409 lines
No EOL
16 KiB
R
409 lines
No EOL
16 KiB
R
#' @title Admixture analysis
|
|
#' @param tietue a named record list
|
|
#' @details If the record == -1, the mixture results file is loaded. Otherwise,
|
|
#' will the required variables be retrieved from the record fields?
|
|
#' `tietue`should contain the following elements: PARTITION, COUNTS, SUMCOUNTS,
|
|
#' alleleCodes, adjprior, popnames, rowsFromInd, data, npops, noalle
|
|
#' @param tietue tietue
|
|
#' @param PARTITION PARTITION
|
|
#' @param COUNTS COUNTS
|
|
#' @param SUMCOUNTS SUMCOUNTS
|
|
#' @importFrom methods is
|
|
#' @export
|
|
admix1 <- function(tietue, PARTITION = matrix(NA, 0, 0),
|
|
COUNTS = matrix(NA, 0, 0), SUMCOUNTS = NA) {
|
|
if (!is.list(tietue)) {
|
|
message('Load mixture result file. These are the files in this directory:')
|
|
print(list.files())
|
|
pathname_filename <- file.choose()
|
|
if (!file.exists(pathname_filename)) {
|
|
stop(
|
|
"File ", pathname_filename,
|
|
" does not exist. Check spelling and location."
|
|
)
|
|
} else {
|
|
cat('---------------------------------------------------\n');
|
|
message('Reading mixture result from: ', pathname_filename, '...')
|
|
}
|
|
Sys.sleep(0.0001) #TODO: remove
|
|
|
|
# ASK: what is this supposed to do? What do graphic obj have to do here?
|
|
# h0 = findobj('Tag','filename1_text');
|
|
# set(h0,'String',filename); clear h0;
|
|
|
|
struct_array <- load(pathname_filename)
|
|
if (isfield(struct_array, 'c')) { #Matlab versio
|
|
c <- struct_array$c
|
|
if (!isfield(c, 'PARTITION') | !isfield(c,'rowsFromInd')) {
|
|
stop('Incorrect file format')
|
|
}
|
|
} else if (isfield(struct_array, 'PARTITION')) { #Mideva versio
|
|
c <- struct_array
|
|
if (!isfield(c,'rowsFromInd')) stop('Incorrect file format')
|
|
} else {
|
|
stop('Incorrect file format')
|
|
}
|
|
|
|
if (isfield(c, 'gene_lengths') &
|
|
strcmp(c$mixtureType, 'linear_mix') |
|
|
strcmp(c$mixtureType, 'codon_mix')) { # if the mixture is from a linkage model
|
|
# Redirect the call to the linkage admixture function.
|
|
# call function noindex to remove the index column
|
|
c$data <- noIndex(c$data, c$noalle)
|
|
# linkage_admix(c) # TODO: obsolete. remove.
|
|
# return
|
|
stop("linkage_admix not implemented")
|
|
}
|
|
PARTITION <- c$PARTITION
|
|
COUNTS <- c$COUNTS
|
|
SUMCOUNTS <- c$SUMCOUNTS
|
|
alleleCodes <- c$alleleCodes
|
|
adjprior <- c$adjprior
|
|
popnames <- c$popnames
|
|
rowsFromInd <- c$rowsFromInd
|
|
data <- c$data
|
|
npops <- c$npops
|
|
noalle <- c$noalle
|
|
} else {
|
|
PARTITION <- tietue$PARTITION
|
|
COUNTS <- tietue$COUNTS
|
|
SUMCOUNTS <- tietue$SUMCOUNTS
|
|
alleleCodes <- tietue$alleleCodes
|
|
adjprior <- tietue$adjprior
|
|
popnames <- tietue$popnames
|
|
rowsFromInd <- tietue$rowsFromInd
|
|
data <- as.double(tietue$data)
|
|
npops <- tietue$npops
|
|
noalle <- tietue$noalle
|
|
}
|
|
|
|
answers <- inputdlg(
|
|
prompt = paste(
|
|
"Input the minimum size of a population that will",
|
|
"be taken into account when admixture is estimated."
|
|
),
|
|
definput = 5
|
|
)
|
|
alaRaja <- as.numeric(answers)
|
|
npops <- poistaLiianPienet(npops, rowsFromInd, alaRaja)
|
|
|
|
nloci <- size(COUNTS, 2)
|
|
ninds <- size(data, 1) / rowsFromInd
|
|
|
|
answers <- inputdlg('Input number of iterations', 50)
|
|
if (isempty(answers)) return()
|
|
iterationCount <- as.numeric(answers[1, 1]) # maybe [[]]?
|
|
|
|
answers <- inputdlg(
|
|
prompt = 'Input number of reference individuals from each population',
|
|
definput = 50
|
|
)
|
|
if (isempty(answers)) {
|
|
nrefIndsInPop <- 50
|
|
} else {
|
|
nrefIndsInPop <- as.numeric(answers[1, 1])
|
|
}
|
|
|
|
answers <- inputdlg(
|
|
prompt = 'Input number of iterations for reference individuals',
|
|
definput = 10
|
|
)
|
|
if (isempty(answers)) return()
|
|
iterationCountRef <- as.numeric(answers[1, 1])
|
|
|
|
# First calculate log-likelihood ratio for all individuals:
|
|
likelihood <- zeros(ninds, 1)
|
|
allfreqs <- computeAllFreqs2(noalle)
|
|
for (ind in 1:ninds) {
|
|
omaFreqs <- computePersonalAllFreqs(ind, data, allfreqs, rowsFromInd)
|
|
osuusTaulu <- zeros(1, npops)
|
|
if (PARTITION[ind] == 0) {
|
|
# Yksil?on outlier
|
|
} else if (PARTITION[ind] != 0) {
|
|
if (PARTITION[ind] > 0) {
|
|
osuusTaulu[PARTITION[ind]] <- 1
|
|
} else {
|
|
# Yksilöt, joita ei ole sijoitettu mihinkään koriin.
|
|
arvot <- zeros(1, npops)
|
|
for (q in 1:npops) {
|
|
osuusTaulu <- zeros(1, npops)
|
|
osuusTaulu[q] <- 1
|
|
arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
|
|
}
|
|
iso_arvo <- max(arvot)
|
|
isoimman_indeksi <- match(max(arvot), arvot)
|
|
osuusTaulu <- zeros(1, npops)
|
|
osuusTaulu[isoimman_indeksi] <- 1
|
|
PARTITION[ind] <- isoimman_indeksi
|
|
}
|
|
logml <- computeIndLogml(omaFreqs, osuusTaulu)
|
|
logmlAlku <- logml
|
|
for (osuus in c(0.5, 0.25, 0.05, 0.01)) {
|
|
etsiResult <- etsiParas(osuus, osuusTaulu, omaFreqs, logml)
|
|
osuusTaulu <- etsiResult[1]
|
|
logml <- etsiResult[2]
|
|
}
|
|
logmlLoppu <- logml
|
|
likelihood[ind] <- logmlLoppu - logmlAlku
|
|
}
|
|
}
|
|
|
|
# Analyze further only individuals who have log-likelihood ratio larger than 3:
|
|
to_investigate <- t(find(likelihood > 3))
|
|
cat('Possibly admixed individuals:\n')
|
|
for (i in 1:length(to_investigate)) {
|
|
cat(as.character(to_investigate[i]))
|
|
}
|
|
cat(' ')
|
|
cat('Populations for possibly admixed individuals:\n')
|
|
admix_populaatiot <- unique(PARTITION[to_investigate])
|
|
for (i in 1:length(admix_populaatiot)) {
|
|
cat(as.character(admix_populaatiot[i]))
|
|
}
|
|
|
|
# THUS, there are two types of individuals, who will not be analyzed with
|
|
# simulated allele frequencies: those who belonged to a mini-population
|
|
# which was removed, and those who have log-likelihood ratio less than 3.
|
|
# The value in the PARTITION for the first kind of individuals is 0. The
|
|
# second kind of individuals can be identified, because they do not
|
|
# belong to "to_investigate" array. When the results are presented, the
|
|
# first kind of individuals are omitted completely, while the second kind
|
|
# of individuals are completely put to the population, where they ended up
|
|
# in the mixture analysis. These second type of individuals will have a
|
|
# unit p-value.
|
|
|
|
|
|
# Simulate allele frequencies a given number of times and save the average
|
|
# result to "proportionsIt" array.
|
|
|
|
proportionsIt <- zeros(ninds, npops)
|
|
for (iterationNum in 1:iterationCount) {
|
|
cat('Iter:', as.character(iterationNum))
|
|
allfreqs <- simulateAllFreqs(noalle) # Allele frequencies on this iteration.
|
|
|
|
for (ind in to_investigate) {
|
|
#disp(num2str(ind));
|
|
omaFreqs <- computePersonalAllFreqs(
|
|
ind, data, allfreqs, rowsFromInd
|
|
)
|
|
osuusTaulu <- zeros(1, npops)
|
|
if (PARTITION[ind] == 0) {
|
|
# Yksil?on outlier
|
|
} else if (PARTITION[ind] != 0) {
|
|
if (PARTITION[ind] > 0) {
|
|
osuusTaulu[PARTITION[ind]] <- 1
|
|
} else {
|
|
# Yksilöt, joita ei ole sijoitettu mihinkään koriin.
|
|
arvot <- zeros(1, npops)
|
|
for (q in 1:npops) {
|
|
osuusTaulu <- zeros(1, npops)
|
|
osuusTaulu[q] <- 1
|
|
arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
|
|
}
|
|
iso_arvo <- max(arvot)
|
|
isoimman_indeksi <- match(max(arvot), arvot)
|
|
osuusTaulu <- zeros(1, npops)
|
|
osuusTaulu[isoimman_indeksi] <- 1
|
|
PARTITION[ind] <- isoimman_indeksi
|
|
}
|
|
logml <- computeIndLogml(omaFreqs, osuusTaulu)
|
|
|
|
for (osuus in c(0.5, 0.25, 0.05, 0.01)) {
|
|
etsiResult <- etsiParas(osuus, osuusTaulu, omaFreqs, logml)
|
|
osuusTaulu <- etsiResult[1]
|
|
logml <- etsiResult[2]
|
|
}
|
|
}
|
|
proportionsIt[ind, ] <- proportionsIt[ind, ] * (iterationNum - 1) +
|
|
osuusTaulu
|
|
proportionsIt[ind, ] <- proportionsIt[ind, ] / iterationNum
|
|
}
|
|
}
|
|
|
|
#disp(['Creating ' num2str(nrefIndsInPop) ' reference individuals from ']);
|
|
#disp('each population.');
|
|
|
|
#allfreqs = simulateAllFreqs(noalle); # Simuloidaan alleelifrekvenssisetti
|
|
allfreqs <- computeAllFreqs2(noalle); # Koitetaan tällaista.
|
|
|
|
|
|
# Initialize the data structures, which are required in taking the missing
|
|
# data into account:
|
|
n_missing_levels <- zeros(npops, 1) # number of different levels of "missingness" in each pop (max 3).
|
|
missing_levels <- zeros(npops, 3) # the mean values for different levels.
|
|
missing_level_partition <- zeros(ninds, 1) # level of each individual (one of the levels of its population).
|
|
for (i in 1:npops) {
|
|
inds <- find(PARTITION == i)
|
|
# Proportions of non-missing data for the individuals:
|
|
non_missing_data <- zeros(length(inds), 1)
|
|
for (j in 1:length(inds)) {
|
|
ind <- inds[j]
|
|
non_missing_data[j] <- length(
|
|
find(data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0)
|
|
) / (rowsFromInd * nloci)
|
|
}
|
|
if (all(non_missing_data > 0.9)) {
|
|
n_missing_levels[i] <- 1
|
|
missing_levels[i, 1] <- mean(non_missing_data)
|
|
missing_level_partition[inds] <- 1
|
|
} else {
|
|
# TODO: fix syntax
|
|
# [ordered, ordering] = sort(non_missing_data);
|
|
ordered <- ordering <- sort(non_missing_data)
|
|
#part = learn_simple_partition(ordered, 0.05);
|
|
part <- learn_partition_modified(ordered)
|
|
aux <- sortrows(cbind(part, ordering), 2)
|
|
part = aux[, 1]
|
|
missing_level_partition[inds]<- part
|
|
n_levels <- length(unique(part))
|
|
n_missing_levels[i] <- n_levels
|
|
for (j in 1:n_levels) {
|
|
missing_levels[i, j] <- mean(non_missing_data[find(part == j)])
|
|
}
|
|
}
|
|
}
|
|
|
|
# Create and analyse reference individuals for populations
|
|
# with potentially admixed individuals:
|
|
refTaulu <- zeros(npops, 100, 3)
|
|
for (pop in t(admix_populaatiot)) {
|
|
|
|
for (level in 1:n_missing_levels[pop]) {
|
|
|
|
potential_inds_in_this_pop_and_level <-
|
|
find(
|
|
PARTITION == pop & missing_level_partition == level &
|
|
likelihood > 3
|
|
) # Potential admix individuals here.
|
|
|
|
if (!isempty(potential_inds_in_this_pop_and_level)) {
|
|
|
|
#refData = simulateIndividuals(nrefIndsInPop,rowsFromInd,allfreqs);
|
|
refData <- simulateIndividuals(
|
|
nrefIndsInPop, rowsFromInd, allfreqs, pop,
|
|
missing_levels[pop, level]
|
|
)
|
|
|
|
cat(
|
|
'Analysing the reference individuals from pop', pop,
|
|
'(level', level, ').'
|
|
)
|
|
refProportions <- zeros(nrefIndsInPop, npops)
|
|
for (iter in 1:iterationCountRef) {
|
|
#disp(['Iter: ' num2str(iter)]);
|
|
allfreqs <- simulateAllFreqs(noalle)
|
|
|
|
for (ind in 1:nrefIndsInPop) {
|
|
omaFreqs <- computePersonalAllFreqs(
|
|
ind, refData, allfreqs, rowsFromInd
|
|
)
|
|
osuusTaulu <- zeros(1, npops)
|
|
osuusTaulu[pop] <- 1
|
|
logml <- computeIndLogml(omaFreqs, osuusTaulu)
|
|
for (osuus in c(0.5, 0.25, 0.05, 0.01)) {
|
|
etsiResult <- etsiParas(
|
|
osuus, osuusTaulu, omaFreqs, logml
|
|
)
|
|
osuusTaulu <- etsiResult[1]
|
|
logml <- etsiResult[2]
|
|
}
|
|
refProportions[ind, ] <-
|
|
refProportions[ind, ] * (iter - 1) + osuusTaulu
|
|
refProportions[ind, ] <- refProportions[ind, ] / iter
|
|
}
|
|
}
|
|
for (ind in 1:nrefIndsInPop) {
|
|
omanOsuus <- refProportions[ind, pop]
|
|
if (round(omanOsuus * 100) == 0) {
|
|
omanOsuus <- 0.01
|
|
}
|
|
if (abs(omanOsuus) < 1e-5) {
|
|
omanOsuus <- 0.01
|
|
}
|
|
refTaulu[pop, round(omanOsuus*100), level] <-
|
|
refTaulu[pop, round(omanOsuus*100),level] + 1
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
# Rounding of the results:
|
|
proportionsIt <- proportionsIt * 100
|
|
proportionsIt <- round(proportionsIt)
|
|
proportionsIt <- proportionsIt / 100
|
|
for (ind in 1:ninds) {
|
|
if (!any(to_investigate == ind)) {
|
|
if (PARTITION[ind] > 0) {
|
|
proportionsIt[ind, PARTITION[ind]] <- 1
|
|
}
|
|
} else {
|
|
# In case of a rounding error, the sum is made equal to unity by
|
|
# fixing the largest value.
|
|
if ((PARTITION[ind] > 0) & (sum(proportionsIt[ind, ]) != 1)) {
|
|
isoin <- max(proportionsIt[ind, ])
|
|
indeksi <- match(isoin, max(proportionsIt[ind, ]))
|
|
erotus <- sum(proportionsIt[ind, ]) - 1
|
|
proportionsIt[ind, indeksi] <- isoin - erotus
|
|
}
|
|
}
|
|
}
|
|
|
|
# Calculate p-value for each individual:
|
|
uskottavuus <- zeros(ninds, 1)
|
|
for (ind in 1:ninds) {
|
|
pop <- PARTITION[ind]
|
|
if (pop == 0) { # Individual is outlier
|
|
uskottavuus[ind] <- 1
|
|
} else if (isempty(find(to_investigate == ind))) {
|
|
# Individual had log-likelihood ratio<3
|
|
uskottavuus[ind] <- 1
|
|
} else {
|
|
omanOsuus <- proportionsIt[ind, pop]
|
|
if (abs(omanOsuus) < 1e-5) {
|
|
omanOsuus <- 0.01
|
|
}
|
|
if (round(omanOsuus*100)==0) {
|
|
omanOsuus <- 0.01
|
|
}
|
|
level <- missing_level_partition[ind]
|
|
refPienempia <- sum(refTaulu[pop, 1:round(100*omanOsuus), level])
|
|
uskottavuus[ind] <- refPienempia / nrefIndsInPop
|
|
}
|
|
}
|
|
|
|
tulostaAdmixtureTiedot(proportionsIt, uskottavuus, alaRaja, iterationCount) # TODO: textual outputs. probably not necessary. translate nonetheless
|
|
viewPartition(proportionsIt, popnames) # TODO: adapt
|
|
|
|
talle = inputdlg('Do you want to save the admixture results? [Y/n]', 'y')
|
|
if (talle %in% c('y', 'Y', 'yes', 'Yes')) {
|
|
#waitALittle;
|
|
filename <- inputdlg(
|
|
'Save results as (file name):', 'admixture_results.rda'
|
|
)
|
|
|
|
|
|
if (filename == 0) {
|
|
# Cancel was pressed
|
|
return()
|
|
} else { # copy 'baps4_output.baps' into the text file with the same name.
|
|
if (file.exists('baps4_output.baps')) {
|
|
file.copy('baps4_output.baps', paste0(filename, '.txt'))
|
|
file.remove('baps4_output.baps')
|
|
}
|
|
}
|
|
|
|
if (!is(tietue, "list")) {
|
|
c$proportionsIt <- proportionsIt
|
|
c$pvalue <- uskottavuus # Added by Jing
|
|
c$mixtureType <- 'admix' # Jing
|
|
c$admixnpops <- npops;
|
|
save(c, file=filename)
|
|
} else {
|
|
tietue$proportionsIt <- proportionsIt
|
|
tietue$pvalue <- uskottavuus; # Added by Jing
|
|
tietue$mixtureType <- 'admix'
|
|
tietue$admixnpops <- npops
|
|
save(tietue, file=filename)
|
|
}
|
|
}
|
|
} |