Added source Matlab code for reference
This commit is contained in:
Waldir Leoncio
parent
b8af977117
commit
b5d99903d2
186 changed files with 61405 additions and 1 deletions
651
matlab/independent/semi_linkageMixture_speed.m
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651
matlab/independent/semi_linkageMixture_speed.m
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function semi_linkageMixture_speed(c_train, c_test)
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% This function process adjusts the priors of the training data accoring to
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% the test data. Based on the adjusted priors, the test data is clustered.
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% modified from linkageMixture_speed.m by Lu Cheng, 16.02.2010
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% Update by Lu Cheng, 07.03.2011
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% case of only 1 sample in the test data has been handled
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% added by Lu Cheng, 11.03.2010
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global SCRIPT_MODE;
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global PARAMETERS;
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if isempty(SCRIPT_MODE)
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SCRIPT_MODE = false;
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end
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% -----------------
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%% compare the training data and test data, adjust priors
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%1% Compare the training data and test data to adjust the prior
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if ~all(all(c_train.component_mat == c_test.component_mat))
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disp('The gene lengths are different between the training data and the test data!');
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return;
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end
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flag = false; % whether the trained priors should be adjusted
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n_loci = size(c_train.alleleCodes,2);
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if c_train.rowsFromInd ~= c_test.rowsFromInd
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error('Inconsistant rows from each individual. Train: %d Test: %d. Quit! \n', ...
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c_train.rowsFromInd, c_test.rowsFromInd);
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return;
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elseif c_train.rowsFromInd > 1
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error('Data must be haploid. Quit! rowsFromInd: %d.\n', c_train.rowsFromInd);
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return;
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end
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for i=1:n_loci
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if flag; break; end
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a = setdiff(c_test.alleleCodes(:,i),c_train.alleleCodes(:,i));
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a = a(a~=0);
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if ~isempty(a)
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flag = true;
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fprintf('New alleles are detected in the test data at loci: %d\n',i);
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fprintf('The processing time will be much longer than usual. \n');
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end
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end
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%2% reprocess the data for clustering
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if flag
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% combing the training data and test data, adjust the priors
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combine_data = [c_train.data; c_test.data];
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n_train = size(c_train.data,1);
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n_samples = size(combine_data,1);
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combine_data(:,end) = (1:n_samples)';
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[data, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = handleData(combine_data);
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index = data(:,end);
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[data_clique, data_separator, noalle_clique, noalle_separator] = ...
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transform4(data, c_train.component_mat,'codon');
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data_clique = [data_clique index];
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data_separator = [data_separator index];
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% Count the data
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[counts_cq, nalleles_cq, prior_cq, adjprior_cq, genotypes_cq]...
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= allfreqsnew2(data_clique, double(noalle_clique));
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[counts_sp, nalleles_sp, prior_sp, adjprior_sp, genotypes_sp]...
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= allfreqsnew2(data_separator, double(noalle_separator));
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counts_cq = uint16(counts_cq);
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counts_sp = uint16(counts_sp);
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c_train.adjprior = adjprior;
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c_train.counts_cq = counts_cq(:,:,1:n_train);
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c_train.counts_sp = counts_sp(:,:,1:n_train);
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c_test.counts_cq = counts_cq(:,:,n_train+1:end);
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c_test.counts_sp = counts_sp(:,:,n_train+1:end);
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c_train.adjprior_cq = adjprior_cq;
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c_train.adjprior_sp = adjprior_sp;
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c_train.alleleCodes = alleleCodes;
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c_train.noalle = noalle;
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clear data rowsFromInd alleleCodes noalle adjprior priorTerm index
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clear data_clique data_separator noalle_clique noalle_separator
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clear counts_cq counts_sp nalleles_cq nalleles_sp prior_cq prior_sp adjprior_cq adjprior_sp
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else
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% KEY: adjust the test data to fit the configuration of the training data
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% the 'codes_cq' and 'codes_sp' are directly translated from DNA sequence
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% SEE 'i_encode_n.m' under the linkage folder
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num_cq = size(c_train.counts_cq, 1); num_sp = size(c_train.counts_sp, 1);
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n_loci_cq = size(c_train.counts_cq, 2); n_loci_sp = size(c_train.counts_sp, 2);
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n_inds = size(c_test.counts_cq, 3);
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counts_cq = zeros(num_cq, n_loci_cq, n_inds);
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counts_sp = zeros(num_sp, n_loci_sp, n_inds);
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% mapping the indexes of cliques and separators of the test data to the
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% indexes of the training data
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for k = 1:n_inds
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for j = 1:n_loci_cq
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[c, ia, ib] = intersect(c_test.codes_cq{j}, c_train.codes_cq{j},'rows');
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counts_cq(ib,j,k) = c_test.counts_cq(ia,j,k);
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end
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for j = 1:n_loci_sp
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[c, ia, ib] = intersect(c_test.codes_sp{j}, c_train.codes_sp{j},'rows');
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counts_sp(ib,j,k) = c_test.counts_sp(ia,j,k);
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end
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end
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c_test.counts_cq = counts_cq;
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c_test.counts_sp = counts_sp;
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clear c ia ib k j i;
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end
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%% cluster the test data
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% case of only 1 sample in the test data, added by Lu Cheng, 07.03.2011
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if size(c_test.data,1)~=1
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[Z,dist] = newGetDistances(c_test.data, c_test.rowsFromInd);
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c_test.Z = Z;
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c_test.dist = dist;
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end
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clear Z dist;
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message = cat(2,'There are currently ',num2str(length(unique(c_train.cluster_labels))),' clusters in the training data, please input upper bounds of cluster numbers in the test data.');
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if SCRIPT_MODE
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cluster_nums = str2num(PARAMETERS.cluster_num_upperbounds);
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else
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cluster_nums = inputdlg(message);
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if isempty(cluster_nums) == 1
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return;
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else
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cluster_nums = str2num(cluster_nums{:});
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end
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end
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% % Test purpose, Check the input data, there should be 1 allele in each loci (column)
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% % Lu Cheng, 25.02.2010
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% if ~all(all(squeeze(sum(c_train.counts_cq,1))))
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% disp('Missing cq value of some sample in counts_cq of the training data');
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% return;
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% elseif ~all(all(squeeze(sum(c_train.counts_sp,1))))
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% disp('Missing sp value of some sample in counts_sp of the training data');
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% return;
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% elseif ~all(all(squeeze(sum(c_test.counts_cq,1))))
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% disp('Missing cq value of some sample in counts_cq of the test data');
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% return;
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% elseif ~all(all(squeeze(sum(c_test.counts_sp,1))))
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% disp('Missing sp value of some sample in counts_sp of the test data');
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% return;
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% end
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tic
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semi_res = semi_linkageMix(c_train, c_test, cluster_nums);
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toc
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semi_res.popnames = c_test.popnames;
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writeMixtureInfo(semi_res);
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% save the results
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if SCRIPT_MODE
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save_results = PARAMETERS.save_results;
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else
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save_results = questdlg('Do you wish to save the results?',...
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'Save Results','Yes','No','Yes');
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end
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if isequal(save_results,'Yes')
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if SCRIPT_MODE
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save(PARAMETERS.result_file,'semi_res','-v7.3');
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else
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[filename, pathname] = uiputfile('*.mat','Save the results as');
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if (sum(filename)==0) || (sum(pathname)==0)
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% do nothing
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else
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save(strcat(pathname,filename),'semi_res','-v7.3');
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end
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end
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end;
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% -----------------------------------------------------------------------
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%--------------------------------------------------------------------------
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%% The next three functions are for computing the initial partition
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% according to the distance between the individuals
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function initial_partition=admixture_initialization(nclusters,Z)
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T=cluster_own(Z,nclusters);
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initial_partition=T;
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function T = cluster_own(Z,nclust)
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% true=logical(1);
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% false=logical(0);
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maxclust = nclust;
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% Start of algorithm
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m = size(Z,1)+1;
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T = zeros(m,1);
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% maximum number of clusters based on inconsistency
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if m <= maxclust
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T = (1:m)';
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elseif maxclust==1
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T = ones(m,1);
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else
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clsnum = 1;
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for k = (m-maxclust+1):(m-1)
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i = Z(k,1); % left tree
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if i <= m % original node, no leafs
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T(i) = clsnum;
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clsnum = clsnum + 1;
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elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
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T = clusternum(Z, T, i-m, clsnum);
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clsnum = clsnum + 1;
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end
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i = Z(k,2); % right tree
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if i <= m % original node, no leafs
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T(i) = clsnum;
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clsnum = clsnum + 1;
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elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
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T = clusternum(Z, T, i-m, clsnum);
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clsnum = clsnum + 1;
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end
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end
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end
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function T = clusternum(X, T, k, c)
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m = size(X,1)+1;
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while(~isempty(k))
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% Get the children of nodes at this level
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children = X(k,1:2);
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children = children(:);
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% Assign this node number to leaf children
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t = (children<=m);
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T(children(t)) = c;
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% Move to next level
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k = children(~t) - m;
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end
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%--------------------------------------------------------------------------
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function Z = computeLinkage(Y, method)
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[k, n] = size(Y);
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m = (1+sqrt(1+8*n))/2;
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if k ~= 1 || m ~= fix(m)
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error('The first input has to match the output of the PDIST function in size.');
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end
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if nargin == 1 % set default switch to be 'co'
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method = 'co';
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end
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method = lower(method(1:2)); % simplify the switch string.
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% monotonic = 1;
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Z = zeros(m-1,3); % allocate the output matrix.
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N = zeros(1,2*m-1);
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N(1:m) = 1;
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n = m; % since m is changing, we need to save m in n.
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R = 1:n;
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for s = 1:(n-1)
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X = Y;
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[v, k] = min(X);
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i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
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j = k - (i-1)*(m-i/2)+i;
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Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
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I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
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U = [I1 I2 I3];
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I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
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J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
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switch method
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case 'si' %single linkage
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Y(I) = min(Y(I),Y(J));
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case 'av' % average linkage
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Y(I) = Y(I) + Y(J);
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case 'co' %complete linkage
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Y(I) = max(Y(I),Y(J));
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case 'ce' % centroid linkage
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K = N(R(i))+N(R(j));
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Y(I) = (N(R(i)).*Y(I)+N(R(j)).*Y(J)-(N(R(i)).*N(R(j))*v^2)./K)./K;
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case 'wa'
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Y(I) = ((N(R(U))+N(R(i))).*Y(I) + (N(R(U))+N(R(j))).*Y(J) - ...
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N(R(U))*v)./(N(R(i))+N(R(j))+N(R(U)));
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end
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J = [J i*(m-(i+1)/2)-m+j];
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Y(J) = []; % no need for the cluster information about j.
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% update m, N, R
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m = m-1;
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N(n+s) = N(R(i)) + N(R(j));
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R(i) = n+s;
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R(j:(n-1))=R((j+1):n);
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end
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%--------------------------------------------------------------------------
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%-------------------------------------------------------------------------
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function [Z, dist] = newGetDistances(data, rowsFromInd)
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ninds = max(data(:,end));
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nloci = size(data,2)-1;
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riviLkm = nchoosek(double(ninds),2);
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% empties = find(data<0);
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% data(empties)=0;
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data(logical(data<0)) = 0;
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data = uint16(data);
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pariTaulu = zeros(riviLkm,2);
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aPointer=1;
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for a=1:ninds-1
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pariTaulu(aPointer:aPointer+double(ninds-1-a),1) = ones(ninds-a,1,'uint16')*a;
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pariTaulu(aPointer:aPointer+double(ninds-1-a),2) = uint16((a+1:ninds)');
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aPointer = aPointer+double(ninds-a);
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end
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eka = pariTaulu(:,ones(1,rowsFromInd));
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eka = eka * rowsFromInd;
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miinus = repmat(rowsFromInd-1 : -1 : 0, [riviLkm 1]);
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eka = eka - miinus;
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toka = pariTaulu(:,ones(1,rowsFromInd)*2);
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toka = toka * rowsFromInd;
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toka = toka - miinus;
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eka = uint16(eka);
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toka = uint16(toka);
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clear pariTaulu; clear miinus;
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summa = uint16(zeros(riviLkm,1));
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vertailuja = uint16(zeros(riviLkm,1));
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x = zeros(size(eka)); x = uint16(x);
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y = zeros(size(toka)); y = uint16(y);
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% fprintf(1,'%%10');
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for j=1:nloci;
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for k=1:rowsFromInd
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x(:,k) = data(eka(:,k),j);
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y(:,k) = data(toka(:,k),j);
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end
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for a=1:rowsFromInd
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for b=1:rowsFromInd
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vertailutNyt = uint16(x(:,a)>0 & y(:,b)>0);
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vertailuja = vertailuja + vertailutNyt;
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lisays = (x(:,a)~=y(:,b) & vertailutNyt);
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summa = summa + uint16(lisays);
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end
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end
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% fprintf(1,'\b\b');
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% fprintf(1,'%d',floor(10+80*j/nloci));
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end
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clear x; clear y; clear vertailutNyt;
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clear eka; clear toka; clear data; clear lisays;
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dist = zeros(length(vertailuja),1);
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% nollat = find(vertailuja==0);
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% dist(nollat) = 1;
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dist(logical(vertailuja==0)) = 1;
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muut = find(vertailuja>0);
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dist(muut) = double(summa(muut))./double(vertailuja(muut));
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clear summa; clear vertailuja; clear muut;
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Z = computeLinkage(dist');
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% fprintf(1,'\b\b');
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% fprintf(1,'%d\n',100);
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%--------------------------------------------------------------------------
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function writeMixtureInfo(c)
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outputFile = 'baps5_semi_output.txt';
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% output the semi-supervised clustering results to the outputFile
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% modified by Lu Cheng, 28.03.2010
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ninds = length(c.PARTITION);
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npops = c.npops;
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popnames = c.popnames;
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logml = c.logml;
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partition = c.PARTITION;
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partitionSummary = c.partitionSummary;
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if ~isempty(outputFile)
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fid = fopen(outputFile,'w');
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else
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fid = -1;
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%diary('baps5_semi_output.baps'); % save in text anyway.
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end
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dispLine;
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disp('RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:');
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disp(['Number of clustered individuals: ' ownNum2Str(ninds)]);
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disp(['Number of groups in optimal partition: ' ownNum2Str(npops)]);
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disp(['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
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disp(' ');
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if (fid ~= -1)
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fprintf(fid,'%10s\n', ['RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:']);
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fprintf(fid,'%20s\n', ['Number of clustered individuals: ' ownNum2Str(ninds)]);
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fprintf(fid,'%20s\n', ['Number of groups in optimal partition: ' ownNum2Str(npops)]);
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fprintf(fid,'%20s\n\n', ['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
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end
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disp('Best Partition: ');
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if (fid ~= -1)
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fprintf(fid,'%s \n','Best Partition: ');
|
||||
end
|
||||
for m=1:npops
|
||||
indsInM = find(partition==m);
|
||||
|
||||
if isempty(indsInM)
|
||||
continue;
|
||||
end
|
||||
|
||||
length_of_beginning = 11 + floor(log10(m));
|
||||
cluster_size = length(indsInM);
|
||||
|
||||
text = ['Cluster ' num2str(m) ': {' char(popnames{indsInM(1)})];
|
||||
for k = 2:cluster_size
|
||||
text = [text ', ' char(popnames{indsInM(k)})];
|
||||
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);
|
||||
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);
|
||||
end
|
||||
end;
|
||||
end
|
||||
|
||||
names = true;
|
||||
|
||||
clusterProbTable = c.clusterProbTable;
|
||||
if npops == 1
|
||||
clusterProbTable = [];
|
||||
else
|
||||
disp('');
|
||||
disp('Posterior probability of assignment into clusters:');
|
||||
|
||||
if (fid ~= -1)
|
||||
fprintf(fid, '%s \n', ' '); fprintf(fid, '\n');
|
||||
fprintf(fid, '%s \n', 'Posterior probability of assignment into clusters: '); fprintf(fid, '\n');
|
||||
end
|
||||
|
||||
text = sprintf('%10s','ind');
|
||||
for ii = 1:npops
|
||||
tmpstr = sprintf('%10s',num2str(ii));
|
||||
text = [text tmpstr];
|
||||
end
|
||||
|
||||
disp(text);
|
||||
if (fid ~= -1)
|
||||
fprintf(fid, '%s \n', text);
|
||||
end
|
||||
|
||||
for ii = 1:ninds
|
||||
text = sprintf('%10s',popnames{ii}{:});
|
||||
for jj = 1:npops
|
||||
tmpstr = sprintf('%10s',num2str(clusterProbTable(ii,jj),'%10.6f'));
|
||||
text = [text tmpstr];
|
||||
end
|
||||
|
||||
if ii<100
|
||||
disp(text);
|
||||
elseif ii==101
|
||||
disp('.......................................');
|
||||
disp('..........see output file..............');
|
||||
end
|
||||
if (fid ~= -1)
|
||||
fprintf(fid, '%s \n', text);
|
||||
end
|
||||
text = [];
|
||||
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\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(logical(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),'%20d') ' ' num2str(partitionSummary(part,2),'%20.6f')];
|
||||
disp(line);
|
||||
if (fid ~= -1)
|
||||
fprintf(fid, '%s \n', line);
|
||||
end
|
||||
end
|
||||
|
||||
if (fid ~= -1)
|
||||
fclose(fid);
|
||||
else
|
||||
diary off
|
||||
end
|
||||
|
||||
%--------------------------------------------------------------
|
||||
function newline = takeLine(description,width)
|
||||
%Returns one line from the description: line ends to the first
|
||||
%space after width:th mark.
|
||||
% newLine = description(1:width);
|
||||
n = width+1;
|
||||
while ~isspace(description(n)) && n<length(description)
|
||||
n = n+1;
|
||||
end;
|
||||
newline = description(1:n);
|
||||
|
||||
|
||||
function dispLine
|
||||
disp('---------------------------------------------------');
|
||||
|
||||
function num2 = omaRound(num)
|
||||
% Py�rist�� luvun num 1 desimaalin tarkkuuteen
|
||||
num = num*10;
|
||||
num = round(num);
|
||||
num2 = num/10;
|
||||
|
||||
%---------------------------------------------------------
|
||||
|
||||
%-------------------------------------------------------------------------
|
||||
|
||||
function [newData, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = ...
|
||||
handleData(raw_data)
|
||||
% Alkuper�isen datan viimeinen sarake kertoo, milt�yksil�lt�
|
||||
% kyseinen rivi on per�isin. Funktio tutkii ensin, ett�montako
|
||||
% rivi�maksimissaan on per�isin yhdelt�yksil�lt� jolloin saadaan
|
||||
% tiet�� onko kyseess�haploidi, diploidi jne... T�m�n j�lkeen funktio
|
||||
% lis�� tyhji�rivej�niille yksil�ille, joilta on per�isin v�hemm�n
|
||||
% rivej�kuin maksimim��r�
|
||||
% Mik�li jonkin alleelin koodi on =0, funktio muuttaa t�m�n alleelin
|
||||
% koodi pienimm�ksi koodiksi, joka isompi kuin mik��n k�yt�ss�oleva koodi.
|
||||
% T�m�n j�lkeen funktio muuttaa alleelikoodit siten, ett�yhden lokuksen j
|
||||
% koodit saavat arvoja v�lill�1,...,noalle(j).
|
||||
|
||||
data = raw_data;
|
||||
nloci=size(raw_data,2)-1;
|
||||
|
||||
dataApu = data(:,1:nloci);
|
||||
nollat = find(dataApu==0);
|
||||
if ~isempty(nollat)
|
||||
isoinAlleeli = max(max(dataApu));
|
||||
dataApu(nollat) = isoinAlleeli+1;
|
||||
data(:,1:nloci) = dataApu;
|
||||
end
|
||||
% dataApu = [];
|
||||
% nollat = [];
|
||||
% isoinAlleeli = [];
|
||||
|
||||
noalle=zeros(1,nloci);
|
||||
alleelitLokuksessa = cell(nloci,1);
|
||||
for i=1:nloci
|
||||
alleelitLokuksessaI = unique(data(:,i));
|
||||
%alleelitLokuksessa{i,1} = alleelitLokuksessaI(find(alleelitLokuksessaI>=0));
|
||||
alleelitLokuksessa{i,1} = alleelitLokuksessaI(logical(alleelitLokuksessaI>=0));
|
||||
noalle(i) = length(alleelitLokuksessa{i,1});
|
||||
end
|
||||
alleleCodes = zeros(max(noalle),nloci);
|
||||
for i=1:nloci
|
||||
alleelitLokuksessaI = alleelitLokuksessa{i,1};
|
||||
puuttuvia = max(noalle)-length(alleelitLokuksessaI);
|
||||
alleleCodes(:,i) = [alleelitLokuksessaI; zeros(puuttuvia,1)];
|
||||
end
|
||||
|
||||
for loc = 1:nloci
|
||||
for all = 1:noalle(loc)
|
||||
% data(find(data(:,loc)==alleleCodes(all,loc)), loc)=all;
|
||||
data(logical(data(:,loc)==alleleCodes(all,loc)), loc)=all;
|
||||
end;
|
||||
end;
|
||||
|
||||
nind = max(data(:,end));
|
||||
nrows = size(data,1);
|
||||
ncols = size(data,2);
|
||||
rowsFromInd = zeros(nind,1);
|
||||
for i=1:nind
|
||||
rowsFromInd(i) = length(find(data(:,end)==i));
|
||||
end
|
||||
maxRowsFromInd = max(rowsFromInd);
|
||||
a = -999;
|
||||
emptyRow = repmat(a, 1, ncols);
|
||||
lessThanMax = find(rowsFromInd < maxRowsFromInd);
|
||||
missingRows = maxRowsFromInd*nind - nrows;
|
||||
data = [data; zeros(missingRows, ncols)];
|
||||
pointer = 1;
|
||||
for ind=lessThanMax' %K�y l�pi ne yksil�t, joilta puuttuu rivej�
|
||||
miss = maxRowsFromInd-rowsFromInd(ind); % T�lt�yksil�lt�puuttuvien lkm.
|
||||
for j=1:miss
|
||||
rowToBeAdded = emptyRow;
|
||||
rowToBeAdded(end) = ind;
|
||||
data(nrows+pointer, :) = rowToBeAdded;
|
||||
pointer = pointer+1;
|
||||
end
|
||||
end
|
||||
data = sortrows(data, ncols); % Sorttaa yksil�iden mukaisesti
|
||||
newData = data;
|
||||
rowsFromInd = maxRowsFromInd;
|
||||
|
||||
adjprior = zeros(max(noalle),nloci);
|
||||
priorTerm = 0;
|
||||
for j=1:nloci
|
||||
adjprior(:,j) = [repmat(1/noalle(j), [noalle(j),1]) ; ones(max(noalle)-noalle(j),1)];
|
||||
priorTerm = priorTerm + noalle(j)*gammaln(1/noalle(j));
|
||||
end
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
Loading…
Add table
Add a link
Reference in a new issue