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Merge branch 'issue-2' into develop

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Waldir Leoncio 2022-05-19 15:08:03 +02:00
commit fccd8b406b
5 changed files with 270 additions and 263 deletions
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2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: rBAPS
Title: Bayesian Analysis of Population Structure
Version: 0.0.0.9007
Version: 0.0.0.9008
Date: 2020-11-09
Authors@R:
c(

1
NAMESPACE
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@ -38,6 +38,7 @@ export(takeLine)
export(testaaOnkoKunnollinenBapsData)
export(testaaPop)
export(writeMixtureInfo)
export(writeMixtureInfoPop)
importFrom(Rsamtools,scanBam)
importFrom(adegenet,.readExt)
importFrom(adegenet,read.genepop)

225
R/writeMixtureInfoPop.R Normal file
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@ -0,0 +1,225 @@
#' @title Write Mixture Info Pop
#' @description Writes information about the pop mixture
#' @param logml logml
#' @param rows rows
#' @param data data
#' @param adjprior adjprior
#' @param priorTerm priorTerm
#' @param outPutFile outPutFile
#' @param inputFile inputFile
#' @param partitionSummary partitionSummary
#' @param popnames popnames
#' @param fixedK fixedK
#' @export
writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
outPutFile, inputFile, partitionSummary,
popnames, fixedK) {
ninds <- size(rows, 1)
npops <- size(COUNTS, 3)
names <- size(popnames, 1) == ninds # Tarkistetaan ett?nimet viittaavat yksilöihin
changesInLogml <- vector()
if (!missing(outPutFile)) {
fid <- vector()
}
cat("RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:\n")
cat("Data file:", inputFile, "\n")
cat("Number of clustered groups:", ownNum2Str(ninds), "\n")
cat("Number of clusters in optimal partition:", ownNum2Str(npops), "\n")
cat("Log(marginal likelihood) of optimal partition:", ownNum2Str(logml), "\n")
if (exists("fid")) {
append(fid, "RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:\n")
append(fid, c("Data file:", inputFile, "\n"))
append(fid, c("Number of clustered groups:", ownNum2Str(ninds), "\n"))
append(fid, c("Number of clusters in optimal partition:", ownNum2Str(npops), "\n"))
append(fid, c("Log(marginal likelihood) of optimal partition:", ownNum2Str(logml), "\n\n"))
}
cluster_count <- length(unique(PARTITION))
cat("Best Partition:\n")
if (exists("fid")) {
append(fid, c("Best partition:\n"))
}
for (m in 1:cluster_count) {
indsInM <- find(PARTITION == m)
length_of_beginning <- 11 + floor(log10(m))
cluster_size <- length(indsInM)
if (names) {
text <- c("Cluster ", as.character(m), ": {", as.character(popnames[indsInM[1]]))
for (k in 2:cluster_size) {
text <- c(text, ", ", as.character(popnames[[indsInM[k]]]))
}
} else {
text <- c("Cluster ", as.character(m), ": {", as.character(indsInM[1]))
for (k in 2:cluster_size) {
text <- c(text, ", ", as.character(indsInM[k]))
}
}
text <- c(text, "}")
while (length(text) > 58) {
# Take one line and display it.
new_line <- takeLine(text, 58)
text <- text[(length(new_line) + 1):length(text)]
cat(new_line, "\n")
if (exists("fid")) {
append(fid, c(new_line, "\n"))
}
if (length(text) > 0) {
text <- c(blanks(length_of_beginning), text)
} else {
text <- vector()
}
}
if (!is.null(text)) {
cat(text, "\n")
if (exists(fid)) {
append(fid, c("\n", text, "\n"))
}
}
}
if (npops > 1) {
cat("\n\nChanges in log(marginal likelihood) if (group i is moved to cluster j:")
if (exists("fid")) {
append(fid, " \n \n", )
append(fid, "Changes in log(marginal likelihood) if (group i is moved to cluster j:")
}
if (names) {
nameSizes <- zeros(ninds, 1)
for (i in 1:ninds) {
nimi <- as.character(popnames[i])
nameSizes[i] <- length(nimi)
}
maxSize <- max(nameSizes)
maxSize <- max(maxSize, 5)
erotus <- maxSize - 5
alku <- blanks(erotus)
ekarivi <- c(alku, "group", blanks(6 + erotus))
} else {
ekarivi <- "group "
}
for (i in 1:cluster_count) {
ekarivi <- c(ekarivi, ownNum2Str(i), blanks(8 - floor(log10(i))))
}
cat(ekarivi, "\n")
if (exists("fid")) {
append(fid, c(ekarivi, "\n"))
}
changesInLogml <- t(LOGDIFF)
for (ind in 1:ninds) {
muutokset <- changesInLogml[, ind]
if (names) {
nimi <- as.character(popnames[ind])
rivi <- c(blanks(maxSize - length(nimi)), nimi, ":")
} else {
rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":")
}
for (j in 1:npops) {
rivi <- c(rivi, " ", logml2String(omaRound(muutokset(j))))
}
cat(rivi, "\n")
if (exists("fid")) {
append(fid, c(rivi, "\n"))
}
}
cat(" ")
cat("KL-divergence matrix in PHYLIP format:")
dist_mat <- zeros(npops, npops)
if (exists("fid")) {
append(fid, " \n")
append(fid, " \n")
append(fid, "KL - divergence matrix in PHYLIP format:\n")
}
maxnoalle <- size(COUNTS, 1)
nloci <- size(COUNTS, 2)
d <- zeros(maxnoalle, nloci, npops)
prior <- adjprior
prior[find[prior == 1]] <- 0
nollia <- find(all(prior == 0)) # Lokukset, joissa oli havaittu vain yht?alleelia.
prior[1, nollia] <- 1
for (pop1 in 1:npops) {
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
}
ekarivi <- as.character(npops)
cat(ekarivi, "\n")
if (exists("fid")) {
append(fid, c(ekarivi, "\n"))
}
for (pop1 in 1:npops) {
rivi <- c(blanks(2 - floor(log10(pop1))), as.character(pop1), " ")
for (pop2 in 1:(pop1 - 1)) {
dist1 <- d[, , pop1]
dist2 <- d[, , pop2]
div12 <- sum(sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))) / nloci
div21 <- sum(sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))) / nloci
div <- (div12 + div21) / 2
dist_mat[pop1, pop2] <- div
}
}
dist_mat <- dist_mat + t(dist_mat) # make it symmetric
for (pop1 in 1:npops) {
rivi <- c("Cluster_", as.character(pop1), " ")
for (pop2 in 1:npops) {
rivi <- c(rivi, kldiv2str(dist_mat(pop1, pop2)), " ")
}
cat(rivi)
if (exists("fid")) {
append(fid, c(rivi, "\n"))
}
}
}
cat(" \n \n \n")
cat("List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
if (exists("fid")) {
append(fid, " \n\n")
append(fid, " \n\n")
append(fid, " \n\n")
append(fid, " \n\n")
append(fid, "List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
}
partitionSummary <- sortrows(partitionSummary, 2)
partitionSummary <- partitionSummary[size(partitionSummary, 1):-1, ]
partitionSummary <- partitionSummary[find(partitionSummary[, 2] > -1e49), ]
if (size(partitionSummary, 1) > 10) {
vikaPartitio <- 10
} else {
vikaPartitio <- size(partitionSummary, 1)
}
for (part in 1:vikaPartitio) {
line <- c(as.character(partitionSummary[part, 1]), " ", as.character(partitionSummary[part, 2]))
cat(line, "\n")
if (exists("fid")) {
append(fid, c(line, "\n"))
}
}
if (!fixedK) {
cat(" \n")
cat(" \n")
cat("Probabilities for number of clusters\n")
if (exists("fid")) {
append(fid, " \n\n")
append(fid, " \n\n")
append(fid, "Probabilities for number of clusters\n")
}
npopsTaulu <- unique(partitionSummary[, 1])
len <- length(npopsTaulu)
probs <- zeros(len, 1)
partitionSummary[, 2] <- partitionSummary[, 2] - max(partitionSummary[, 2])
sumtn <- sum(exp(partitionSummary[, 2]))
for (i in 1:len) {
npopstn <- sum(exp(partitionSummary(find(partitionSummary[, 1] == npopsTaulu(i)), 2)))
probs[i] <- npopstn / sumtn
}
for (i in 1:len) {
if (probs(i) > 1e-5) {
line <- c(as.character(npopsTaulu(i)), " ", as.character(probs(i)))
cat(line)
if (exists("fid")) {
append(fid, line)
append(fid, "\n")
}
}
}
}
if (exists("fid")) {
save(fid, file = outPutFile)
}
return(changesInLogml)
}

43
man/writeMixtureInfoPop.Rd Normal file
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@ -0,0 +1,43 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/writeMixtureInfoPop.R
\name{writeMixtureInfoPop}
\alias{writeMixtureInfoPop}
\title{Write Mixture Info Pop}
\usage{
writeMixtureInfoPop(
logml,
rows,
data,
adjprior,
priorTerm,
outPutFile,
inputFile,
partitionSummary,
popnames,
fixedK
)
}
\arguments{
\item{logml}{logml}
\item{rows}{rows}
\item{data}{data}
\item{adjprior}{adjprior}
\item{priorTerm}{priorTerm}
\item{outPutFile}{outPutFile}
\item{inputFile}{inputFile}
\item{partitionSummary}{partitionSummary}
\item{popnames}{popnames}
\item{fixedK}{fixedK}
}
\description{
Writes information about the pop mixture
}

262
matlab/independent/greedyPopMix.m
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@ -250,265 +250,3 @@ else
delete('baps4_output.baps')
end
end
%-------------------------------------------------------------------
function changesInLogml = writeMixtureInfoPop(logml, rows, data, adjprior, ...
priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK)
global PARTITION;
global COUNTS;
global SUMCOUNTS;
global LOGDIFF;
ninds = size(rows,1);
npops = size(COUNTS,3);
names = (size(popnames,1) == ninds); %Tarkistetaan ett?nimet viittaavat yksilöihin
changesInLogml = [];
if length(outPutFile)>0
fid = fopen(outPutFile,'a');
else
fid = -1;
diary('baps4_output.baps'); % save in text anyway.
end
dispLine;
disp('RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:');
disp(['Data file: ' inputFile]);
disp(['Number of clustered groups: ' ownNum2Str(ninds)]);
disp(['Number of clusters in optimal partition: ' ownNum2Str(npops)]);
disp(['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
disp(' ');
if (fid ~= -1)
fprintf(fid,'%s \n', ['RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:']); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Data file: ' inputFile]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Number of clustered groups: ' ownNum2Str(ninds)]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Number of clusters in optimal partition: ' ownNum2Str(npops)]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]); fprintf(fid,'\n');
fprintf(fid,'\n');
end
cluster_count = length(unique(PARTITION));
disp(['Best Partition: ']);
if (fid ~= -1)
fprintf(fid,'%s \n',['Best Partition: ']); fprintf(fid,'\n');
end
for m=1:cluster_count
indsInM = find(PARTITION==m);
length_of_beginning = 11 + floor(log10(m));
cluster_size = length(indsInM);
if names
text = ['Cluster ' num2str(m) ': {' char(popnames{indsInM(1)})];
for k = 2:cluster_size
text = [text ', ' char(popnames{indsInM(k)})];
end;
else
text = ['Cluster ' num2str(m) ': {' num2str(indsInM(1))];
for k = 2:cluster_size
text = [text ', ' num2str(indsInM(k))];
end;
end
text = [text '}'];
while length(text)>58
%Take one line and display it.
new_line = takeLine(text,58);
text = text(length(new_line)+1:end);
disp(new_line);
if (fid ~= -1)
fprintf(fid,'%s \n',[new_line]);
fprintf(fid,'\n');
end
if length(text)>0
text = [blanks(length_of_beginning) text];
else
text = [];
end;
end;
if ~isempty(text)
disp(text);
if (fid ~= -1)
fprintf(fid,'%s \n',[text]);
fprintf(fid,'\n');
end
end;
end
if npops > 1
disp(' ');
disp(' ');
disp('Changes in log(marginal likelihood) if group i is moved to cluster j:');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', ['Changes in log(marginal likelihood) if group i is moved to cluster j:']); %fprintf(fid, '\n');
end
if names
nameSizes = zeros(ninds,1);
for i = 1:ninds
nimi = char(popnames{i});
nameSizes(i) = length(nimi);
end
maxSize = max(nameSizes);
maxSize = max(maxSize, 5);
erotus = maxSize - 5;
alku = blanks(erotus);
ekarivi = [alku 'group' blanks(6+erotus)];
else
ekarivi = 'group ';
end
for i = 1:cluster_count
ekarivi = [ekarivi ownNum2Str(i) blanks(8-floor(log10(i)))];
end
disp(ekarivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
end
changesInLogml = LOGDIFF';
for ind = 1:ninds
%[muutokset, diffInCounts] = laskeMuutokset(ind, rows, data, ...
% adjprior, priorTerm);
%changesInLogml(:,ind) = muutokset;
muutokset = changesInLogml(:,ind);
if names
nimi = char(popnames{ind});
rivi = [blanks(maxSize - length(nimi)) nimi ':'];
else
rivi = [blanks(4-floor(log10(ind))) ownNum2Str(ind) ':'];
end
for j = 1:npops
rivi = [rivi ' ' logml2String(omaRound(muutokset(j)))];
end
disp(rivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
end
end
disp(' '); disp(' ');
disp('KL-divergence matrix in PHYLIP format:');
dist_mat = zeros(npops, npops);
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', ['KL-divergence matrix in PHYLIP format:']); %fprintf(fid, '\n');
end
maxnoalle = size(COUNTS,1);
nloci = size(COUNTS,2);
d = zeros(maxnoalle, nloci, npops);
prior = adjprior;
prior(find(prior==1))=0;
nollia = find(all(prior==0)); %Lokukset, joissa oli havaittu vain yht?alleelia.
prior(1,nollia)=1;
for pop1 = 1:npops
d(:,:,pop1) = (squeeze(COUNTS(:,:,pop1))+prior) ./ repmat(sum(squeeze(COUNTS(:,:,pop1))+prior),maxnoalle,1);
%dist1(pop1) = (squeeze(COUNTS(:,:,pop1))+adjprior) ./ repmat((SUMCOUNTS(pop1,:)+adjprior), maxnoalle, 1);
end
% ekarivi = blanks(7);
% for pop = 1:npops
% ekarivi = [ekarivi num2str(pop) blanks(7-floor(log10(pop)))];
% end
ekarivi = num2str(npops);
disp(ekarivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
end
for pop1 = 1:npops
rivi = [blanks(2-floor(log10(pop1))) num2str(pop1) ' '];
for pop2 = 1:pop1-1
dist1 = d(:,:,pop1); dist2 = d(:,:,pop2);
div12 = sum(sum(dist1.*log2((dist1+10^-10) ./ (dist2+10^-10))))/nloci;
div21 = sum(sum(dist2.*log2((dist2+10^-10) ./ (dist1+10^-10))))/nloci;
div = (div12+div21)/2;
% rivi = [rivi kldiv2str(div) ' '];
dist_mat(pop1,pop2) = div;
end
% disp(rivi);
% if (fid ~= -1)
% fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
% end
end
dist_mat = dist_mat + dist_mat'; % make it symmetric
for pop1 = 1:npops
rivi = ['Cluster_' num2str(pop1) ' '];
for pop2 = 1:npops
rivi = [rivi kldiv2str(dist_mat(pop1,pop2)) ' '];
end
disp(rivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [rivi]); %fprintf(fid, '\n');
end
end
end
disp(' ');
disp(' ');
disp('List of sizes of 10 best visited partitions and corresponding log(ml) values');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', ['List of sizes of 10 best visited partitions and corresponding log(ml) values']); fprintf(fid, '\n');
end
partitionSummary = sortrows(partitionSummary,2);
partitionSummary = partitionSummary(size(partitionSummary,1):-1:1 , :);
partitionSummary = partitionSummary(find(partitionSummary(:,2)>-1e49),:);
if size(partitionSummary,1)>10
vikaPartitio = 10;
else
vikaPartitio = size(partitionSummary,1);
end
for part = 1:vikaPartitio
line = [num2str(partitionSummary(part,1)) ' ' num2str(partitionSummary(part,2))];
disp(line);
if (fid ~= -1)
fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
end
end
if ~fixedK
disp(' ');
disp(' ');
disp('Probabilities for number of clusters');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', ['Probabilities for number of clusters']); fprintf(fid, '\n');
end
npopsTaulu = unique(partitionSummary(:,1));
len = length(npopsTaulu);
probs = zeros(len,1);
partitionSummary(:,2) = partitionSummary(:,2)-max(partitionSummary(:,2));
sumtn = sum(exp(partitionSummary(:,2)));
for i=1:len
npopstn = sum(exp(partitionSummary(find(partitionSummary(:,1)==npopsTaulu(i)),2)));
probs(i) = npopstn / sumtn;
end
for i=1:len
if probs(i)>1e-5
line = [num2str(npopsTaulu(i)) ' ' num2str(probs(i))];
disp(line);
if (fid ~= -1)
fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
end
end
end
end
if (fid ~= -1)
fclose(fid);
else
diary off
end