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TmrClnTLtest_v4_mcg.m
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TmrClnTLtest_v4_mcg.m
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clc; clear; %close all
DATAPATH = sprintf('C:\\Users\\%s\\', getenv('username'));
DATAPATH = {[DATAPATH, 'Dropbox (Texas Tech)\#DataStorageSRD\METABRIC_cBioPortal\brca_metabric\'];
[DATAPATH, 'Dropbox (Texas Tech)\#DataStorageSRD\CCLE_cBioPortal\cellline_ccle_broad\'];
[DATAPATH, 'Google Drive\Data CCLE and GDSC\GDSC\Version 7\']};
FILENAME = {'data_expression_metabric.txt'; 'data_expression_median_ccle.txt';...
'gdsc_v7_gene_expression_processed_SRD_09_27_2018.xlsx'};
EmptyDataFlag = {'.', 'NA', 'N/A'};
%% Data Extraction...
%%% METABRIC...
dataTableGX1 = readtable([DATAPATH{1}, FILENAME{1}], 'TreatAsEmpty', EmptyDataFlag);
GX1 = dataTableGX1{:, 3:end}'; genes1 = dataTableGX1(:, 1:2);
samples1 = dataTableGX1.Properties.VariableNames(3:end)';
[~, ind11] = sort(lower(genes1.Hugo_Symbol)); [~, ind12] = sort(lower(samples1));
genes1 = genes1(ind11, :); GX1 = GX1(:, ind11);
samples1 = samples1(ind12); GX1 = GX1(ind12, :);
%%% CCLE (vNew)...
dataTableGX2 = readtable([DATAPATH{2}, FILENAME{2}], 'TreatAsEmpty', EmptyDataFlag);
dataGX2 = dataTableGX2{:, 3:end}'; genes2 = dataTableGX2(:, 1:2);
CL_list2 = dataTableGX2.Properties.VariableNames(3:end)';
CL2 = cell(numel(CL_list2), 2); % CL table
for k = 1:numel(CL_list2)
C = split(CL_list2(k), '_');
CL2(k, :) = [C(1), join(C(2:end), ' ')];
end
CL2 = array2table(CL2, 'variablenames', {'CL_Name', 'Organ'});
[~, ind2] = sort(lower(CL2.Organ)); CL2 = CL2(ind2, :); dataGX2 = dataGX2(ind2, :);
% BRCA data...
brCLidx2 = strcmpi(CL2.Organ, 'breast');
brCL2 = CL2{brCLidx2, 1}; GX2 = dataGX2(brCLidx2, :);
[~, ind21] = sort(lower(genes2.Hugo_Symbol)); [~, ind22] = sort(lower(brCL2));
genes2 = genes2(ind21, :); GX2 = GX2(:, ind21);
brCL2 = brCL2(ind22); GX2 = GX2(ind22, :);
% All CL data...
CL2tot = CL2.CL_Name; GX2tot = dataGX2(:, ind21);
%%% GDSC (v7)...
dataTableGX3 = readtable([DATAPATH{3}, FILENAME{3}], 'TreatAsEmpty', EmptyDataFlag);
dataGX3 = dataTableGX3{:, 2:end}'; genes3 = dataTableGX3{:, 1};
CL_list3 = dataTableGX3.Properties.VariableNames(2:end)';
CL3 = cell(numel(CL_list3), 2); % CL table
for k = 1:numel(CL_list3)
C = split(CL_list3(k), '_');
CL3(k, :) = [C(1), join(C(2:end), ' ')];
end
CL3 = array2table(CL3, 'variablenames', {'CL_Name', 'Organ'});
[~, ind3] = sort(lower(CL3.Organ)); CL3 = CL3(ind3, :); dataGX3 = dataGX3(ind3, :);
% BRCA data...
brCLidx3 = strcmpi(CL3.Organ, 'breast');
brCL3 = CL3{brCLidx3, 1}; GX3 = dataGX3(brCLidx3, :);
[~, ind31] = sort(lower(genes3)); [~, ind32] = sort(lower(brCL3));
genes3 = genes3(ind31, :); GX3 = GX3(:, ind31);
brCL3 = brCL3(ind32); GX3 = GX3(ind32, :);
% All CL data...
CL3tot = CL3.CL_Name; GX3tot = dataGX3(:, ind31);
%% Matched data...
matGN = [ ]; [~, matGN(:, 1), matGN(:, 2), matGN(:, 3)] =...
intersectn(lower(genes1.Hugo_Symbol), lower(genes2.Hugo_Symbol), lower(genes3), 'stable');
% samples1, brCL2, brCL3
geneSet = genes1(matGN(:, 1), :); GXset1 = GX1(:, matGN(:, 1));
GXset2tot = GX2tot(:, matGN(:, 2)); GXset2 = GX2(:, matGN(:, 2));
GXset3tot = GX3tot(:, matGN(:, 3)); GXset3 = GX3(:, matGN(:, 3));
% Missing value imputation...
Knn = 10;
if sum(isnan(GXset1(:))) > 0, GXset1 = knnimpute(GXset1', Knn)'; end
if sum(isnan(GXset2(:))) > 0, GXset2 = knnimpute(GXset2', Knn)'; end
if sum(isnan(GXset3(:))) > 0, GXset3 = knnimpute(GXset3', Knn)'; end
if sum(isnan(GXset2tot(:))) > 0, GXset2tot = knnimpute(GXset2tot', Knn)'; end
if sum(isnan(GXset3tot(:))) > 0, GXset3tot = knnimpute(GXset3tot', Knn)'; end
% % Standardization (z-score)...
% GXset1 = zscore(GXset1, [ ], 1);
% GXset2 = zscore(GXset2, [ ], 1); GXset2tot = zscore(GXset2tot, [ ], 1);
% GXset3 = zscore(GXset3, [ ], 1); GXset3tot = zscore(GXset3tot, [ ], 1);
% Finding biomarkers in data...
bmChoice = 2;
switch bmChoice
case 1
biomarkerList = importdata('Biomarker_list.csv');
[~, ind] = intersect(lower(geneSet.Hugo_Symbol), lower(biomarkerList), 'stable');
case 2
biomarkerList = readtable('Biomarker_list2.csv');
[~, ind] = intersect(lower(geneSet.Hugo_Symbol), lower(biomarkerList.Symbol), 'stable');
end
bmIdx = false(numel(geneSet.Hugo_Symbol), 1); bmIdx(ind) = true;
biomarkers = geneSet.Hugo_Symbol(bmIdx); XgeneSet = geneSet(~bmIdx, :);
% Biomarker expression as response...
Xdata1 = GXset1(:, ~bmIdx); Ydata1 = GXset1(:, bmIdx);
Xdata2 = GXset2(:, ~bmIdx); Ydata2 = GXset2(:, bmIdx);
Xdata3 = GXset3(:, ~bmIdx); Ydata3 = GXset3(:, bmIdx);
Xdata2tot = GXset2tot(:, ~bmIdx); Ydata2tot = GXset2tot(:, bmIdx);
Xdata3tot = GXset3tot(:, ~bmIdx); Ydata3tot = GXset3tot(:, bmIdx);
p = sum(~bmIdx); q = sum(bmIdx);
n1 = numel(samples1);
n2 = numel(brCL2); n2tot = numel(CL2tot);
n3 = numel(brCL3); n3tot = numel(CL3tot);
% Standardization (z-score)...
GXset11 = zscore(GXset1, [ ], 1);
GXset22 = zscore(GXset2, [ ], 1); GXset22tot = zscore(GXset2tot, [ ], 1);
GXset33 = zscore(GXset3, [ ], 1); GXset33tot = zscore(GXset3tot, [ ], 1);
Xdata11 = zscore(Xdata1, [ ], 1); Ydata11 = zscore(Ydata1, [ ], 1);
Xdata22 = zscore(Xdata2, [ ], 1); Ydata22 = zscore(Ydata2, [ ], 1);
Xdata33 = zscore(Xdata3, [ ], 1); Ydata33 = zscore(Ydata3, [ ], 1);
Xdata22tot = zscore(Xdata2tot, [ ], 1); Ydata22tot = zscore(Ydata2tot, [ ], 1);
Xdata33tot = zscore(Xdata3tot, [ ], 1); Ydata33tot = zscore(Ydata3tot, [ ], 1);
%%% Distributions...
numBin = 50;
% Biomarkers...
hb = [figure(2), figure(3)]; nPts = 1e3; clf(figure(hb(1))); clf(figure(hb(2)));
mbox = [1:q/2, 3*q/2+1:2*q; q/2+1:q, 2*q+1:5*q/2; q+1:3*q/2, 5*q/2+1:3*q];
for k = 1 : q
[Ypdf1, pts1] = ksdensity(Ydata11(:, k), 'NumPoints', nPts);
[Ypdf2, pts2] = ksdensity(Ydata22(:, k), 'NumPoints', nPts);
[Ypdf3, pts3] = ksdensity(Ydata33(:, k), 'NumPoints', nPts);
[Ypdf2tot, pts2tot] = ksdensity(Ydata22tot(:, k), 'NumPoints', nPts);
[Ypdf3tot, pts3tot] = ksdensity(Ydata33tot(:, k), 'NumPoints', nPts);
figure(hb(1)) % BRCA
subplot(6,q/2,mbox(1,k)), histogram(Ydata11(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts1, Ypdf1, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0.5,0,0}METABRIC'])
subplot(6,q/2,mbox(2,k)), histogram(Ydata22(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts2, Ypdf2, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0.5,0}CCLE'])
subplot(6,q/2,mbox(3,k)), histogram(Ydata33(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts3, Ypdf3, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0,0.5}GDSC'])
figure(hb(2)) % All CLs
subplot(6,q/2,mbox(1,k)), histogram(Ydata11(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts1, Ypdf1, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0.5,0,0}METABRIC'])
subplot(6,q/2,mbox(2,k)), histogram(Ydata22tot(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts2tot, Ypdf2tot, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0.5,0}CCLE'])
subplot(6,q/2,mbox(3,k)), histogram(Ydata33tot(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts3tot, Ypdf3tot, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0,0.5}GDSC'])
end
defn = ['Row 1, 3 - {\color[rgb]{0,0,0.8}METABRIC}, Row 2, 4 - {\color[rgb]{0,0,0.8}CCLE}, ',...
'Row 3, 6 - {\color[rgb]{0,0,0.8}GDSC}'];
figure(hb(1)), suptitle([{'\bfBiomarker Distributions for {\color[rgb]{0.5,0,0}BRCA} CLs'}; {defn}])
figure(hb(2)), suptitle([{'\bfBiomarker Distributions for {\color[rgb]{0.5,0,0}All} CLs'}; {defn}])
%% Save data...
XdataTable1 = array2table(Xdata1, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', samples1);
XdataTable2 = array2table(Xdata2, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', brCL2);
XdataTable3 = array2table(Xdata3, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', brCL3);
YdataTable1 = array2table(Ydata1, 'VariableNames', biomarkers, 'RowNames', samples1);
YdataTable2 = array2table(Ydata2, 'VariableNames', biomarkers, 'RowNames', brCL2);
YdataTable3 = array2table(Ydata3, 'VariableNames', biomarkers, 'RowNames', brCL3);
SavePath = '.\\DataProcessed\\BRCA_MCG\\';
writetable(XdataTable1, sprintf([SavePath, 'BRCA_gene_expression_METABRIC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(XdataTable2, sprintf([SavePath, 'BRCA_gene_expression_CCLE_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(XdataTable3, sprintf([SavePath, 'BRCA_gene_expression_GDSC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable1, sprintf([SavePath, 'BRCA_biomarker_expression_METABRIC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable2, sprintf([SavePath, 'BRCA_biomarker_expression_CCLE_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable3, sprintf([SavePath, 'BRCA_biomarker_expression_GDSC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
%% Housekeeping Gene Data...
HKGtab = readtable('HKG_list.csv');
nHKG = HKGtab.nHKG; nHKG(cellfun(@isempty, nHKG)) = [ ];
tHKG = HKGtab.tHKG; tHKG(cellfun(@isempty, tHKG)) = [ ];
HKGlist = {nHKG, tHKG, [nHKG; tHKG]};
[~, nHKGidx] = intersect(lower(XgeneSet.Hugo_Symbol), lower(nHKG), 'stable');
[~, tHKGidx] = intersect(lower(XgeneSet.Hugo_Symbol), lower(tHKG), 'stable');
HKGidx = {nHKGidx, tHKGidx}; pHKG = [numel(HKGidx{1}), numel(HKGidx{2})]; % #HKGs
HKGset = {XgeneSet.Hugo_Symbol(HKGidx{1}), XgeneSet.Hugo_Symbol(HKGidx{2})};
% Distributions...
numBin = 50; nPts = 1e3;
defn = {'nHKG', 'tHKG', ['Row 1 - {\color[rgb]{0,0,0.8}METABRIC}, ',...
'Row 2 - {\color[rgb]{0,0,0.8}CCLE: BRCA}, Row 3 - {\color[rgb]{0,0,0.8}GDSC: BRCA}']};
hh = [figure(4), figure(5)]; clf(figure(hh(1))); clf(figure(hh(2)));
for i = 1 : 2
figure(hh(i))
for k = 1 : pHKG(i)
[Xpdf1, pts1] = ksdensity(GXset11(:, HKGidx{i}(k)), 'NumPoints', nPts);
[Xpdf2, pts2] = ksdensity(GXset22(:, HKGidx{i}(k)), 'NumPoints', nPts);
[Xpdf3, pts3] = ksdensity(GXset33(:, HKGidx{i}(k)), 'NumPoints', nPts);
subplot(3,7,k), histogram(GXset11(:, HKGidx{i}(k)), numBin, 'normalization', 'pdf')
hold on, plot(pts1, Xpdf1, 'r-.', 'linewidth', 2), hold off
title([HKGset{i}{k}, ' - \color[rgb]{0.5,0,0}METABRIC'])
subplot(3,7,k+7), histogram(GXset22(:, HKGidx{i}(k)), numBin, 'normalization', 'pdf')
hold on, plot(pts2, Xpdf2, 'r-.', 'linewidth', 2), hold off
title([HKGset{i}{k}, ' - \color[rgb]{0,0.5,0}CCLE: BRCA'])
subplot(3,7,k+14), histogram(GXset33(:, HKGidx{i}(k)), numBin, 'normalization', 'pdf')
hold on, plot(pts3, Xpdf3, 'r-.', 'linewidth', 2), hold off
title([HKGset{i}{k}, ' - \color[rgb]{0,0,0.5}GDSC: BRCA'])
end
suptitle([{['\bfHousekeeping Gene Distributions - {\color[rgb]{0.5,0,0}', defn{i}, '}']; defn{3}}])
end
%% Pairwise Dependency...
seed = 0; rng(seed)
% Non-biomarker data...
repSwitch = 2;
switch repSwitch
case 1
fprintf(1, 'Chosen non-biomarkers = '), fprintf(2, 'Random regular genes\n')
gnIdx = randperm(p, q); % Random representatives, # = q
case 2
hkSwitch = 3; hkChoice = {'nHKG', 'tHKG', 'All'};
fprintf(1, 'Chosen non-biomarkers = '), fprintf(2, '%s\n', hkChoice{hkSwitch})
housekeepingList = HKGlist{hkSwitch};
[~, gnIdx] = intersect(lower(XgeneSet.Hugo_Symbol), lower(housekeepingList), 'stable');
end
repGenes = XgeneSet.Hugo_Symbol(gnIdx); pRep = numel(repGenes);
XdataRep1 = Xdata1(:, gnIdx);
XdataRep2 = Xdata2(:, gnIdx); XdataRep2tot = Xdata2tot(:, gnIdx);
XdataRep3 = Xdata3(:, gnIdx); XdataRep3tot = Xdata3tot(:, gnIdx);
% Gene-pair combinations...
bmPairs = nchoosek(1:q, 2); nPairsBM = size(bmPairs, 1);
bmPairList = strrep(cellstr([num2str(bmPairs(:, 1)), repmat('-', [nPairsBM, 1]), num2str(bmPairs(:, 2))]), ' ', '');
gnPairs = nchoosek(1:pRep, 2); nPairsGN = size(gnPairs, 1);
gnPairList = strrep(cellstr([num2str(gnPairs(:, 1)), repmat('-', [nPairsGN, 1]), num2str(gnPairs(:, 2))]), ' ', '');
%%% Pairwise Dependency - Correlation...
% Biomarkers...
ycorrmat1 = corr(Ydata1, 'type', 'spearman'); ycorr1 = 1 - squareform(1 - ycorrmat1, 'tovector')';
ycorrmat2 = corr(Ydata2, 'type', 'spearman'); ycorr2 = 1 - squareform(1 - ycorrmat2, 'tovector')';
ycorrmat3 = corr(Ydata3, 'type', 'spearman'); ycorr3 = 1 - squareform(1 - ycorrmat3, 'tovector')';
ycorrmat2tot = corr(Ydata2tot, 'type', 'spearman'); ycorr2tot = 1 - squareform(1 - ycorrmat2tot, 'tovector')';
ycorrmat3tot = corr(Ydata3tot, 'type', 'spearman'); ycorr3tot = 1 - squareform(1 - ycorrmat3tot, 'tovector')';
ycorr_struct = [corr(ycorr1, ycorr2, 'type', 'pearson'), corr(ycorr1, ycorr2tot, 'type', 'pearson');...
corr(ycorr1, ycorr3, 'type', 'pearson'), corr(ycorr1, ycorr3tot, 'type', 'pearson');...
corr(ycorr2, ycorr3, 'type', 'pearson'), corr(ycorr2tot, ycorr3tot, 'type', 'pearson')];
% Non-biomarker genes...
xcorrmat1 = corr(XdataRep1, 'type', 'spearman'); xcorr1 = 1 - squareform(1 - xcorrmat1, 'tovector')';
xcorrmat2 = corr(XdataRep2, 'type', 'spearman'); xcorr2 = 1 - squareform(1 - xcorrmat2, 'tovector')';
xcorrmat3 = corr(XdataRep3, 'type', 'spearman'); xcorr3 = 1 - squareform(1 - xcorrmat3, 'tovector')';
xcorrmat2tot = corr(XdataRep2tot, 'type', 'spearman'); xcorr2tot = 1 - squareform(1 - xcorrmat2tot, 'tovector')';
xcorrmat3tot = corr(XdataRep3tot, 'type', 'spearman'); xcorr3tot = 1 - squareform(1 - xcorrmat3tot, 'tovector')';
xcorr_struct = [corr(xcorr1, xcorr2, 'type', 'pearson'), corr(xcorr1, xcorr2tot, 'type', 'pearson');...
corr(xcorr1, xcorr3, 'type', 'pearson'), corr(xcorr1, xcorr3tot, 'type', 'pearson');...
corr(xcorr2, xcorr3, 'type', 'pearson'), corr(xcorr2tot, xcorr3tot, 'type', 'pearson')];
% Results...
fprintf(1, 'Correlation betn Gene-pair corr. vectors for Biomarkers & ')
switch repSwitch
case 1
fprintf(1, 'Regular Genes:\n')
case 2
fprintf(1, 'Housekeeping Genes (%s):\n', hkChoice{hkSwitch})
end
rNames = {'r_MC - BM'; 'r_MG - BM'; 'r_CG - BM'; 'r_MC'; 'r_MG'; 'r_CG'};
corr_struct = array2table(round([ycorr_struct; xcorr_struct], 4), 'rownames', rNames,...
'variablenames', {'BRCA', 'ALL'});
disp(corr_struct)
% Correlation plots...
switch repSwitch
case 1
hc = figure(7); % Regular genes
case 2
switch hkSwitch
case 1
hc = figure(8); % nHKG
case 2
hc = figure(9); % tHKG
case 3
hc = figure(10); % All HKGs
end
end
defn = {'Regular Genes'; 'Housekeeping Genes'; ' (nHKG)'; ' (tHKG)'; ''};
defn2 = {'Row 1 - For {\color[rgb]{0,0,0.8}BRCA} CLs, Row 2 - For {\color[rgb]{0,0,0.8}All} CLs'};
% Biomarkers...
figure(6), clf
subplot(241), plot(1:nPairsBM, ycorr1, 'rs--', 1:nPairsBM, ycorr2, 'gs--', 1:nPairsBM, ycorr3, 'bs--')
hold on, line([0, nPairsBM+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--')
xlim([0, nPairsBM+1]), xticksNoted = floor(linspace(1, nPairsBM, 12));
xticks(xticksNoted), xticklabels(bmPairList(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}BRCA}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(ycorr_struct(1,1), 2)), ', r_{MG} = ', num2str(round(ycorr_struct(2,1), 2)),...
', r_{CG} = ', num2str(round(ycorr_struct(3,1), 2)), '})']}])
subplot(242), scatter(ycorr1, ycorr2, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(243), scatter(ycorr1, ycorr3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(244), scatter(ycorr2, ycorr3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(245), plot(1:nPairsBM, ycorr1, 'rs--', 1:nPairsBM, ycorr2tot, 'gs--', 1:nPairsBM, ycorr3tot, 'bs--')
hold on, line([0, nPairsBM+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--')
xlim([0, nPairsBM+1]), xticksNoted = floor(linspace(1, nPairsBM, 12));
xticks(xticksNoted), xticklabels(bmPairList(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}ALL}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(ycorr_struct(1,2), 2)), ', r_{MG} = ', num2str(round(ycorr_struct(2,2), 2)),...
', r_{CG} = ', num2str(round(ycorr_struct(3,2), 2)), '})']}])
subplot(246), scatter(ycorr1, ycorr2tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(247), scatter(ycorr1, ycorr3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(248), scatter(ycorr2tot, ycorr3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
suptitle([{'\bfComparison of Gene-pair Correlations for {\color[rgb]{0.5,0,0}Biomarkers}'}; defn2])
% Non-biomarker genes...
figure(hc), clf
subplot(241), plot(1:nPairsGN, xcorr1, 'rs--', 1:nPairsGN, xcorr2, 'gs--', 1:nPairsGN, xcorr3, 'bs--')
hold on, line([0, nPairsGN+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--'), ylim([-0.4, 0.6])
xlim([0, nPairsGN+1]), xticksNoted = floor(linspace(1, nPairsGN, 12));
xticks(xticksNoted), xticklabels(gnPairList(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}BRCA}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(xcorr_struct(1,1), 2)), ', r_{MG} = ', num2str(round(xcorr_struct(2,1), 2)),...
', r_{CG} = ', num2str(round(xcorr_struct(3,1), 2)), '})']}])
subplot(242), scatter(xcorr1, xcorr2, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(243), scatter(xcorr1, xcorr3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(244), scatter(xcorr2, xcorr3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(245), plot(1:nPairsGN, xcorr1, 'rs--', 1:nPairsGN, xcorr2tot, 'gs--', 1:nPairsGN, xcorr3tot, 'bs--')
hold on, line([0, nPairsGN+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--'), ylim([-0.4, 0.6])
xlim([0, nPairsGN+1]), xticksNoted = floor(linspace(1, nPairsGN, 12));
xticks(xticksNoted), xticklabels(gnPairList(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}ALL}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(xcorr_struct(1,2), 2)), ', r_{MG} = ', num2str(round(xcorr_struct(2,2), 2)),...
', r_{CG} = ', num2str(round(xcorr_struct(3,2), 2)), '})']}])
subplot(246), scatter(xcorr1, xcorr2tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(247), scatter(xcorr1, xcorr3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(248), scatter(xcorr2tot, xcorr3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
if repSwitch == 1
suptitle([{['\bfComparison of Gene-pair Correlations for {\color[rgb]{0.5,0,0}', defn{repSwitch}, '}']}; defn2])
elseif repSwitch == 2
suptitle([{['\bfComparison of Gene-pair Correlations for {\color[rgb]{0.5,0,0}', defn{repSwitch},...
defn{hkSwitch+2}, '}']}; defn2])
end
%% Correlation Heatmaps...
% Unaltered Corr. Matrix Heatmaps...
ColMap = jet;
figure(11), clf
subplot(221), heatmap(ycorrmat1, 'XData', biomarkers, 'YData', biomarkers, 'Colormap', ColMap);
colorbar off, title('METABRIC')
subplot(222), heatmap(ycorrmat2, 'XData', biomarkers, 'YData', biomarkers, 'Colormap', ColMap);
title('CCLE: BRCA')
subplot(224), heatmap(ycorrmat3, 'XData', biomarkers, 'YData', biomarkers, 'Colormap', ColMap);
title('GDSC: BRCA')
suptitle('\bfBiomarker Correlation Structure')
% Hierarchical clustering...
ydistmat1 = squareform(pdist(Ydata1', 'euclidean')); yTree1 = linkage(ydistmat1, 'average');
ydistmat2 = squareform(pdist(Ydata2', 'euclidean')); yTree2 = linkage(ydistmat2, 'average');
ydistmat3 = squareform(pdist(Ydata3', 'euclidean')); yTree3 = linkage(ydistmat3, 'average');
ydistmat2tot = squareform(pdist(Ydata2tot', 'euclidean')); yTree2tot = linkage(ydistmat2tot, 'average');
ydistmat3tot = squareform(pdist(Ydata3tot', 'euclidean')); yTree3tot = linkage(ydistmat3tot, 'average');
nCls = 3; % #clusters
IdxCls1 = cluster(yTree1, 'maxclust', nCls);
IdxCls2 = cluster(yTree2, 'maxclust', nCls); IdxCls2tot = cluster(yTree2tot, 'maxclust', nCls);
IdxCls3 = cluster(yTree3, 'maxclust', nCls); IdxCls3tot = cluster(yTree3tot, 'maxclust', nCls);
w = 1.000; TreeCut = zeros(3, 2);
TreeCut(1, 1) = w * yTree1((q - nCls + 1), 3);
TreeCut(2, :) = [w * yTree2((q - nCls + 1), 3), w * yTree2tot((q - nCls + 1), 3)];
TreeCut(3, :) = [w * yTree3((q - nCls + 1), 3), w * yTree3tot((q - nCls + 1), 3)];
% TreeCut
% Cluster tree plot w/ cut-off...
figure(12), clf
subplot(231), ht1 = dendrogram(yTree1); set(ht1, 'color', 'k'), box on
hold on, line([0, q+1], [TreeCut(1, 1), TreeCut(1, 1)], 'linestyle', '--', 'linewidth', 1.2, 'color', 'r')
xticklabels(biomarkers(xticks)), xtickangle(45), ylabel('\bfLinkage Distance'), title(['METABRIC'])
subplot(232), ht2 = dendrogram(yTree2); set(ht2, 'color', 'k'), box on
hold on, line([0, q+1], [TreeCut(2, 1), TreeCut(2, 1)], 'linestyle', '--', 'linewidth', 1.2, 'color', 'r')
xticklabels(biomarkers(xticks)), xtickangle(45), ylabel('\bfLinkage Distance'), title(['CCLE: BRCA'])
subplot(233), ht3 = dendrogram(yTree3); set(ht3, 'color', 'k'), box on
hold on, line([0, q+1], [TreeCut(3, 1), TreeCut(3, 1)], 'linestyle', '--', 'linewidth', 1.2, 'color', 'r')
xticklabels(biomarkers(xticks)), xtickangle(45), ylabel('\bfLinkage Distance'), title(['GDSC: BRCA'])
% Sorted clusters w/ corresponding corr. matrices...
bmSorted = cell(q, 5); [~, ind1] = sort(IdxCls1);
[~, ind2] = sort(IdxCls2); [~, ind2tot] = sort(IdxCls2tot);
[~, ind3] = sort(IdxCls3); [~, ind3tot] = sort(IdxCls3tot);
bmSorted(:, 1) = biomarkers(ind2); YdataSorted1 = Ydata1(:, ind2);
bmSorted(:, 2) = biomarkers(ind2); YdataSorted2 = Ydata2(:, ind2);
bmSorted(:, 3) = biomarkers(ind2); YdataSorted3 = Ydata3(:, ind2);
bmSorted(:, 4) = biomarkers(ind2); YdataSorted2tot = Ydata2tot(:, ind2);
bmSorted(:, 5) = biomarkers(ind2); YdataSorted3tot = Ydata3tot(:, ind2);
ys_cmat1 = corr(YdataSorted1, 'type', 'spearman'); ys_corr1 = 1 - squareform(1 - ys_cmat1, 'tovector')';
ys_cmat2 = corr(YdataSorted2, 'type', 'spearman'); ys_corr2 = 1 - squareform(1 - ys_cmat2, 'tovector')';
ys_cmat3 = corr(YdataSorted3, 'type', 'spearman'); ys_corr3 = 1 - squareform(1 - ys_cmat3, 'tovector')';
ys_cmat2tot = corr(YdataSorted2tot, 'type', 'spearman'); ys_corr2tot = 1 - squareform(1 - ys_cmat2tot, 'tovector')';
ys_cmat3tot = corr(YdataSorted3tot, 'type', 'spearman'); ys_corr3tot = 1 - squareform(1 - ys_cmat3tot, 'tovector')';
ys_corr_struct = [corr(ys_corr1, ys_corr2, 'type', 'pearson'), corr(ys_corr1, ys_corr2tot, 'type', 'pearson');...
corr(ys_corr1, ys_corr3, 'type', 'pearson'), corr(ys_corr1, ys_corr3tot, 'type', 'pearson');...
corr(ys_corr2, ys_corr3, 'type', 'pearson'), corr(ys_corr2tot, ys_corr3tot, 'type', 'pearson')];
% New heatmaps...
subplot(234), heatmap(ys_cmat1, 'XData', bmSorted(:, 1), 'YData', bmSorted(:, 1), 'Colormap', ColMap);
colorbar off, title('METABRIC')
subplot(235), heatmap(ys_cmat2, 'XData', bmSorted(:, 2), 'YData', bmSorted(:, 2), 'Colormap', ColMap);
colorbar off, title('CCLE: BRCA')
subplot(236), heatmap(ys_cmat3, 'XData', bmSorted(:, 3), 'YData', bmSorted(:, 3), 'Colormap', ColMap);
title('GDSC: BRCA')
defn = [sprintf('Row 1 - {\\color[rgb]{0,0,0.8}Hierarchical Clustering (K = %d)}, ', nCls),...
'Row 2 - {\color[rgb]{0,0,0.8}Correlation Heatmap}'];
suptitle([{'\bfBiomarker Correlation Structure'}; {defn}])
%% Distribution Smoothing w/ Spline Fitting & Kernel Smoothing Density Estimate...
% Ydata1, Ydata2, Ydata2tot
% Cubic spline fitting...
samp0 = {linspace(0, 1, n1)', linspace(0, 1, n2)', linspace(0, 1, n3)', linspace(0, 1, n2tot)', linspace(0, 1, n3tot)'};
nPts = n1; samp1 = linspace(0, 1, nPts)';
YdataFit11 = zeros(nPts, q);
YdataFit22 = zeros(nPts, q); YdataFit22tot = zeros(nPts, q);
YdataFit33 = zeros(nPts, q); YdataFit33tot = zeros(nPts, q);
for k = 1 : q
YdataFit11(:, k) = spline(samp0{1}, sort(Ydata11(:, k)), samp1);
YdataFit22(:, k) = spline(samp0{2}, sort(Ydata22(:, k)), samp1);
YdataFit33(:, k) = spline(samp0{3}, sort(Ydata33(:, k)), samp1);
YdataFit22tot(:, k) = spline(samp0{4}, sort(Ydata22tot(:, k)), samp1);
YdataFit33tot(:, k) = spline(samp0{5}, sort(Ydata33tot(:, k)), samp1);
end
% Smoothed distributions w/ kernel density overlayed...
hb = [figure(14), figure(15)]; clf(figure(hb(1))); clf(figure(hb(2)));
mbox = [1:q/2, 3*q/2+1:2*q; q/2+1:q, 2*q+1:5*q/2; q+1:3*q/2, 5*q/2+1:3*q]; nPts = 1e3;
for k = 1 : q
[Ypdf1, pts1] = ksdensity(Ydata11(:, k), 'NumPoints', nPts);
[Ypdf2, pts2] = ksdensity(Ydata22(:, k), 'NumPoints', nPts);
[Ypdf3, pts3] = ksdensity(Ydata33(:, k), 'NumPoints', nPts);
[Ypdf2tot, pts2tot] = ksdensity(Ydata22tot(:, k), 'NumPoints', nPts);
[Ypdf3tot, pts3tot] = ksdensity(Ydata33tot(:, k), 'NumPoints', nPts);
figure(hb(1)) % BRCA
subplot(6,q/2,mbox(1,k)), histogram(YdataFit11(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts1, Ypdf1, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0.5,0,0}METABRIC'])
subplot(6,q/2,mbox(2,k)), histogram(YdataFit22(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts2, Ypdf2, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0.5,0}CCLE'])
subplot(6,q/2,mbox(3,k)), histogram(YdataFit33(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts3, Ypdf3, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0,0.5}GDSC'])
figure(hb(2)) % All CLs
subplot(6,q/2,mbox(1,k)), histogram(YdataFit11(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts1, Ypdf1, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0.5,0,0}METABRIC'])
subplot(6,q/2,mbox(2,k)), histogram(YdataFit22tot(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts2tot, Ypdf2tot, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0.5,0}CCLE'])
subplot(6,q/2,mbox(3,k)), histogram(YdataFit33tot(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts3tot, Ypdf3tot, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0,0.5}GDSC'])
end
defn = ['Row 1, 3 - {\color[rgb]{0,0,0.8}METABRIC}, Row 2, 4 - {\color[rgb]{0,0,0.8}CCLE}, ',...
'Row 3, 6 - {\color[rgb]{0,0,0.8}GDSC}'];
figure(hb(1)), suptitle([{['\bf{\color[rgb]{0.5,0,0}Spline-fitted} Biomarker Distributions ',...
'for {\color[rgb]{0.5, 0.4, 0}BRCA} CLs']}; {defn}])
figure(hb(2)), suptitle([{['\bf{\color[rgb]{0.5,0,0}Spline-fitted} Biomarker Distributions ',...
'for {\color[rgb]{0.5, 0.4, 0}All} CLs']}; {defn}])
%% Feature selection - TIME-CONSUMING LOOP!!!...
% RELIEFF gene ranking...
K = 10; rank1 = zeros(p, q);
rank2 = zeros(p, q); rank2tot = zeros(p, q);
rank3 = zeros(p, q); rank3tot = zeros(p, q);
tic
for k = 1 : q
rank1(:, k) = relieff(Xdata1, Ydata1(:, k), K, 'method', 'regression');
rank2(:, k) = relieff(Xdata2, Ydata2(:, k), K, 'method', 'regression');
rank3(:, k) = relieff(Xdata3, Ydata3(:, k), K, 'method', 'regression');
rank2tot(:, k) = relieff(Xdata2tot, Ydata2tot(:, k), K, 'method', 'regression');
rank3tot(:, k) = relieff(Xdata3tot, Ydata3tot(:, k), K, 'method', 'regression');
end
FEATFILENAME = sprintf('relieff_ranked_genes_MCG_v2_bm%d.mat', bmChoice);
save(FEATFILENAME, 'rank1', 'rank2', 'rank3', 'rank2tot', 'rank3tot')
toc
% Save files...
SavePath = '.\\DataProcessed\\BRCA_MCG\\';
WriteTableForRanks = @(RankArray, FileName) writetable(array2table(RankArray, 'VariableNames', biomarkers,...
'RowNames', XgeneSet.Hugo_Symbol), [SavePath, FileName],...
'Delimiter', '\t', 'WriteVariableNames', true, 'WriteRowNames', true);
WriteTableForRanks(rank1, sprintf('BRCA_biomarker_ranks_METABRIC_%s.txt', strrep(date, '-', '_')))
WriteTableForRanks(rank2, sprintf('BRCA_biomarker_ranks_CCLE_%s.txt', strrep(date, '-', '_')))
WriteTableForRanks(rank3, sprintf('BRCA_biomarker_ranks_GDSC_%s.txt', strrep(date, '-', '_')))
%% Pairwise Dependency - Top Genes...
% Load RELIEFF ranking...
FEATFILENAME = sprintf('relieff_ranked_genes_MCG_v2_bm%d.mat', bmChoice);
load(FEATFILENAME, 'rank1', 'rank2', 'rank3', 'rank2tot', 'rank3tot')
% Finding common genes [Gene names => XgeneSet.Hugo_Symbol]...
nFeat = 10e3; rankImp = cell(q, 1); rankImpTot = cell(q, 1); geneImp = cell(q, 2);
fprintf(1, 'No. of top features used = '), fprintf(2, '%d\n', nFeat)
fprintf(1, 'Intersection of common features betn all %d biomarkers:\n', q)
cRank = (1 : p)'; cRankTot = (1 : p)';
for k = 1 : q
rankImp{k} = intersectn(rank1(1:nFeat, k), rank2(1:nFeat, k), rank3(1:nFeat, k), 'sorted');
rankImpTot{k} = intersectn(rank1(1:nFeat, k), rank2tot(1:nFeat, k), rank3tot(1:nFeat, k), 'sorted');
geneImp(k, :) = {XgeneSet.Hugo_Symbol(rankImp{k}), XgeneSet.Hugo_Symbol(rankImpTot{k})};
cRank = intersectn(cRank, rankImp{k}, 'sorted');
cRankTot = intersectn(cRankTot, rankImpTot{k}, 'sorted');
% fprintf(1, 'Size: BRCA = %d x %d, ALL = %d x %d\n', size(cRank), size(cRankTot))
end
sizeImp = array2table([cellfun(@length, rankImp), cellfun(@length, rankImpTot)],...
'variablenames', {'BRCA', 'ALL'}, 'rownames', biomarkers);
cGeneImp = XgeneSet.Hugo_Symbol(cRank); pFS = numel(cGeneImp);
cGeneImpTot = XgeneSet.Hugo_Symbol(cRankTot); pFStot = numel(cGeneImpTot);
cRank2 = intersect(cRank, cRankTot, 'sorted'); % Overall common genes for both BRCA & All CLs
cGeneCL = XgeneSet.Hugo_Symbol(cRank2); pFS2 = numel(cGeneCL);
% Print results...
% fprintf(1, 'No. of common features betn tumor & cell lines...\n'), disp(sizeImp)
fprintf(1, 'No. of common important genes:\n')
disp(array2table([pFS, pFStot, pFS2]', 'rownames', {'BRCA'; 'ALL'; 'Intersection'}, 'variablenames', {'nCL'}))
% Common gene data [BRCA, ALL]...
XdataFS1 = Xdata1(:, cRank); XdataFS1tot = Xdata1(:, cRankTot);
XdataFS2 = Xdata2(:, cRank); XdataFS2tot = Xdata2tot(:, cRankTot);
XdataFS3 = Xdata3(:, cRank); XdataFS3tot = Xdata3tot(:, cRankTot);
% % Pairwise correlation...
% Pairs...
gnPairsFS = nchoosek(1 : pFS, 2); nPairsFS = size(gnPairsFS, 1);
gnPairListFS = strrep(cellstr([num2str(gnPairsFS(:, 1)), repmat('-', [nPairsFS, 1]), num2str(gnPairsFS(:, 2))]), ' ', '');
gnPairsFStot = nchoosek(1 : pFStot, 2); nPairsFStot = size(gnPairsFStot, 1);
gnPairListFStot = strrep(cellstr([num2str(gnPairsFStot(:, 1)), repmat('-', [nPairsFStot, 1]),...
num2str(gnPairsFStot(:, 2))]), ' ', '');
% Correlation...
xcorrmatFS1 = corr(XdataFS1, 'type', 'spearman'); xcorrFS1 = 1 - squareform(1 - xcorrmatFS1, 'tovector')';
xcorrmatFS2 = corr(XdataFS2, 'type', 'spearman'); xcorrFS2 = 1 - squareform(1 - xcorrmatFS2, 'tovector')';
xcorrmatFS3 = corr(XdataFS3, 'type', 'spearman'); xcorrFS3 = 1 - squareform(1 - xcorrmatFS3, 'tovector')';
xcorrmatFS1tot = corr(XdataFS1tot, 'type', 'spearman');
xcorrFS1tot = 1 - squareform(1 - xcorrmatFS1tot, 'tovector')';
xcorrmatFS2tot = corr(XdataFS2tot, 'type', 'spearman');
xcorrFS2tot = 1 - squareform(1 - xcorrmatFS2tot, 'tovector')';
xcorrmatFS3tot = corr(XdataFS3tot, 'type', 'spearman');
xcorrFS3tot = 1 - squareform(1 - xcorrmatFS3tot, 'tovector')';
xcorrFS_struct = [corr(xcorrFS1, xcorrFS2, 'type', 'pearson'), corr(xcorrFS1tot, xcorrFS2tot, 'type', 'pearson');...
corr(xcorrFS1, xcorrFS3, 'type', 'pearson'), corr(xcorrFS1tot, xcorrFS3tot, 'type', 'pearson');...
corr(xcorrFS2, xcorrFS3, 'type', 'pearson'), corr(xcorrFS2tot, xcorrFS3tot, 'type', 'pearson')];
% Display results...
fprintf(1, 'Correlation betn Gene-pair corr. vectors:\n')
disp(array2table([pFS, pFStot; round(xcorrFS_struct, 4)], 'variablenames', {'BRCA', 'ALL'},....
'rownames', {'nGenesTop'; 'r_MC'; 'r_MG'; 'r_CG'}))
figure(16)
subplot(241), plot(1:nPairsFS, xcorrFS1, 'rs--', 1:nPairsFS, xcorrFS2, 'gs--',...
1:nPairsFS, xcorrFS3, 'bs--')
hold on, line([0, nPairsFS+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--'), % ylim([-0.4, 0.6])
xlim([0, nPairsFS+1]), xticksNoted = floor(linspace(1, nPairsFS, 12));
xticks(xticksNoted), xticklabels(gnPairListFS(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}BRCA}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(xcorrFS_struct(1,1), 2)), ', r_{MG} = ', num2str(round(xcorrFS_struct(2,1), 2)),...
', r_{CG} = ', num2str(round(xcorrFS_struct(3,1), 2)), '})']}])
subplot(242), scatter(xcorrFS1, xcorrFS2, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(243), scatter(xcorrFS1, xcorrFS3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(244), scatter(xcorrFS2, xcorrFS3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}BRCA}]')
subplot(245), plot(1:nPairsFStot, xcorrFS1tot, 'rs--', 1:nPairsFStot, xcorrFS2tot, 'gs--',...
1:nPairsFStot, xcorrFS3tot, 'bs--')
hold on, line([0, nPairsFStot+1], [0.5, 0.5], 'color', 'k', 'linestyle', '--'), % ylim([-0.4, 0.6])
xlim([0, nPairsFStot+1]), xticksNoted = floor(linspace(1, nPairsFStot, 12));
xticks(xticksNoted), xticklabels(gnPairListFStot(xticksNoted))
xlabel('\bfGene-pairs'), ylabel('\bfCorrelation'), legend({'\bfMETABRIC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}ALL}'}; {['({\it\color[rgb]{0.5,0,0}r_{MC} = ',...
num2str(round(xcorrFS_struct(1,2), 2)), ', r_{MG} = ', num2str(round(xcorrFS_struct(2,2), 2)),...
', r_{CG} = ', num2str(round(xcorrFS_struct(3,2), 2)), '})']}])
subplot(246), scatter(xcorrFS1tot, xcorrFS2tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfCCLE')
title('{\color[rgb]{0,0,0.8}METABRIC vs. CCLE} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(247), scatter(xcorrFS1tot, xcorrFS3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfMETABRIC'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}METABRIC vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
subplot(248), scatter(xcorrFS2tot, xcorrFS3tot, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('{\color[rgb]{0,0,0.8}CCLE vs. GDSC} [{\color[rgb]{0.5,0.4,0}ALL}]')
defn = {'Row 1 - For {\color[rgb]{0,0,0.8}BRCA} CLs, Row 2 - For {\color[rgb]{0,0,0.8}All} CLs'};
suptitle([{'\bfComparison of Gene-pair Correlations for {\color[rgb]{0.5,0,0}Top Genes}'}; defn])
%% Additional analyses...
norm01 = @(x) (x - min(ones(size(x, 1), 1) * min(x, [ ], 1))) ./ (ones(size(x, 1), 1) * range(x, 1));
nPts = 1e3; pts = linspace(0, 1, nPts)';
bmPairs = nchoosek(1:q, 2); nPairsBM = size(bmPairs, 1); bmPairsNameList = join(biomarkers(bmPairs), ' - ');
Zstates = {'s00', 's01', 's10', 's11'}; % 4-state discretization
Zpmf1 = array2table(zeros(nPairsBM, 4), 'VariableNames', Zstates);
Zpmf2 = array2table(zeros(nPairsBM, 4), 'VariableNames', Zstates);
Zpmf3 = array2table(zeros(nPairsBM, 4), 'VariableNames', Zstates);
Th = [0.25, 0.75]; % Distribution tail quantiles
asc_met = 'NMI'; % Association metric
YpairLo_asc = NaN(nPairsBM, 3); YpairUp_asc = NaN(nPairsBM, 3);
for b = 1 : nPairsBM
Ypair1 = norm01(Ydata1(:, bmPairs(b, :))); Ypair_pdf1 = [ksdensity(Ypair1(:, 1), pts), ksdensity(Ypair1(:, 2), pts)];
Ypair2 = norm01(Ydata2(:, bmPairs(b, :))); Ypair_pdf2 = [ksdensity(Ypair2(:, 1), pts), ksdensity(Ypair2(:, 2), pts)];
Ypair3 = norm01(Ydata3(:, bmPairs(b, :))); Ypair_pdf3 = [ksdensity(Ypair3(:, 1), pts), ksdensity(Ypair3(:, 2), pts)];
% Otsu's thresholding...
ot1 = [otsuthresh(Ypair_pdf1(:, 1)), otsuthresh(Ypair_pdf1(:, 2))];
ot2 = [otsuthresh(Ypair_pdf2(:, 1)), otsuthresh(Ypair_pdf2(:, 2))];
ot3 = [otsuthresh(Ypair_pdf3(:, 1)), otsuthresh(Ypair_pdf3(:, 2))];
% Discretization...
z1 = ~(Ypair1 <= ot1); Zpmf1{b, :} = [mean(sum(z1 == [0, 0], 2) == 2), mean(sum(z1 == [0, 1], 2) == 2),...
mean(sum(z1 == [1, 0], 2) == 2), mean(sum(z1 == [1, 1], 2) == 2)];
z2 = ~(Ypair2 <= ot2); Zpmf2{b, :} = [mean(sum(z2 == [0, 0], 2) == 2), mean(sum(z2 == [0, 1], 2) == 2),...
mean(sum(z2 == [1, 0], 2) == 2), mean(sum(z2 == [1, 1], 2) == 2)];
z3 = ~(Ypair3 <= ot3); Zpmf3{b, :} = [mean(sum(z3 == [0, 0], 2) == 2), mean(sum(z3 == [0, 1], 2) == 2),...
mean(sum(z3 == [1, 0], 2) == 2), mean(sum(z3 == [1, 1], 2) == 2)];
% Distribution tail threshold quantiles...
qt1 = quantile(Ypair1, Th); qt2 = quantile(Ypair2, Th); qt3 = quantile(Ypair3, Th);
% Lower & upper dist. tails...
YpairLo1 = Ypair1((sum(Ypair1 < qt1(1, :), 2) == 2), :); YpairHi1 = Ypair1((sum(Ypair1 > qt1(2, :), 2) == 2), :);
YpairLo2 = Ypair2((sum(Ypair2 < qt2(1, :), 2) == 2), :); YpairHi2 = Ypair2((sum(Ypair2 > qt2(2, :), 2) == 2), :);
YpairLo3 = Ypair3((sum(Ypair3 < qt3(1, :), 2) == 2), :); YpairHi3 = Ypair3((sum(Ypair3 > qt3(2, :), 2) == 2), :);
% Association betn. pairwise distribution tails...
switch asc_met
case 'corr'
% Correlation betn. tails...
if numel(YpairLo1) > 2, YpairLo_asc(b, 1) = corr(YpairLo1(:, 1), YpairLo1(:, 2), 'type', 'spearman'); end
if numel(YpairLo2) > 2, YpairLo_asc(b, 2) = corr(YpairLo2(:, 1), YpairLo2(:, 2), 'type', 'spearman'); end
if numel(YpairLo3) > 2, YpairLo_asc(b, 3) = corr(YpairLo3(:, 1), YpairLo3(:, 2), 'type', 'spearman'); end
if numel(YpairHi1) > 2, YpairUp_asc(b, 1) = corr(YpairHi1(:, 1), YpairHi1(:, 2), 'type', 'spearman'); end
if numel(YpairHi2) > 2, YpairUp_asc(b, 2) = corr(YpairHi2(:, 1), YpairHi2(:, 2), 'type', 'spearman'); end
if numel(YpairHi3) > 2, YpairUp_asc(b, 3) = corr(YpairHi3(:, 1), YpairHi3(:, 2), 'type', 'spearman'); end
defn = 'Correlation';
case 'NMI'
% Normalized Mutual Info betn. tails...
%%% [MI, Idx] = MutualInformation(X, opt, scale, dtype, C)
if numel(YpairLo1) > 2, YpairLo_asc(b, 1) = MutualInformation(YpairLo1, 'single', 'corr'); end
if numel(YpairLo2) > 2, YpairLo_asc(b, 2) = MutualInformation(YpairLo2, 'single', 'corr'); end
if numel(YpairLo3) > 2, YpairLo_asc(b, 3) = MutualInformation(YpairLo3, 'single', 'corr'); end
if numel(YpairHi1) > 2, YpairUp_asc(b, 1) = MutualInformation(YpairHi1, 'single', 'corr'); end
if numel(YpairHi2) > 2, YpairUp_asc(b, 2) = MutualInformation(YpairHi2, 'single', 'corr'); end
if numel(YpairHi3) > 2, YpairUp_asc(b, 3) = MutualInformation(YpairHi3, 'single', 'corr'); end
defn = 'Normalized MI';
end
% Printing results...
if ~mod(b, 10)
pmfDiff = array2table(round([Zpmf1{b, :} - Zpmf2{b, :}; Zpmf1{b, :} - Zpmf3{b, :}; Zpmf2{b, :} - Zpmf3{b, :}], 4),...
'RowNames', {'M-C'; 'M-G'; 'C-G'}, 'VariableNames', Zstates);
fprintf(1, '\nBiomarker pair [%d] = ', b), fprintf(2, '%s\n', bmPairsNameList{b})
fprintf(1, 'Error betn discrete PMFs = \n'), disp(pmfDiff)
ascTail = array2table(round([YpairLo_asc(b, :); YpairUp_asc(b, :)], 4), 'RowNames', {'Lower tail'; 'Upper tail'},...
'VariableNames', {'METABRIC', 'CCLE', 'GDSC'});
fprintf(1, '%s between lower & upper distribution tails = \n', asc_met), disp(ascTail)
end
end
% %%
% Visualization...
numBin = 50;
bmPairList = strrep(cellstr([num2str(bmPairs(:, 1)), repmat('-', [nPairsBM, 1]), num2str(bmPairs(:, 2))]), ' ', '');
figure(17), clf
ax1 = axes('Position', [0.05, 0.14, 0.15, 0.6]); ax2 = axes('Position', [0.21, 0.75, 0.65, 0.2]);
ax3 = axes('Position', [0.21, 0.14, 0.65, 0.6], 'YAxisLocation', 'right');
histogram(ax1, Ypair1(:, 2), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax1, [-90, 90]), hold on
histogram(ax2, Ypair1(:, 1), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax2, [0, 90]), hold on
scatter(ax3, Ypair1(:, 1), Ypair1(:, 2), 18, 'filled', 'o'), box on
hold on, line(ax3, [0, 1], [ot1(2), ot1(2)], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt1(1, 2), qt1(1, 2)], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt1(1, 1), qt1(1, 1)], [0, 1], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [ot1(1), ot1(1)], [0, 1], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt1(2, 2), qt1(2, 2)], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt1(2, 1), qt1(2, 1)], [0, 1], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
legend('\bf\itData', '\bfTh_{OTSU}', '\bfTh_{LO}', '\bfTh_{UP}')
xlabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 1)})), ylabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 2)}))
title('\bfPairwise Biomarker Scatter: {\color[rgb]{0,0,0.8}METABRIC}', 'Position', [0.4, 1.35, 0], 'FontSize', 14)
set(ax1, 'visible', 'off'), set(ax2, 'visible', 'off')
% saveas(figure(17), ['.\Figures__MB_CCLE_GDSC_Analysis\v2\Biomarker_pair_scatters\',...
% sprintf('Biomarker_pair_scatter_%s_%s_MB.png', biomarkers{bmPairs(b, 1)}, biomarkers{bmPairs(b, 2)})])
figure(18), clf
ax1 = axes('Position', [0.05, 0.14, 0.15, 0.6]); ax2 = axes('Position', [0.21, 0.75, 0.65, 0.2]);
ax3 = axes('Position', [0.21, 0.14, 0.65, 0.6], 'YAxisLocation', 'right');
histogram(ax1, Ypair2(:, 2), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax1, [-90, 90]), hold on
histogram(ax2, Ypair2(:, 1), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax2, [0, 90]), hold on
scatter(ax3, Ypair2(:, 1), Ypair2(:, 2), 18, 'filled', 'o'), box on
hold on, line(ax3, [0, 1], [ot2(2), ot2(2)], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt2(1, 2), qt2(1, 2)], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt2(1, 1), qt2(1, 1)], [0, 1], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [ot2(1), ot2(1)], [0, 1], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt2(2, 2), qt2(2, 2)], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt2(2, 1), qt2(2, 1)], [0, 1], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
legend('\bf\itData', '\bfTh_{OTSU}', '\bfTh_{LO}', '\bfTh_{UP}')
xlabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 1)})), ylabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 2)}))
title('\bfPairwise Biomarker Scatter: {\color[rgb]{0,0,0.8}CCLE}', 'Position', [0.4, 1.35, 0], 'FontSize', 14)
set(ax1, 'visible', 'off'), set(ax2, 'visible', 'off')
% saveas(figure(18), ['.\Figures__MB_CCLE_GDSC_Analysis\v2\Biomarker_pair_scatters\',...
% sprintf('Biomarker_pair_scatter_%s_%s_CCLE.png', biomarkers{bmPairs(b, 1)}, biomarkers{bmPairs(b, 2)})])
figure(19), clf
ax1 = axes('Position', [0.05, 0.14, 0.15, 0.6]); ax2 = axes('Position', [0.21, 0.75, 0.65, 0.2]);
ax3 = axes('Position', [0.21, 0.14, 0.65, 0.6], 'YAxisLocation', 'right');
histogram(ax1, Ypair3(:, 2), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax1, [-90, 90]), hold on
histogram(ax2, Ypair3(:, 1), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax2, [0, 90]), hold on
scatter(ax3, Ypair3(:, 1), Ypair3(:, 2), 18, 'filled', 'o'), box on
hold on, line(ax3, [0, 1], [ot3(2), ot3(2)], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt3(1, 2), qt3(1, 2)], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt3(1, 1), qt3(1, 1)], [0, 1], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [ot3(1), ot3(1)], [0, 1], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt3(2, 2), qt3(2, 2)], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt3(2, 1), qt3(2, 1)], [0, 1], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
legend('\bf\itData', '\bfTh_{OTSU}', '\bfTh_{LO}', '\bfTh_{UP}')
xlabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 1)})), ylabel(sprintf('\\bf%s', biomarkers{bmPairs(b, 2)}))
title('\bfPairwise Biomarker Scatter: {\color[rgb]{0,0,0.8}GDSC}', 'Position', [0.4, 1.35, 0], 'FontSize', 14)
set(ax1, 'visible', 'off'), set(ax2, 'visible', 'off')
% saveas(figure(19), ['.\Figures__MB_CCLE_GDSC_Analysis\v2\Biomarker_pair_scatters\',...
% sprintf('Biomarker_pair_scatter_%s_%s_GDSC.png', biomarkers{bmPairs(b, 1)}, biomarkers{bmPairs(b, 2)})])
% end
% %
% Linear model fit...
model12 = {fitlm(Zpmf1{:, 1}, Zpmf2{:, 1}), fitlm(Zpmf1{:, 2}, Zpmf2{:, 2}),...
fitlm(Zpmf1{:, 3}, Zpmf2{:, 3}), fitlm(Zpmf1{:, 4}, Zpmf2{:, 4})};
model13 = {fitlm(Zpmf1{:, 1}, Zpmf3{:, 1}), fitlm(Zpmf1{:, 2}, Zpmf3{:, 2}),...
fitlm(Zpmf1{:, 3}, Zpmf3{:, 3}), fitlm(Zpmf1{:, 4}, Zpmf3{:, 4})};
w12 = [model12{1}.Coefficients.Estimate, model12{2}.Coefficients.Estimate,...
model12{3}.Coefficients.Estimate, model12{4}.Coefficients.Estimate];
w13 = [model13{1}.Coefficients.Estimate, model13{2}.Coefficients.Estimate,...
model13{3}.Coefficients.Estimate, model13{4}.Coefficients.Estimate];
Wm = [mean([w12(1, :); w13(1, :)], 1); mean([w12(2, :); w13(2, :)], 1)]; % Mean coefficient
xval = linspace(0, round(max(max(Zpmf1{:, :})), 1), 100)'; Xval = [ones(size(xval)), xval];
yfit = [Xval * Wm(:, 1), Xval * Wm(:, 2), Xval * Wm(:, 3), Xval * Wm(:, 4)]; % Fitted line
% Discretized biomarker-pair PMFs...
figure(20), clf
subplot(221), scatter(Zpmf1{:, 1}, Zpmf2{:, 1}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 1}, Zpmf3{:, 1}, 24, 'ro', 'filled'), box on
hold on, plot(xval, yfit(:, 1), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfMETABRIC - CCLE', '\bfMETABRIC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}00')
subplot(222), scatter(Zpmf1{:, 2}, Zpmf2{:, 2}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 2}, Zpmf3{:, 2}, 24, 'ro', 'filled'), box on
hold on, plot(xval, yfit(:, 2), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfMETABRIC - CCLE', '\bfMETABRIC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}01')
subplot(223), scatter(Zpmf1{:, 3}, Zpmf2{:, 3}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 3}, Zpmf3{:, 3}, 24, 'ro', 'filled'), box on
hold on, plot(xval, yfit(:, 3), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfMETABRIC - CCLE', '\bfMETABRIC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}10')
subplot(224), scatter(Zpmf1{:, 4}, Zpmf2{:, 4}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 4}, Zpmf3{:, 4}, 24, 'ro', 'filled'), box on
hold on, plot(xval, yfit(:, 4), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfMETABRIC - CCLE', '\bfMETABRIC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}11')
suptitle(['\bfComparison of {\color[rgb]{0,0,0.8}Discretized} Gene-pair Fractions for Biomarkers ',...
'[{\color[rgb]{0.6,0.2,0.2}BRCA}]'])
% saveas(figure(20), '.\Figures__MB_CCLE_GDSC_Analysis\v2\Biomarker_pair_discrete_fractions_BRCA.png')
% Corr. betn. tails...
figure(21), clf
subplot(231), scatter(YpairLo_asc(:, 1), YpairLo_asc(:, 2), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairLo_asc(:, 1))+0.1, -0.1, max(YpairLo_asc(:, 2))+0.1])
xlabel('\bfMETABRIC'), ylabel('\bfCCLE'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(232), scatter(YpairLo_asc(:, 1), YpairLo_asc(:, 3), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairLo_asc(:, 1))+0.1, -0.1, max(YpairLo_asc(:, 3))+0.1])
xlabel('\bfMETABRIC'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(233), scatter(YpairLo_asc(:, 2), YpairLo_asc(:, 3), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairLo_asc(:, 2))+0.1, -0.1, max(YpairLo_asc(:, 3))+0.1])
xlabel('\bfCCLE'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(234), scatter(YpairUp_asc(:, 1), YpairUp_asc(:, 2), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairUp_asc(:, 1))+0.1, -0.1, max(YpairUp_asc(:, 2))+0.1])
xlabel('\bfMETABRIC'), ylabel('\bfCCLE'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
subplot(235), scatter(YpairUp_asc(:, 1), YpairUp_asc(:, 3), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairUp_asc(:, 1))+0.1, -0.1, max(YpairUp_asc(:, 3))+0.1])
xlabel('\bfMETABRIC'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
subplot(236), scatter(YpairUp_asc(:, 2), YpairUp_asc(:, 3), 24, 'o', 'filled'), box on, %grid on
axis([-0.1, max(YpairUp_asc(:, 2))+0.1, -0.1, max(YpairUp_asc(:, 3))+0.1])
xlabel('\bfCCLE'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
suptitle('\bfComparison of Gene-pair Distribution {\itTails} for Biomarkers [{\color[rgb]{0.6,0.2,0.2}BRCA}]')
% saveas(figure(21), '.\Figures__MB_CCLE_GDSC_Analysis\v2\Biomarker_pair_tail_associations_BRCA.png')
% Correlation betn. association vectors...
kLo12 = ~(isnan(YpairLo_asc(:, 1)) | isnan(YpairLo_asc(:, 2)));
kLo13 = ~(isnan(YpairLo_asc(:, 1)) | isnan(YpairLo_asc(:, 3)));
kLo23 = ~(isnan(YpairLo_asc(:, 2)) | isnan(YpairLo_asc(:, 3)));
kUp12 = ~(isnan(YpairUp_asc(:, 1)) | isnan(YpairUp_asc(:, 2)));
kUp13 = ~(isnan(YpairUp_asc(:, 1)) | isnan(YpairUp_asc(:, 3)));
kUp23 = ~(isnan(YpairUp_asc(:, 2)) | isnan(YpairUp_asc(:, 3)));
YpairTail_ascStruct = array2table(round([corr(YpairLo_asc(kLo12, 1), YpairLo_asc(kLo12, 2), 'type', 'spearman'),...
corr(YpairLo_asc(kLo13, 1), YpairLo_asc(kLo13, 3), 'type', 'spearman'),...
corr(YpairLo_asc(kLo23, 2), YpairLo_asc(kLo23, 3), 'type', 'spearman');...
corr(YpairUp_asc(kUp12, 1), YpairUp_asc(kUp12, 2), 'type', 'spearman'),...
corr(YpairUp_asc(kUp13, 1), YpairUp_asc(kUp13, 3), 'type', 'spearman'),...
corr(YpairUp_asc(kUp23, 2), YpairUp_asc(kUp23, 3), 'type', 'spearman')], 4),...
'rownames', {'Lower tail'; 'Upper tail'}, 'variablename', {'METABRIC', 'CCLE', 'GDSC'});
disp(YpairTail_ascStruct)
%% TRANSFER LEARNING v4...
% Xdata1, Xdata2, Ydata1, Ydata2
% % Directory info for image saving...
% SAVEPATH = sprintf('C:\\Users\\%s\\', getenv('username'));
% SAVEPATH = [SAVEPATH, 'Dropbox (Texas Tech)\Tumor_CL_TL_2018\Figures__MB_CCLE_GDSC_Analysis\v2\'];
% Load RELEIFF features...
FEATFILENAME = sprintf('relieff_ranked_genes_MCG_v2_bm%d.mat', bmChoice);
load(FEATFILENAME, 'rank1', 'rank2', 'rank3', 'rank2tot', 'rank3tot')
% Metric function definitions...
norm01 = @(x) (x - (ones(size(x, 1), 1) * min(x, [ ], 1))) ./ (ones(size(x, 1), 1) * range(x, 1)); % Normalize in [0, 1]
NMAE = @(y, y_hat) mean(abs(y - y_hat)) / mean(abs(y - mean(y)));
NRMSE = @(y, y_hat) sqrt(mean((y - y_hat).^2)) / std(y, 1);
fprintf(2, 'Tumor cultures to cell line data Transfer Learning...\n')
PredApp = {'DMTL', 'DMTL_SS', 'CATL', 'BL'};
RESULTS = array2table(zeros(q+1, numel(PredApp)), 'RowNames', [biomarkers; {'Mean'}], 'VariableNames', PredApp);
RESULTS = struct('NRMSE', RESULTS, 'NMAE', RESULTS, 'SCC', RESULTS, 'numFeat', zeros(q+1, 1));
for chosenBMidx = 1:q
%%% Feature selection...
fprintf(1, 'Chosen biomarker = '), fprintf(2, '%s\n', biomarkers{chosenBMidx})
ranks = [rank1(:, chosenBMidx), rank2(:, chosenBMidx), rank3(:, chosenBMidx)];
% fprintf(1, 'Extracting common CL features...\n') % Extracting a common set of size m_opt
nGN = 300; m_opt = 100; gnRank = intersectn(ranks(1:nGN, 2), ranks(1:nGN, 3), 'sorted');
m = numel(gnRank); nI = 0; m0 = m;
while m < m_opt
nI = nI + 1; nGN = nGN + 100;
gnRank = intersectn(ranks(1:nGN, 2), ranks(1:nGN, 3), 'sorted'); m = numel(gnRank);
end
% fprintf(1, '\t#genes used = %d, #Iterations = %d\n\tInitial size = %d, Final size = %d\n', nGN, nI, m0, m)
% Defining primary & secondary sets...
% fprintf(2, 'Tumor cultures to cell line data Transfer Learning...\n')
dsChoice = 1; % Switch for dataset choices
switch dsChoice
case 1 % Tumor => primary, CL => secondary
dsFlag = {'METABRIC', 'CCLE + GDSC'};
gnList = XgeneSet.Hugo_Symbol(gnRank); m = numel(gnList);
X1 = Xdata1(:, gnRank); X2 = [Xdata2(:, gnRank); Xdata3(:, gnRank)];
Y1 = (Ydata1(:, chosenBMidx));
Y2 = [(Ydata2(:, chosenBMidx)); (Ydata3(:, chosenBMidx))];
case 2 % CL => primary, Tumor => secondary
dsFlag = {'CCLE + GDSC', 'METABRIC'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 1)); m = numel(gnList);
X1 = [Xdata2(:, ranks(1:m_opt, 1)); Xdata3(:, ranks(1:m_opt, 1))]; X2 = Xdata1(:, ranks(1:m_opt, 1));
Y1 = [Ydata2(:, chosenBMidx); Ydata3(:, chosenBMidx)]; Y2 = Ydata1(:, chosenBMidx);
case 3 % Tumor => primary, CCLE => secondary
dsFlag = {'METABRIC', 'CCLE'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 2)); m = numel(gnList);
X1 = Xdata1(:, ranks(1:m_opt, 2)); X2 = Xdata2(:, ranks(1:m_opt, 2));
Y1 = Ydata1(:, chosenBMidx); Y2 = Ydata2(:, chosenBMidx);
case 4 % CCLE => primary, Tumor => secondary
dsFlag = {'CCLE', 'METABRIC'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 1)); m = numel(gnList);
X1 = Xdata2(:, ranks(1:m_opt, 1)); X2 = Xdata1(:, ranks(1:m_opt, 1));
Y1 = Ydata2(:, chosenBMidx); Y2 = Ydata1(:, chosenBMidx);
case 5 % Tumor => primary, GDSC => secondary
dsFlag = {'METABRIC', 'GDSC'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 3)); m = numel(gnList);
X1 = Xdata1(:, ranks(1:m_opt, 3)); X2 = Xdata3(:, ranks(1:m_opt, 3));
Y1 = Ydata1(:, chosenBMidx); Y2 = Ydata3(:, chosenBMidx);
case 6 % GDSC => primary, Tumor => secondary
dsFlag = {'GDSC', 'METABRIC'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 1)); m = numel(gnList);
X1 = Xdata3(:, ranks(1:m_opt, 1)); X2 = Xdata1(:, ranks(1:m_opt, 1));
Y1 = Ydata3(:, chosenBMidx); Y2 = Ydata1(:, chosenBMidx);
end
assert(size(X1, 2) == size(X2, 2)) % Check if #features are same
X1 = (X1); Y1 = norm01(Y1); N1 = numel(Y1);
X2 = (X2); Y2 = norm01(Y2); N2 = numel(Y2);
fprintf(1, 'Primary = '), fprintf(2, '%s,\t', dsFlag{1})
fprintf(1, 'Secondary = '), fprintf(2, '%s\n', dsFlag{2})
% Save BM datasets for analyses...
datapath = sprintf('C:\\Users\\%s\\Dropbox (Texas Tech)\\#DataStorageSRD\\TumorCLfeatureSelectedData',...
getenv('username'));
if ~mod(dsChoice, 2), dsChar = {'_Rev', strrep(dsFlag{1}, ' + ', '_'), 'Target'};
else, dsChar = {'', strrep(dsFlag{2}, ' + ', '_'), 'Source'}; end
% datadir = sprintf('BRCA_%s_%s', dsChar{3}, dsChar{2});
% filename = sprintf('Data_BM_%d_f%d_%s%s.mat', chosenBMidx, m_opt, dsChar{2}, dsChar{1});
% save(fullfile(datapath, datadir, filename), 'X1', 'X2', 'Y1', 'Y2')
% Distributions...
figure(31), clf
subplot(221), histogram(X1, numBin, 'normalization', 'probability')
title(sprintf('X_{primary}, dim = \\color[rgb]{0, 0, 0.8}%d x %d', size(X1)))
subplot(222), histogram(Y1, numBin, 'normalization', 'probability')
title(sprintf('y_{primary}, dim = \\color[rgb]{0, 0, 0.8}%d x %d', size(Y1)))
subplot(223), histogram(X2, numBin, 'normalization', 'probability')
title(sprintf('X_{secondary}, dim = \\color[rgb]{0, 0, 0.8}%d x %d', size(X2)))
subplot(224), histogram(Y2, numBin, 'normalization', 'probability')
title(sprintf('y_{secondary}, dim = \\color[rgb]{0, 0, 0.8}%d x %d', size(Y2)))
defn = sprintf('Row 1 - {\\color[rgb]{0,0,0.8}%s}, Row 2 - {\\color[rgb]{0,0,0.8}%s}', dsFlag{1}, dsFlag{2});
suptitle([{sprintf('\\bfPrimary & Secondary Data Histograms, Biomarker = {\\color[rgb]{0, 0.5, 0.4}%s}',...
biomarkers{chosenBMidx})}; {defn}])
% saveas(figure(31), [SAVEPATH, sprintf('TLP_data_distribution_%s.png', biomarkers{chosenBMidx})])
% saveas(figure(31), [SAVEPATH, sprintf('TLP_data_distribution_MC_%s.png', biomarkers{chosenBMidx})])
%%% Transfer Learning predictions...
[Y1c, X2a, CorrMatCA] = CorrAlignTransLearn(X1, X2, Y2);
[Y1m, Y2m, Map, densityPts, CorrMatDM] = DistMatchTransLearn(X1, Y1, X2, Y2);
%%% GX Corr. Heatmaps...
% Distribution matching...
CorrStructDM = [corr(CorrMatDM.Xps1D, CorrMatDM.Xp1D, 'type', 'pearson'),...
corr(CorrMatDM.Xps1D, CorrMatDM.Xs1D, 'type', 'pearson')];
figure(32), clf; ColMap = jet;
subplot(221), heatmap(CorrMatDM.Xp, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
colorbar off; title(['Primary: ', dsFlag{1}])
subplot(223), heatmap(CorrMatDM.Xs, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
colorbar off; title(['Secondary: ', dsFlag{2}])
subplot(224), heatmap(CorrMatDM.Xps, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
title('Primary Data mapped to SS')
subplot(222), str = {'{\bfCorrelation between...}';...
[' Mapped primary & primary Corr vectors = {\bf', num2str(round(CorrStructDM(1), 4)), '}'];...
[' Mapped primary & secondary Corr vectors = {\bf', num2str(round(CorrStructDM(2), 4)), '}']};
dim = [0.6, 0.5, 0.5, 0.3]; ann = annotation('textbox', dim, 'String', str, 'FitBoxToText', 'on');
ann.FitBoxToText = 'on'; ann.EdgeColor = 'b'; ann.LineWidth = 2; ann.FontSize = 12; axis off
suptitle(['\bfCovariate Correlation Structure, Biomarker = ', biomarkers{chosenBMidx}])
% % saveas(figure(32), ['./Figures__MB_CCLE_GDSC_Analysis/',...
% % 'Covariate_heatmaps_before_after_mapping_MCG_', biomarkers{chosenBMidx}, '.png'])
% % saveas(figure(32), ['./Figures__MB_CCLE_GDSC_Analysis/',...
% % 'Covariate_heatmaps_before_after_mapping_MCG_', biomarkers{chosenBMidx}, '.fig'])
% saveas(figure(32),...
% [SAVEPATH, sprintf('TLP_covariate_heatmaps_before_after_mapping_%s.png', biomarkers{chosenBMidx})])
% saveas(figure(32),...
% [SAVEPATH, sprintf('TLP_covariate_heatmaps_before_after_mapping_MC_%s.png', biomarkers{chosenBMidx})])
% Correlation alignment...
CorrStructCA = [corr(CorrMatCA.Xp1D, CorrMatCA.Xsp1D, 'type', 'pearson'),...
corr(CorrMatCA.Xp1D, CorrMatCA.Xs1D, 'type', 'pearson')];
figure(33), clf; ColMap = jet;
subplot(221), heatmap(CorrMatCA.Xp, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
colorbar off; title(['Primary: ', dsFlag{1}])
subplot(223), heatmap(CorrMatCA.Xs, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
colorbar off; title(['Secondary: ', dsFlag{2}])
subplot(224), heatmap(CorrMatCA.Xsp, 'XLabel', {'Top Genes'}, 'YLabel', {'Top Genes'}, 'Colormap', ColMap);
title('Secondary Data after CORAL')
subplot(222), str = {'{\bfCorrelation between...}';...
[' Aligned secondary & primary Corr vectors = {\bf', num2str(round(CorrStructCA(1), 4)), '}'];...
[' Aligned secondary & secondary Corr vectors = {\bf', num2str(round(CorrStructCA(2), 4)), '}']};
dim = [0.6, 0.5, 0.5, 0.3]; ann = annotation('textbox', dim, 'String', str, 'FitBoxToText', 'on');