived from
#'   allele counts or allele frequencies, depending on the `freq` option. This
#'   has been shown to be robust to estimates with mixed ploidy (Ronfort et al.
#'   1998; Meirmans and Liu 2018). If you wish to brute-force your way to 
#'   estimating AMOVA using missing values, you can split your haplotypes with
#'   the [make_haplotypes()] function.
#'   
#'   One strategy for addressing ambiguous dosage in your polyploid data set 
#'   would be to convert your data to \pkg{polysat}'s `genambig` class with the
#'   [as.genambig()], estimate allele frequencies with [polysat::deSilvaFreq()],
#'   and use these frequencies to randomly sample alleles to fill in the 
#'   ambiguous alleles. 
#'   }
#'   
#' @keywords amova
#' @aliases amova
#' 
#' @references 
#' Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of
#' molecular variance inferred from metric distances among DNA haplotypes:
#' application to human mitochondrial DNA restriction data. *Genetics*,
#' **131**, 479-491.
#' 
#' Ronfort, J., Jenczewski, E., Bataillon, T., and Rousset, F. (1998). Analysis
#' of population structure in autotetraploid species. *Genetics*, **150**,
#' 921–930.
#'
#' Meirmans, P., Liu, S. (2018) Analysis of Molecular Variance (AMOVA) for
#' Autopolyploids *Submitted*.
#' 
#' @seealso [ade4::amova()], [pegas::amova()], [clonecorrect()], [diss.dist()],
#'  [missingno()], [ade4::is.euclid()], [strata()], [make_haplotypes()], 
#'  [as.genambig()]
#' @export
#' @md
#' @examples
#' data(Aeut)
#' strata(Aeut) <- other(Aeut)$population_hierarchy[-1]
#' agc <- as.genclone(Aeut)
#' agc
#' amova.result <- poppr.amova(agc, ~Pop/Subpop)
#' amova.result
#' amova.test <- randtest(amova.result) # Test for significance
#' plot(amova.test)
#' amova.test
#' 
#' \dontrun{
#' 
#' # You can get the same results with the pegas implementation
#' amova.pegas <- poppr.amova(agc, ~Pop/Subpop, method = "pegas")
#' amova.pegas
#' amova.pegas$varcomp/sum(amova.pegas$varcomp)
#' 
#' # Clone correction is possible
#' amova.cc.result <- poppr.amova(agc, ~Pop/Subpop, clonecorrect = TRUE)
#' amova.cc.result
#' amova.cc.test <- randtest(amova.cc.result)
#' plot(amova.cc.test)
#' amova.cc.test
#' 
#' 
#' # Example with filtering
#' data(monpop)
#' splitStrata(monpop) <- ~Tree/Year/Symptom
#' poppr.amova(monpop, ~Symptom/Year) # gets a warning of zero distances
#' poppr.amova(monpop, ~Symptom/Year, filter = TRUE, threshold = 0.1) # no warning
#' 
#' 
#' }
#==============================================================================#
#' @importFrom ade4 amova is.euclid cailliez quasieuclid lingoes
poppr.amova <- function(x, hier = NULL, clonecorrect = FALSE, within = TRUE, 
                        dist = NULL, squared = TRUE, freq = TRUE, 
                        correction = "quasieuclid", sep = "_", filter = FALSE, 
                        threshold = 0, algorithm = "farthest_neighbor", 
                        threads = 1L, missing = "loci", cutoff = 0.05, 
                        quiet = FALSE,  method = c("ade4", "pegas"), nperm = 0){
  if (!inherits(x, c("genind", "genlight"))) stop(paste(substitute(x), "must be a genind object."))
  if (is.null(hier)) stop("A population hierarchy must be specified")
  methods   <- c("ade4", "pegas")
  method    <- match.arg(method, methods)
  setPop(x) <- hier
  is_genind <- is.genind(x)
  # Sanity Checks
  haploid      <- all(ploidy(x) == 1)
  within       <- if (haploid) FALSE else within
  heterozygous <- if (is_genind) check_Hs(x) else TRUE
  codominant   <- if (is_genind) x@type != "PA" else TRUE
  freq         <- freq & codominant & !haploid # freq does not need to be in place for haploid data
  if (!is.clone(x)) {
    x <- if (is_genind) as.genclone(x) else as.snpclone(x)
  }
  if (filter && within && !haploid) {
    msg <- paste("`filter = TRUE` overrides `within = TRUE`.\n",
                 "The calculations will run with the option `within = FALSE`.\n",
                 "To remove this warning, set either `filter` or `within` to `FALSE`.")
    warning(msg)
    within <- FALSE
  }
  # Filtering genotypes -----------------------------------------------------
  if (filter && (haploid | !within | !heterozygous | !codominant)) {
    
    if (!is(x@mlg, "MLG")) {
      x@mlg <- new("MLG", x@mlg)
    }
    if (!quiet) {
      message("Filtering ...")
      message("Original multilocus genotypes   ... ", nmll(x, "original"))
    }
    if (is.null(dist)) {
      if (is_genind) {
        dist <- dist(tab(x, freq = freq)) 
      } else {
        dist <- bitwise.dist(x, euclidean = TRUE, scale_missing = TRUE, threads = threads)
      }
      squared <- FALSE
    }
    filt_stats <- mlg.filter(x, threshold = threshold, algorithm = algorithm,
                             distance = dist, stats = "ALL", threads = threads)
    dist <- as.dist(filt_stats$DISTANCE)
    # Forcing this. Probably should make an explicit method for this.
    x@mlg@mlg["contracted"]     <- filt_stats$MLGS
    distname(x@mlg)             <- "dist"
    distalgo(x@mlg)             <- algorithm
    cutoff(x@mlg)["contracted"] <- threshold
    mll(x)                      <- "contracted"
    if (!quiet) message("Contracted multilocus genotypes ... ", nmll(x))
  }
  if (clonecorrect) {
    x <- clonecorrect(x, strata = hier, keep = seq(all.vars(hier)))
  }
  # Treat missing data. This is a part I do not particularly like. The distance
  # matrix must be euclidean, but the dissimilarity distance will not allow
  # missing data to contribute to the distance. 
  #
  # This is corrected by setting missing to zero: more diversity
  # missing to mean of the columns: indiscreet distances.
  # remove loci at cutoff
  # remove individuals at cutoff
  if (is_genind) {
    x <- missingno(x, type = missing, cutoff = cutoff, quiet = quiet)
  } else {
    if (!all(lengths(NA.posi(x)) == 0L)) warning("Missing data are not filtered from genlight data.")
  }

  # Splitting haplotypes ----------------------------------------------------
  #
  full_ploidy <- if (is_genind) codominant && sum(tabulate(get_local_ploidy(x)) > 0) == 1 else TRUE
  if (within && heterozygous && codominant && !haploid && full_ploidy) {
    hier <- update(hier, ~./Individual)
    x    <- make_haplotypes(x)
    x    <- if (is_genind) as.genclone(x) else as.snpclone(x)
  } else if (within && codominant && !full_ploidy && is.null(dist)) {
    warning(paste("Data with mixed ploidy or ambiguous allele dosage cannot have",
            "within-individual variance calculated until the dosage is correctly",
            "estimated.\n\n",
            "This function will return the summary statistic, rho (Ronfort et al 1998)",
            "but be aware that this estimate will be skewed due to ambiguous dosage.",
            if (test_zeroes(x)) "If you have zeroes encoded in your data, you may wish to remove them.\n",
            "To remove this warning, use within = FALSE")
            )
  }
  
  hierdf <- strata(x, formula = hier)
  if (method == "ade4") xstruct <- make_ade_df(hier, hierdf)
  if (is.null(dist)) {
    squared <- FALSE
    if (method == "ade4") {
      if (is_genind) {
        xdist <- dist(tab(clonecorrect(x, strata = NA), freq = freq)) 
      } else {
        xdist <- bitwise.dist(clonecorrect(x, strata = NA), 
                              euclidean = TRUE, scale_missing = TRUE, threads = threads)
      }
    } else {
      if (is_genind) {
        xdist <- dist(tab(x, freq = freq)) 
      } else {
        xdist <- bitwise.dist(x, euclidean = TRUE, scale_missing = TRUE, threads = threads)
      }
    }
  } else {
    datalength <- choose(nInd(x), 2)
    mlgs       <- mlg(x, quiet = TRUE)
    mlglength  <- choose(mlgs, 2)
    if (length(dist) > mlglength & length(dist) == datalength) {
      corrected <- if (method == "ade4") .clonecorrector(x) else TRUE
      xdist     <- as.dist(as.matrix(dist)[corrected, corrected])
    } else if (length(dist) == mlglength) {
      xdist <- dist
    } else {
      distobs <- ceiling(sqrt(length(dist)*2))
      msg <- paste("\nDistance matrix does not match the data.\n",
      "\n\tUncorrected observations expected..........", nInd(x),
      "\n\tClone corrected observations expected......", mlgs,
      "\n\tObservations in provided distance matrix...", distobs,
      ifelse(within == TRUE, "\n\n\tTry setting within = FALSE.", "\n"))
      stop(msg)
    }
    if (squared) {
      xdist <- sqrt(xdist)
    }
  }
  if (!is.euclid(xdist)) {
    CORRECTIONS <- c("cailliez", "quasieuclid", "lingoes")
    try(correct <- match.arg(correction, CORRECTIONS), silent = TRUE)
    if (!exists("correct")){
      stop(not_euclid_msg(correction))
    } else {
      correct_fun <- match.fun(correct)
      if (correct == CORRECTIONS[2]){
        message("Distance matrix is non-euclidean.")
        message(c("Using quasieuclid correction method.",
                  " See ?quasieuclid for details."))
        xdist <- correct_fun(xdist)        
      } else {
        xdist <- correct_fun(xdist, print = TRUE, cor.zero = FALSE)        
      }
    }
  }
  if (method == "ade4") {
    allmlgs <- unique(mlg.vector(x))
    xtab    <- t(mlg.table(x, plot = FALSE, quiet = TRUE, mlgsub = allmlgs))
    xtab    <- as.data.frame(xtab)
    return(ade4::amova(samples = xtab, distances = xdist, structures = xstruct))
  } else {
    form <- paste(all.vars(hier), collapse = "/")
    hier <- as.formula(paste("xdist ~", form))
    return(pegas::amova(hier, data = hierdf, nperm = nperm, is.squared = FALSE))
  }
}

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poppr documentation built on Sept. 7, 2021, 9:06 a.m.