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TmrClnTLnsclcTest_v2.m
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TmrClnTLnsclcTest_v2.m
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clc; clear; %close all
DATAPATH = sprintf('C:\\Users\\%s\\', getenv('username'));
DATAPATH = {[DATAPATH, 'Dropbox (Texas Tech)\#DataStorageSRD\TCGA_LUAD_cBioPortal\'];
[DATAPATH, 'Dropbox (Texas Tech)\#DataStorageSRD\TCGA_LUSC_cBioPortal\'];
[DATAPATH, 'Dropbox (Texas Tech)\#DataStorageSRD\CCLE_cBioPortal\cellline_ccle_broad\'];
[DATAPATH, 'Google Drive\Data CCLE and GDSC\GDSC\Version 7\']};
FILENAME = {'data_RNA_Seq_v2_expression_median_tcga_luad.txt';
'data_RNA_Seq_v2_expression_median_tcga_lusc.txt';
'data_expression_median_ccle.txt'; 'gdsc_v7_gene_expression_processed_SRD_09_27_2018.xlsx';
'data_clinical_sample_ccle.txt'; 'gdsc_v7_Cell_Lines_Details.xlsx'};
EmptyDataFlag = {'.', 'NA', 'N/A'};
%% Data Extraction...
ThZero = 60;
%%% TCGA LUAD (provisional)...
dataTableGX1 = readtable([DATAPATH{1}, FILENAME{1}], 'TreatAsEmpty', EmptyDataFlag);
GX1 = dataTableGX1{:, 3:end}'; genes1 = dataTableGX1(:, 1:2);
samples1 = dataTableGX1.Properties.VariableNames(3:end)';
nZeros1 = [sum(GX1 == 0, 1); mean(GX1 == 0, 1)*100]'; idxZeros1 = (nZeros1(:, 2) > ThZero); % Checking zero%
GX1 = GX1(:, ~idxZeros1); genes1 = genes1(~idxZeros1, :);
fprintf(1, '# of LUAD genes discarded due to high zero%% = '), fprintf(2, '%d\n', sum(idxZeros1))
[~, ind11] = sort(lower(genes1.Hugo_Symbol)); [~, ind12] = sort(lower(samples1)); % Alphabetical sorting
genes1 = genes1(ind11, :); GX1 = GX1(:, ind11);
samples1 = samples1(ind12); GX1 = GX1(ind12, :);
%%% TCGA LUSC (provisional)...
dataTableGX2 = readtable([DATAPATH{2}, FILENAME{2}], 'TreatAsEmpty', EmptyDataFlag);
GX2 = dataTableGX2{:, 3:end}'; genes2 = dataTableGX2(:, 1:2);
samples2 = dataTableGX2.Properties.VariableNames(3:end)';
nZeros2 = [sum(GX2 == 0, 1); mean(GX2 == 0, 1)*100]'; idxZeros2 = (nZeros2(:, 2) > ThZero); % Checking zero%
GX2 = GX2(:, ~idxZeros2); genes2 = genes2(~idxZeros2, :);
fprintf(1, '# of LUSC genes discarded due to high zero%% = '), fprintf(2, '%d\n', sum(idxZeros2))
[~, ind21] = sort(lower(genes2.Hugo_Symbol)); [~, ind22] = sort(lower(samples2)); % Alphabetical sorting
genes2 = genes2(ind21, :); GX2 = GX2(:, ind21);
samples2 = samples2(ind22); GX2 = GX2(ind22, :);
% [~, ig1, ig2] = intersect(lower(genes1.Hugo_Symbol), lower(genes2.Hugo_Symbol), 'sorted');
% genes12 = genes1(ig1, :); % Common NSCLC genes
%%% CCLE (vNew)...
dataTableGX3 = readtable([DATAPATH{3}, FILENAME{3}], 'TreatAsEmpty', EmptyDataFlag);
dataGX3 = dataTableGX3{:, 3:end}'; genes3 = dataTableGX3(:, 1:2);
CLdes3 = dataTableGX3.Properties.VariableDescriptions(3:end)';
idx3 = ~cellfun(@isempty, CLdes3); CLdes3 = split(CLdes3(idx3), {' ', ''''});
CL3 = dataTableGX3.Properties.VariableNames(3:end)'; CL3(idx3) = CLdes3(:, end-1); % Proper CL names
CLtable3 = readtable([DATAPATH{3}, FILENAME{5}], 'HeaderLines', 4, 'ReadVariableNames', true); % CL info
[~, ~, idx3] = intersect(CL3, CLtable3.SAMPLE_ID, 'stable'); CLinfo3 = CLtable3(idx3, 2:5); % Relevant info
tumorType3 = sort(unique(CLinfo3.TUMOR_TYPE)); lungInd3 = strcmpi(CLinfo3.TUMOR_TYPE, 'lung_nsc');
lungCL3 = CL3(lungInd3); GX3 = dataGX3(lungInd3, :); lungCLinfo3 = CLinfo3(lungInd3, :); % NSCLC data
[~, ind31] = sort(lower(genes3.Hugo_Symbol)); [~, ind32] = sort(lower(lungCL3)); % Alphabetical sorting
genes3 = genes3(ind31, :); GX3 = GX3(:, ind31);
lungCL3 = lungCL3(ind32); GX3 = GX3(ind32, :); lungCLinfo3 = lungCLinfo3(ind32, :);
%%% GDSC (v7)...
dataTableGX4 = readtable([DATAPATH{4}, FILENAME{4}], 'TreatAsEmpty', EmptyDataFlag);
dataGX4 = dataTableGX4{:, 2:end}'; genes4 = dataTableGX4.Gene_CL;
CLdes4 = dataTableGX4.Properties.VariableDescriptions(2:end)';
idx4 = ~cellfun(@isempty, CLdes4); CLdes4 = split(CLdes4(idx4), {' ', ''''});
CL4 = dataTableGX4.Properties.VariableNames(2:end)'; CL4(idx4) = CLdes4(:, end-1); % Proper CL names
CLtable4 = readtable([DATAPATH{4}, FILENAME{6}], 'ReadVariableNames', true); % CL info
CLinfo4 = CLtable4(1:end-1, [1, 8:10]); CLinfo4.Sample_Name = strrep(CLinfo4.Sample_Name, '-', '');
[~, ~, idx4] = intersect(CL4, join(CLinfo4{:, 1:2}, '_'), 'stable'); % Relevant info
CLinfo4 = CLinfo4(idx4, :); tumorType4 = sort(unique(CLinfo4.GDSC_Tissue_descriptor_1));
lungInd4 = strcmpi(CLinfo4.GDSC_Tissue_descriptor_1, 'lung_nsclc');
lungCL4 = CL4(lungInd4); GX4 = dataGX4(lungInd4, :); lungCLinfo4 = CLinfo4(lungInd4, :); % NSCLC data
[~, ind41] = sort(lower(genes4)); [~, ind42] = sort(lower(lungCL4)); % Alphabetical sorting
genes4 = genes4(ind41, :); GX4 = GX4(:, ind41);
lungCL4 = lungCL4(ind42); GX4 = GX4(ind42, :); lungCLinfo4 = lungCLinfo4(ind42, :);
% [~, ig3, ig4] = intersect(lower(genes3.Hugo_Symbol), lower(genes4), 'sorted');
% genes34 = genes3(ig3, :); % Common NSCLC genes
%% Matched data...
matGN = [ ]; [~, matGN(:, 1), matGN(:, 2), matGN(:, 3), matGN(:, 4)] = intersectn(lower(genes1.Hugo_Symbol),...
lower(genes2.Hugo_Symbol), lower(genes3.Hugo_Symbol), lower(genes4), 'stable');
% samples1, samples2, lungCL3, lungCL4
geneSet = genes1(matGN(:, 1), :); % Common genes betn TCGA, CCLE & GDSC
GXset1 = GX1(:, matGN(:, 1)); GXset2 = GX2(:, matGN(:, 2));
GXset3 = GX3(:, matGN(:, 3)); GXset4 = GX4(:, matGN(:, 4));
% % Standardization (z-score)...
% GXset1 = zscore(GXset1, [ ], 1); GXset2 = zscore(GXset2, [ ], 1);
% GXset3 = zscore(GXset3, [ ], 1); GXset4 = zscore(GXset4, [ ], 1);
% Finding biomarkers in data...
biomarkerLungList = readtable('Biomarker_list_nsclc.csv');
[~, ind] = intersect(lower(geneSet.Hugo_Symbol), lower(biomarkerLungList.Symbol), 'stable');
bmLungIdx = false(numel(geneSet.Hugo_Symbol), 1); bmLungIdx(ind) = true;
biomarkers = geneSet.Hugo_Symbol(bmLungIdx); XgeneSet = geneSet(~bmLungIdx, :);
% Biomarker expression as response...
Xdata1 = GXset1(:, ~bmLungIdx); Ydata1 = GXset1(:, bmLungIdx);
Xdata2 = GXset2(:, ~bmLungIdx); Ydata2 = GXset2(:, bmLungIdx);
Xdata3 = GXset3(:, ~bmLungIdx); Ydata3 = GXset3(:, bmLungIdx);
Xdata4 = GXset4(:, ~bmLungIdx); Ydata4 = GXset4(:, bmLungIdx);
p = sum(~bmLungIdx); q = sum(bmLungIdx);
n1 = numel(samples1); n2 = numel(samples2);
n3 = numel(lungCL3); n4 = numel(lungCL4);
% Standardization (z-score)...
GXset11 = zscore(GXset1, [ ], 1); GXset22 = zscore(GXset2, [ ], 1);
GXset33 = zscore(GXset3, [ ], 1); GXset44 = zscore(GXset4, [ ], 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);
Xdata44 = zscore(Xdata4, [ ], 1); Ydata44 = zscore(Ydata4, [ ], 1);
%%% Distributions...
numBin = 50;
% Biomarkers...
hb = figure(2); nPts = 1e3; clf(figure(hb)); Q = q + mod(q, 2);
mbox = [1:Q/2, 2*Q+1:5*Q/2; Q/2+1:Q, 5*Q/2+1:3*Q; Q+1:3*Q/2, 3*Q+1:7*Q/2; 3*Q/2+1:2*Q, 7*Q/2+1:4*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);
[Ypdf4, pts4] = ksdensity(Ydata44(:, k), 'NumPoints', nPts);
figure(hb) % NSCLC
subplot(8,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}TCGA-LUAD'])
subplot(8,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]{0.5,0.2,0.2}TCGA-LUSC'])
subplot(8,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]{0,0.5,0}CCLE-NSCLC'])
subplot(8,Q/2,mbox(4,k)), histogram(Ydata44(:, k), numBin, 'normalization', 'pdf')
hold on, plot(pts4, Ypdf4, 'r-.', 'linewidth', 2), hold off
title([biomarkers{k}, ' - \color[rgb]{0,0,0.5}GDSC-NSCLC'])
end
defn = ['Row 1, 4 - {\color[rgb]{0,0,0.8}TCGA-LUAD}, Row 2, 5 - {\color[rgb]{0,0,0.8}TCGA-LUSC}, ',...
'Row 3, 6 - {\color[rgb]{0,0,0.8}CCLE-NSCLC}, Row 4, 8 - {\color[rgb]{0,0,0.8}GDSC-NSCLC}'];
figure(hb), suptitle([{'\bfBiomarker Distributions for {\color[rgb]{0.5,0,0}NSCLC} CLs'}; {defn}])
%% Save data...
XdataTable1 = array2table(Xdata1, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', samples1);
XdataTable2 = array2table(Xdata2, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', samples2);
XdataTable3 = array2table(Xdata3, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', lungCL3);
XdataTable4 = array2table(Xdata4, 'VariableNames', XgeneSet.Hugo_Symbol, 'RowNames', lungCL4);
YdataTable1 = array2table(Ydata1, 'VariableNames', biomarkers, 'RowNames', samples1);
YdataTable2 = array2table(Ydata2, 'VariableNames', biomarkers, 'RowNames', samples2);
YdataTable3 = array2table(Ydata3, 'VariableNames', biomarkers, 'RowNames', lungCL3);
YdataTable4 = array2table(Ydata4, 'VariableNames', biomarkers, 'RowNames', lungCL4);
SavePath = '.\\Data\\';
writetable(XdataTable1, sprintf([SavePath, 'LUAD_gene_expression_TCGA_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(XdataTable2, sprintf([SavePath, 'LUSC_gene_expression_TCGA_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(XdataTable3, sprintf([SavePath, 'NSCLC_gene_expression_CCLE_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(XdataTable4, sprintf([SavePath, 'NSCLC_gene_expression_GDSC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable1, sprintf([SavePath, 'LUAD_biomarker_expression_TCGA_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable2, sprintf([SavePath, 'LUSC_biomarker_expression_TCGA_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable3, sprintf([SavePath, 'NSCLC_biomarker_expression_CCLE_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
writetable(YdataTable4, sprintf([SavePath, 'NSCLC_biomarker_expression_GDSC_%s.txt'], strrep(date, '-', '_')),...
'Delimiter', '\t', 'WriteRowNames', true, 'WriteVariableNames', true)
%% Pairwise dependency...
% Gene-pair combinations...
bmPairs = nchoosek(1:q, 2); nPairsBM = size(bmPairs, 1);
bmPairList = strrep(cellstr([num2str(bmPairs(:, 1)), repmat('-', [nPairsBM, 1]), num2str(bmPairs(:, 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')';
ycorrmat4 = corr(Ydata4, 'type', 'spearman'); ycorr4 = 1 - squareform(1 - ycorrmat4, 'tovector')';
ycorr_struct = [corr(ycorr1, ycorr2, 'type', 'pearson'), corr(ycorr1, ycorr3, 'type', 'pearson'),...
corr(ycorr1, ycorr4, 'type', 'pearson'), corr(ycorr2, ycorr3, 'type', 'pearson'),...
corr(ycorr2, ycorr4, 'type', 'pearson'), corr(ycorr3, ycorr4, 'type', 'pearson')]';
ycorr_struct = [ycorr_struct, round(ycorr_struct, 2)];
% Biomarkers...
figure(3), clf
subplot(131),
plot(1:nPairsBM, ycorr1, 'rs--', 1:nPairsBM, ycorr2, 'rd--', 1:nPairsBM, ycorr3, 'go--', 1:nPairsBM, ycorr4, 'bo--')
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({'\bfTCGA-LUAD', '\bfTCGA-LUSC', '\bfCCLE', '\bfGDSC'})
title([{'Correlation vector plot: {\color[rgb]{0,0,0.8}NSCLC}'};
{['({\it\color[rgb]{0.5,0,0}r_{TT} = ', num2str(ycorr_struct(1,2)), ', r_{TC} = ', num2str(ycorr_struct(2,2)),...
', r_{TG} = ', num2str(ycorr_struct(3,2)), ', r_{CG} = ', num2str(ycorr_struct(6,2)), '})']}])
subplot(232), scatter(ycorr1, ycorr2, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfTCGA-LUAD'), ylabel('\bfTCGA-LUSC')
title('TCGA-LUAD vs. TCGA-LUSC')
subplot(233), scatter(ycorr1, ycorr3, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfTCGA-LUAD'), ylabel('\bfCCLE-NSCLC')
title('TCGA-LUAD vs. CCLE-NSCLC')
subplot(235), scatter(ycorr1, ycorr4, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfTCGA-LUAD'), ylabel('\bfGDSC-NSCLC')
title('TCGA-LUAD vs. GDSC-NSCLC')
subplot(236), scatter(ycorr3, ycorr4, 24, 'bo', 'filled'), box on
xticks(-1:0.2:1), yticks(-1:0.2:1), xlabel('\bfCCLE'), ylabel('\bfGDSC')
title('CCLE vs. GDSC [{\color[rgb]{0,0,0.5}NSCLC}]')
suptitle('\bfComparison of Gene-pair Correlations for {\color[rgb]{0.5,0,0}NSCLC} Biomarkers')
%%%
%% 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);
Zpmf4 = array2table(zeros(nPairsBM, 4), 'VariableNames', Zstates);
Th = [0.25, 0.75]; % Distribution tail quantiles
asc_met = 'NMI'; % Association metric
YpairLo_asc = NaN(nPairsBM, 4); YpairUp_asc = NaN(nPairsBM, 4);
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)];
Ypair4 = norm01(Ydata4(:, bmPairs(b, :))); Ypair_pdf4 = [ksdensity(Ypair4(:, 1), pts), ksdensity(Ypair4(:, 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))];
ot4 = [otsuthresh(Ypair_pdf4(:, 1)), otsuthresh(Ypair_pdf4(:, 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)];
z4 = ~(Ypair4 <= ot4); Zpmf4{b, :} = [mean(sum(z4 == [0, 0], 2) == 2), mean(sum(z4 == [0, 1], 2) == 2),...
mean(sum(z4 == [1, 0], 2) == 2), mean(sum(z4 == [1, 1], 2) == 2)];
% Distribution tail threshold quantiles...
qt1 = quantile(Ypair1, Th); qt2 = quantile(Ypair2, Th); qt3 = quantile(Ypair3, Th); qt4 = quantile(Ypair4, 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), :);
YpairLo4 = Ypair4((sum(Ypair4 < qt4(1, :), 2) == 2), :); YpairHi4 = Ypair4((sum(Ypair4 > qt4(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(YpairLo4) > 2, YpairLo_asc(b, 4) = corr(YpairLo4(:, 1), YpairLo4(:, 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
if numel(YpairHi4) > 2, YpairUp_asc(b, 4) = corr(YpairHi4(:, 1), YpairHi4(:, 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(YpairLo4) > 2, YpairLo_asc(b, 4) = MutualInformation(YpairLo4, '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
if numel(YpairHi4) > 2, YpairUp_asc(b, 4) = MutualInformation(YpairHi4, 'single', 'corr'); end
defn = 'Normalized MI';
end
% Printing results...
if ~mod(b, 10)
pmfDiff = array2table(round([Zpmf1{b, :} - Zpmf2{b, :}; Zpmf1{b, :} - Zpmf3{b, :}; Zpmf1{b, :} - Zpmf4{b, :};...
Zpmf2{b, :} - Zpmf3{b, :}; Zpmf2{b, :} - Zpmf4{b, :}; Zpmf3{b, :} - Zpmf4{b, :}], 4),...
'RowNames', {'LA-LS', 'LA-C', 'LA-G', 'LS-C', 'LS-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', {'TCGA_LUAD', 'TCGA_LUSC', '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(4), 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(['\bf', biomarkers{bmPairs(b, 1)}]), ylabel(['\bf', biomarkers{bmPairs(b, 2)}])
title('\bfPairwise Biomarker Scatter: {\color[rgb]{0,0,0.8}TCGA-LUAD}', 'Position', [0.4, 1.35, 0], 'FontSize', 14)
set(ax1, 'visible', 'off'), set(ax2, 'visible', 'off')
figure(5), 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(['\bf', biomarkers{bmPairs(b, 1)}]), ylabel(['\bf', biomarkers{bmPairs(b, 2)}])
title('\bfPairwise Biomarker Scatter: {\color[rgb]{0,0,0.8}TCGA-LUSC}', 'Position', [0.4, 1.35, 0], 'FontSize', 14)
set(ax1, 'visible', 'off'), set(ax2, 'visible', 'off')
figure(6), 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(['\bf', biomarkers{bmPairs(b, 1)}]), ylabel(['\bf', 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')
figure(7), 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, Ypair4(:, 2), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax1, [-90, 90]), hold on
histogram(ax2, Ypair4(:, 1), numBin, 'Normalization', 'pdf', 'FaceColor', [0.8, 0.5, 0]), view(ax2, [0, 90]), hold on
scatter(ax3, Ypair4(:, 1), Ypair4(:, 2), 18, 'filled', 'o'), box on
hold on, line(ax3, [0, 1], [ot4(2), ot4(2)], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt4(1, 2), qt4(1, 2)], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt4(1, 1), qt4(1, 1)], [0, 1], 'color', [0.4, 0.6, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [ot4(1), ot4(1)], [0, 1], 'color', 'r', 'linewidth', 2, 'linestyle', '--')
hold on, line(ax3, [0, 1], [qt4(2, 2), qt4(2, 2)], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
hold on, line(ax3, [qt4(2, 1), qt4(2, 1)], [0, 1], 'color', [0.6, 0.4, 0], 'linewidth', 1.5, 'linestyle', '--')
legend('\bf\itData', '\bfTh_{OTSU}', '\bfTh_{LO}', '\bfTh_{UP}')
xlabel(['\bf', biomarkers{bmPairs(b, 1)}]), ylabel(['\bf', 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')
%%%
% Linear model fit...
model13 = {fitlm(Zpmf1{:, 1}, Zpmf3{:, 1}), fitlm(Zpmf1{:, 2}, Zpmf3{:, 2}),...
fitlm(Zpmf1{:, 3}, Zpmf3{:, 3}), fitlm(Zpmf1{:, 4}, Zpmf3{:, 4})};
model14 = {fitlm(Zpmf1{:, 1}, Zpmf4{:, 1}), fitlm(Zpmf1{:, 2}, Zpmf4{:, 2}),...
fitlm(Zpmf1{:, 3}, Zpmf4{:, 3}), fitlm(Zpmf1{:, 4}, Zpmf4{:, 4})};
model23 = {fitlm(Zpmf2{:, 1}, Zpmf3{:, 1}), fitlm(Zpmf2{:, 2}, Zpmf3{:, 2}),...
fitlm(Zpmf2{:, 3}, Zpmf3{:, 3}), fitlm(Zpmf2{:, 4}, Zpmf3{:, 4})};
model24 = {fitlm(Zpmf2{:, 1}, Zpmf4{:, 1}), fitlm(Zpmf2{:, 2}, Zpmf4{:, 2}),...
fitlm(Zpmf2{:, 3}, Zpmf4{:, 3}), fitlm(Zpmf2{:, 4}, Zpmf4{:, 4})};
w13 = [model13{1}.Coefficients.Estimate, model13{2}.Coefficients.Estimate,...
model13{3}.Coefficients.Estimate, model13{4}.Coefficients.Estimate];
w14 = [model14{1}.Coefficients.Estimate, model14{2}.Coefficients.Estimate,...
model14{3}.Coefficients.Estimate, model14{4}.Coefficients.Estimate];
w23 = [model23{1}.Coefficients.Estimate, model23{2}.Coefficients.Estimate,...
model23{3}.Coefficients.Estimate, model23{4}.Coefficients.Estimate];
w24 = [model24{1}.Coefficients.Estimate, model24{2}.Coefficients.Estimate,...
model24{3}.Coefficients.Estimate, model24{4}.Coefficients.Estimate];
Wm1 = [mean([w13(1, :); w14(1, :)], 1); mean([w13(2, :); w14(2, :)], 1)]; % Mean coefficient
xval1 = linspace(0, round(max(max(Zpmf1{:, :})), 1), 100)'; Xval1 = [ones(size(xval1)), xval1];
yfit1 = [Xval1 * Wm1(:, 1), Xval1 * Wm1(:, 2), Xval1 * Wm1(:, 3), Xval1 * Wm1(:, 4)]; % Fitted line
Wm2 = [mean([w23(1, :); w24(1, :)], 1); mean([w23(2, :); w24(2, :)], 1)]; % Mean coefficient
xval2 = linspace(0, round(max(max(Zpmf2{:, :})), 1), 100)'; Xval2 = [ones(size(xval2)), xval2];
yfit2 = [Xval2 * Wm2(:, 1), Xval2 * Wm2(:, 2), Xval2 * Wm2(:, 3), Xval2 * Wm2(:, 4)]; % Fitted line
% Discretized biomarker-pair PMFs...
figure(8), clf
subplot(241), scatter(Zpmf1{:, 1}, Zpmf3{:, 1}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 1}, Zpmf4{:, 1}, 24, 'ro', 'filled'), box on
hold on, plot(xval1, yfit1(:, 1), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUAD - CCLE', '\bfLUAD - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}00')
subplot(242), scatter(Zpmf1{:, 2}, Zpmf3{:, 2}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 2}, Zpmf4{:, 2}, 24, 'ro', 'filled'), box on
hold on, plot(xval1, yfit1(:, 2), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUAD - CCLE', '\bfLUAD - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}01')
subplot(243), scatter(Zpmf1{:, 3}, Zpmf3{:, 3}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 3}, Zpmf4{:, 3}, 24, 'ro', 'filled'), box on
hold on, plot(xval1, yfit1(:, 3), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUAD - CCLE', '\bfLUAD - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}10')
subplot(244), scatter(Zpmf1{:, 4}, Zpmf3{:, 4}, 24, 'bo', 'filled')
hold on, scatter(Zpmf1{:, 4}, Zpmf4{:, 4}, 24, 'ro', 'filled'), box on
hold on, plot(xval1, yfit1(:, 4), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUAD - CCLE', '\bfLUAD - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}11')
subplot(245), scatter(Zpmf2{:, 1}, Zpmf3{:, 1}, 24, 'bo', 'filled')
hold on, scatter(Zpmf2{:, 1}, Zpmf4{:, 1}, 24, 'ro', 'filled'), box on
hold on, plot(xval2, yfit2(:, 1), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUSC - CCLE', '\bfLUSC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}00')
subplot(246), scatter(Zpmf2{:, 2}, Zpmf3{:, 2}, 24, 'bo', 'filled')
hold on, scatter(Zpmf2{:, 2}, Zpmf4{:, 2}, 24, 'ro', 'filled'), box on
hold on, plot(xval2, yfit2(:, 2), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUSC - CCLE', '\bfLUSC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}01')
subplot(247), scatter(Zpmf2{:, 3}, Zpmf3{:, 3}, 24, 'bo', 'filled')
hold on, scatter(Zpmf2{:, 3}, Zpmf4{:, 3}, 24, 'ro', 'filled'), box on
hold on, plot(xval2, yfit2(:, 3), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUSC - CCLE', '\bfLUSC - GDSC', 'Location', 'nw'), title('State = \color[rgb]{0,0,0.8}10')
subplot(248), scatter(Zpmf2{:, 4}, Zpmf3{:, 4}, 24, 'bo', 'filled')
hold on, scatter(Zpmf2{:, 4}, Zpmf4{:, 4}, 24, 'ro', 'filled'), box on
hold on, plot(xval2, yfit2(:, 4), 'g-'), xlabel('\bfTumor Culture'), ylabel('\bfCell line')
legend('\bfLUSC - CCLE', '\bfLUSC - 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}NSCLC}]'])
% Corr. betn. tails...
figure(9), clf
subplot(241), 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('\bfTCGA-LUAD'), ylabel('\bfTCGA-LUSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(242), 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('\bfTCGA-LUAD'), ylabel('\bfCCLE'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(243), scatter(YpairLo_asc(:, 1), YpairLo_asc(:, 4), 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('\bfTCGA-LUAD'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Lower Tails', defn))
subplot(244), scatter(YpairLo_asc(:, 3), YpairLo_asc(:, 4), 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(245), 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('\bfTCGA-LUAD'), ylabel('\bfTCGA-LUSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
subplot(246), 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('\bfTCGA-LUAD'), ylabel('\bfCCLE'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
subplot(247), scatter(YpairUp_asc(:, 1), YpairUp_asc(:, 4), 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('\bfTCGA-LUAD'), ylabel('\bfGDSC'), title(sprintf('{\\color[rgb]{0,0,0.8}%s}: Upper Tails', defn))
subplot(248), scatter(YpairUp_asc(:, 3), YpairUp_asc(:, 4), 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}NSCLC}]')
% Correlation betn. association vectors...
kLo12 = ~(isnan(YpairLo_asc(:, 1)) | isnan(YpairLo_asc(:, 2))); kLo13 = ~(isnan(YpairLo_asc(:, 1)) | isnan(YpairLo_asc(:, 3)));
kLo14 = ~(isnan(YpairLo_asc(:, 1)) | isnan(YpairLo_asc(:, 4))); kLo23 = ~(isnan(YpairLo_asc(:, 2)) | isnan(YpairLo_asc(:, 3)));
kLo24 = ~(isnan(YpairLo_asc(:, 2)) | isnan(YpairLo_asc(:, 4))); kLo34 = ~(isnan(YpairLo_asc(:, 3)) | isnan(YpairLo_asc(:, 4)));
kUp12 = ~(isnan(YpairUp_asc(:, 1)) | isnan(YpairUp_asc(:, 2))); kUp13 = ~(isnan(YpairUp_asc(:, 1)) | isnan(YpairUp_asc(:, 3)));
kUp14 = ~(isnan(YpairUp_asc(:, 1)) | isnan(YpairUp_asc(:, 4))); kUp23 = ~(isnan(YpairUp_asc(:, 2)) | isnan(YpairUp_asc(:, 3)));
kUp24 = ~(isnan(YpairUp_asc(:, 2)) | isnan(YpairUp_asc(:, 4))); kUp34 = ~(isnan(YpairUp_asc(:, 3)) | isnan(YpairUp_asc(:, 4)));
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(kLo14, 1), YpairLo_asc(kLo14, 4), 'type', 'spearman'),...
corr(YpairLo_asc(kLo23, 2), YpairLo_asc(kLo23, 3), 'type', 'spearman'),...
corr(YpairLo_asc(kLo24, 2), YpairLo_asc(kLo24, 4), 'type', 'spearman'),...
corr(YpairLo_asc(kLo34, 3), YpairLo_asc(kLo34, 4), '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(kUp14, 1), YpairUp_asc(kUp14, 4), 'type', 'spearman'),...
corr(YpairUp_asc(kUp23, 2), YpairUp_asc(kUp23, 3), 'type', 'spearman'),...
corr(YpairUp_asc(kUp24, 2), YpairUp_asc(kUp24, 4), 'type', 'spearman'),...
corr(YpairUp_asc(kUp34, 3), YpairUp_asc(kUp34, 4), 'type', 'spearman')], 4),...
'rownames', {'Lower tail'; 'Upper tail'}, 'variablename',...
{'LA_LS', 'LA_C', 'LA_G', 'LS_C', 'LS_G', 'C_G'});
disp(YpairTail_ascStruct)
%% Feature selection - TIME-CONSUMING LOOP!!!...
% RELIEFF gene ranking...
K = 10;
rank1 = zeros(p, q); rank2 = zeros(p, q);
rank3 = zeros(p, q); rank4 = zeros(p, q);
tic
for k = 1 : q
fprintf('Biomarker %d: %s\n', k, biomarkers{k})
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');
rank4(:, k) = relieff(Xdata4, Ydata4(:, k), K, 'method', 'regression');
end
FEATFILENAME = 'relieff_ranked_genes_nsclc_TCG.mat';
save(FEATFILENAME, 'rank1', 'rank2', 'rank3', 'rank4')
toc
% Save files...
SavePath = '.\\Data\\';
WriteTableForRanks = @(RankArray, FileName) writetable(array2table(RankArray, 'VariableNames', biomarkers,...
'RowNames', XgeneSet.Hugo_Symbol), [SavePath, FileName],...
'Delimiter', '\t', 'WriteVariableNames', true, 'WriteRowNames', true);
WriteTableForRanks(rank1, sprintf('LUAD_biomarker_ranks_TCGA_%s.txt', strrep(date, '-', '_')))
WriteTableForRanks(rank2, sprintf('LUSC_biomarker_ranks_TCGA_%s.txt', strrep(date, '-', '_')))
WriteTableForRanks(rank3, sprintf('NSCLC_biomarker_ranks_CCLE_%s.txt', strrep(date, '-', '_')))
WriteTableForRanks(rank4, sprintf('NSCLC_biomarker_ranks_GDSC_%s.txt', strrep(date, '-', '_')))
%% Transfer Learning...
% Load RELEIFF features...
FEATFILENAME = 'relieff_ranked_genes_nsclc_TCG.mat';
load(FEATFILENAME, 'rank1', 'rank2', 'rank3', 'rank4')
% Metric function definitions...
norm01 = @(x) (x - min(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);
RSQ = @(y, y_hat) 1 - mean((y - y_hat).^2) / var(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));
qn = 1 : q;
for chosenBMidx = qn
%%% Feature selection...
fprintf(1, 'Chosen biomarker = '), fprintf(2, '%s\n', biomarkers{chosenBMidx})
ranks = [rank1(:, chosenBMidx), rank2(:, chosenBMidx), rank3(:, chosenBMidx), rank4(:, chosenBMidx)];
% Tumor-to-CL TL case...
% fprintf(1, 'Extracting common CL features...\n') % Extracting a common set of size m_opt
nGN = 300; m_opt = 150; gnRank = intersectn(ranks(1:nGN, 3), ranks(1:nGN, 4), 'sorted');
m = numel(gnRank); nI = 0; m0 = m;
while m < m_opt
nI = nI + 1; nGN = nGN + 100;
gnRank = intersectn(ranks(1:nGN, 3), ranks(1:nGN, 4), '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)
% CL-to-Tumor TL case...
% fprintf(1, 'Extracting common tumor culture features...\n') % Extracting a common set of size m_opt
nGN = 300; m_opt = 150; gnRank_rev = intersectn(ranks(1:nGN, 1), ranks(1:nGN, 2), 'sorted');
m_rev = numel(gnRank_rev); nI = 0; m0_rev = m_rev;
while m_rev < m_opt
nI = nI + 1; nGN = nGN + 100;
gnRank_rev = intersectn(ranks(1:nGN, 1), ranks(1:nGN, 2), 'sorted'); m_rev = numel(gnRank_rev);
end
% fprintf(1, '\t#genes used = %d, #Iterations = %d\n\tInitial size = %d, Final size = %d\n', nGN, nI, m0_rev, m_rev)
% Defining primary & secondary sets...
fprintf(1, 'Tumor cultures to cell line data Transfer Learning...\n')
dsChoice = 1;
switch dsChoice
case 1 % Tumor => primary, CL => secondary
dsFlag = {'TCGA-NSCLC', 'CCLE + GDSC'};
gnList = XgeneSet.Hugo_Symbol(gnRank); m = numel(gnList);
X1 = [Xdata1(:, gnRank); Xdata2(:, gnRank)]; X2 = [Xdata3(:, gnRank); Xdata4(:, gnRank)];
Y1 = [(Ydata1(:, chosenBMidx)); (Ydata2(:, chosenBMidx))];
Y2 = [(Ydata3(:, chosenBMidx)); (Ydata4(:, chosenBMidx))];
case 2 % CL => primary, Tumor => secondary
dsFlag = {'CCLE + GDSC', 'TCGA-NSCLC'};
gnList = XgeneSet.Huwgo_Symbol(gnRank_rev); m = numel(gnList);
X1 = [Xdata3(:, gnRank_rev); Xdata4(:, gnRank_rev)]; X2 = [Xdata1(:, gnRank_rev); Xdata2(:, gnRank_rev)];
Y1 = [(Ydata3(:, chosenBMidx)); (Ydata4(:, chosenBMidx))];
Y2 = [(Ydata1(:, chosenBMidx)); (Ydata2(:, chosenBMidx))];
case 3 % Tumor => primary, CCLE => secondary
dsFlag = {'TCGA-NSCLC', 'CCLE'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 3)); m = numel(gnList);
X1 = [Xdata1(:, ranks(1:m_opt, 3)); Xdata2(:, ranks(1:m_opt, 3))]; X2 = Xdata3(:, ranks(1:m_opt, 3));
Y1 = [Ydata1(:, chosenBMidx); Ydata2(:, chosenBMidx)]; Y2 = Ydata3(:, chosenBMidx);
case 4 % CCLE => primary, Tumor => secondary
dsFlag = {'CCLE', 'TCGA-NSCLC'};
gnList = XgeneSet.Hugo_Symbol(gnRank_rev); m = numel(gnList);
X1 = Xdata3(:, gnRank_rev); X2 = [Xdata1(:, gnRank_rev); Xdata2(:, gnRank_rev)];
Y1 = Ydata3(:, chosenBMidx); Y2 = [Ydata1(:, chosenBMidx); Ydata2(:, chosenBMidx)];
case 5 % Tumor => primary, GDSC => secondary
dsFlag = {'TCGA-NSCLC', 'GDSC'};
gnList = XgeneSet.Hugo_Symbol(ranks(1:m_opt, 4)); m = numel(gnList);
X1 = [Xdata1(:, ranks(1:m_opt, 4)); Xdata2(:, ranks(1:m_opt, 4))]; X2 = Xdata4(:, ranks(1:m_opt, 4));
Y1 = [Ydata1(:, chosenBMidx); Ydata2(:, chosenBMidx)]; Y2 = Ydata4(:, chosenBMidx);
case 6 % GDSC => primary, Tumor => secondary
dsFlag = {'GDSC', 'TCGA-NSCLC'};
gnList = XgeneSet.Hugo_Symbol(gnRank_rev); m = numel(gnList);
X1 = Xdata4(:, gnRank_rev); X2 = [Xdata1(:, gnRank_rev); Xdata2(:, gnRank_rev)];
Y1 = Ydata4(:, chosenBMidx); Y2 = [Ydata1(:, chosenBMidx); Ydata2(:, 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('NSCLC_%s_%s', dsChar{3}, dsChar{2});
% filename = sprintf('Data_BM_%d_f%d_%s%s_NSCLC.mat', chosenBMidx, m_opt, dsChar{2}, dsChar{1});
% save(fullfile(datapath, datadir, filename), 'X1', 'X2', 'Y1', 'Y2')
% Distributions...
figure(10)
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}])
%%% Transfer Learning predictions...
[Y1c, X2a, CorrMatCA] = CorrAlignTransLearn(X1, X2, Y2);
[Y1m, Y2m, Map, densityPts, CorrMatDM] = DistMatchTransLearn(X1, Y1, X2, Y2);
%%% Baseline model prediction...
TrainX = zscore(X2); TrainY = Y2; TestX = zscore(X1);
rng(0); nTree = 200; RF = TreeBagger(nTree, TrainX, TrainY, 'method', 'regression');
PredY = predict(RF, TestX); Y1b = PredY; %Y1b(Y1b < 0) = 0; Y1b(Y1b > 1) = 1;
%%% Response distributions...
Hist.Y1 = ksdensity(Y1, densityPts, 'kernel', 'Normal');
Hist.Y2 = ksdensity(Y2, densityPts, 'kernel', 'Normal');
Hist.Y1c = ksdensity(Y1c, densityPts, 'kernel', 'Normal');
Hist.Y1m = ksdensity(Y1m, densityPts, 'kernel', 'Normal');
Hist.Y2m = ksdensity(Y2m, densityPts, 'kernel', 'Normal');
Hist.Y1b = ksdensity(Y1b, densityPts, 'kernel', 'Normal');
%%% Measure performance and display...
clearvars ERR
ERR.Matrix(1, :) = [NRMSE(Y1, Y1m), NMAE(Y1, Y1m), corr(Y1, Y1m, 'type', 'spearman')];
ERR.Matrix(2, :) = [NRMSE(Y1, Y2m), NMAE(Y1, Y2m), corr(Y1, Y2m, 'type', 'spearman')];
ERR.Matrix(3, :) = [NRMSE(Y1, Y1c), NMAE(Y1, Y1c), corr(Y1, Y1c, 'type', 'spearman')];
ERR.Matrix(4, :) = [NRMSE(Y1, Y1b), NMAE(Y1, Y1b), corr(Y1, Y1b, 'type', 'spearman')];
ERR.Table = array2table(round(ERR.Matrix', 4), 'variablenames', PredApp, 'rownames', {'NRMSE', 'NMAE', 'SCC'});
% Prediction histograms...
figure(11), clf
subplot(231), histogram(Y1, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1, 'r-.', 'linewidth', 2), title(sprintf('Target %s Response', dsFlag{1}))
subplot(234), histogram(Y2, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y2, 'r-.', 'linewidth', 2), title(sprintf('%s Response', dsFlag{2}))
subplot(235), histogram(Y1b, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1b, 'r-.', 'linewidth', 2), title(sprintf('%s Model Prediction', dsFlag{2}))
subplot(232), histogram(Y1m, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1m, 'r-.', 'linewidth', 2), title('{\color[rgb]{0,0.3,0.8}DMTL} Prediction')
subplot(233), histogram(Y1c, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1c, 'r-.', 'linewidth', 2), title('{\color[rgb]{0,0.3,0.8}CORAL-TL} Prediction')
suptitle([{sprintf('\\bfPrediction of Tumor Culture Response from Cell line Data, Biomarker = {\\color[rgb]{0,0.5,0.4}%s}',...
biomarkers{chosenBMidx})}; {'{\color[rgb]{0.7,0,0}Red Line}: Kernel Density Estimate'}])
% Prediction distributions...
% Histograms...
figure(12), clf
subplot(231), histogram(Y1, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1, 'r-.', 'linewidth', 2), title(sprintf('Target %s Response', dsFlag{1}))
subplot(234), histogram(Y2, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y2, 'r-.', 'linewidth', 2), title(sprintf('%s Response', dsFlag{2}))
subplot(236), histogram(Y1b, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1b, 'r-.', 'linewidth', 2), title(sprintf('%s Model Prediction', dsFlag{2}))
subplot(232), histogram(Y2m, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y2m, 'r-.', 'linewidth', 2), title('TL_{SS} Prediction')
subplot(233), histogram(Y1m, numBin, 'normalization', 'pdf')
hold on, plot(densityPts, Hist.Y1m, 'r-.', 'linewidth', 2), title('TL Prediction')
suptitle([{sprintf('\\bfComparison of Predictions for Tumor data, Biomarker = {\\color[rgb]{0,0.5,0.4}%s}',...
biomarkers{chosenBMidx})}; {'{\color[rgb]{0.5,0,0}Red Line}: Kernel density estimate'}])
%%% Complete Tables...
RESULTS.NRMSE{chosenBMidx, :} = ERR.Table{1, :}; RESULTS.NMAE{chosenBMidx, :} = ERR.Table{2, :};
RESULTS.SCC{chosenBMidx, :} = ERR.Table{3, :}; RESULTS.numFeat(chosenBMidx) = m;
% fprintf('\n')
% disp(array2table(round([RESULTS.NRMSE{chosenBMidx, :}; RESULTS.NMAE{chosenBMidx, :};...
% RESULTS.SCC{chosenBMidx, :}], 4), 'RowNames', {'NRMSE', 'NMAE', 'SCC'},...
% 'VariableNames', PredApp))
end
RESULTS.NRMSE{end, :} = mean(RESULTS.NRMSE{qn, :}, 1);
RESULTS.NMAE{end, :} = mean(RESULTS.NMAE{qn, :}, 1);
RESULTS.SCC{end, :} = mean(RESULTS.SCC{qn, :}, 1);
RESULTS.numFeat(end) = mean(RESULTS.numFeat(qn));
fprintf(2, 'Mean performance over %d biomarkers...\n', length(qn))
fprintf(1, '\tm_opt = %d, m_avg = %d\n', m_opt, round(RESULTS.numFeat(end)))
RESULTS.summary = array2table(round([RESULTS.NRMSE{end, :}; RESULTS.NMAE{end, :}; RESULTS.SCC{end, :}], 4),...
'RowNames', {'NRMSE', 'NMAE', 'SCC'}, 'VariableNames', PredApp);
disp(RESULTS.summary)