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#' @name utils.assignment_2
#' @title Population assignment probabilities
#' @description
#' This function takes one individual and estimates
#' their probability of coming from individual populations
#' from multilocus genotype frequencies.
#
#' @param x Name of the genlight object containing the SNP data [required].
#' @param unknown Name of the individual to be assigned to a population [required].
# @param inbreeding_par The inbreeding parameter [default 0].
#' @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
#' This function is a re-implementation of the function multilocus_assignment
#' from package gstudio.
#' Description of the method used in this function can be found at:
#' https://dyerlab.github.io/applied_population_genetics/population-assignment.html
#' @return A \code{data.frame} consisting of assignment probabilities for each
#' population.
#' @author Custodian: Luis Mijangos -- Post to
#' \url{https://groups.google.com/d/forum/dartr}
#' @examples
#' require("dartR.data")
#' res <- utils.assignment_2(platypus.gl,unknown="T27")
#' @export
utils.assignment_2 <- function(x,
unknown,
# inbreeding_par = 0,
verbose = 2) {
# 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)
if (unknown %in% indNames(x) == FALSE) {
stop(error(
paste(" Individual", unknown, "is not in the genlight object\n")
))
}
# DO THE JOB
# filtering loci with all missing data by population
# x <- gl.filter.allna(x, by.pop = TRUE, verbose = 0)
unknown_pop <- gl.keep.ind(x, ind.list = unknown, verbose = 0)
unknown_pop <- data.frame(gl2alleles(unknown_pop))
x <- gl.drop.ind(x, ind.list = unknown, verbose = 0)
pop_names <- popNames(x)
pop_list <- seppop(x)
gl_alleles <- do.call(rbind, strsplit(x$loc.all, "/"))
frequencies <- lapply(pop_list, function(y) {
freq_allele <- gl.alf(y)
freqs_gl <-
data.frame(
Allele1 = gl_alleles[, 1],
Allele2 = gl_alleles[, 2],
count1 = freq_allele[, 1] * (nInd(y)*2),
count2 = freq_allele[, 2] * (nInd(y)*2)
)
return(freqs_gl)
})
ret <- data.frame(Population = pop_names, Likelihood = 0)
for (popx in 1:nPop(x)) {
# alpha_ = k in Baudouin and Lebrun (2000)
alpha_ <- 2
# the total number of different allelic states at this locus over all
# reference populations
k <- 2
# the total number of genes to be assigned
m <- nLoc(x)
# the total number of genes in the reference population sample
n <- nLoc(x)
term1 <- lgamma(m+1)
term2 <- lgamma(n+alpha_)
if (verbose >= 2) {
cat(
report(
" Assigning individual",
unknown,
"against population",
pop_names[popx],
"\n"
)
)
}
popfreq <- frequencies[[popx]]
loc <-
as.data.frame(do.call(rbind, strsplit(unname(
unlist(unknown_pop)
), ":")))
colnames(loc) <- c("a1", "a2")
df_assign <- cbind(loc, popfreq)
df_assign$a1_count <- NA
df_assign[which(df_assign$a1 == df_assign$a2 & df_assign$a1 == df_assign$Allele1),"a1_count"] <- 2
df_assign[which(df_assign$a1 == df_assign$a2 & df_assign$a1 == df_assign$Allele2),"a1_count"] <- 0
df_assign[which(df_assign$a1 != df_assign$a2),"a1_count"] <- 1
df_assign$a2_count <- NA
df_assign[which(df_assign$a1 == df_assign$a2 & df_assign$a1 == df_assign$Allele2),"a2_count"] <- 2
df_assign[which(df_assign$a1 == df_assign$a2 & df_assign$a1 == df_assign$Allele1),"a2_count"] <- 0
df_assign[which(df_assign$a1 != df_assign$a2),"a2_count"] <- 1
term3_1 <- lgamma(( df_assign$a1_count + df_assign$count1 + (alpha_/k) ))
term3_2 <- lgamma(( df_assign$a2_count + df_assign$count2 + (alpha_/k) ))
term3 <- sum(term3_1,term3_2,na.rm = TRUE)
term4_1 <- lgamma(df_assign$a1_count + 1)
term4_2 <- lgamma(df_assign$a2_count + 1)
term4 <- sum(term4_1,term4_2,na.rm = TRUE)
term5_1 <- lgamma(df_assign$a1_count + (alpha_/k) )
term5_2 <- lgamma(df_assign$a2_count + (alpha_/k) )
term5 <- sum(term5_1,term5_2,na.rm = TRUE)
term6 <- lgamma(m+n+alpha_)
log_L <- term1 + term2 + term3 - term4 - term5 - term6
# assign Likelihood
ret[popx, "Likelihood"] <- -log_L
}
ret <- ret[order(ret$Likelihood,decreasing = TRUE),]
ret$score <- ret$Likelihood / sum(ret$Likelihood)
ret$score <- round(ret$score, 5)
ret$Likelihood <- round(ret$Likelihood, 5)
# FLAG SCRIPT END
if (verbose >= 1) {
cat(report("Completed:", funname, "\n"))
}
# RETURN
return(invisible(ret))
}
gl2alleles <- function (gl) {
x <- as.matrix(gl[, ])
homs1 <-
paste(substr(gl@loc.all, 1, 1), "/", substr(gl@loc.all, 1, 1), sep = "")
hets <- gl@loc.all
homs2 <-
paste(substr(gl@loc.all, 3, 3), "/", substr(gl@loc.all, 3, 3), sep = "")
xx <- matrix(NA, ncol = ncol(x), nrow = nrow(x))
for (i in 1:nrow(x)) {
for (ii in 1:ncol(x)) {
inp <- x[i, ii]
if (!is.na(inp)) {
if (inp == 0)
xx[i, ii] <- homs1[ii]
else if (inp == 1)
xx[i, ii] <- hets[ii]
else if (inp == 2)
xx[i, ii] <- homs2[ii]
} else {
xx[i, ii] <- NA
}
}
}
xx <- gsub("/", ":", xx)
return(xx)
}
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