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gensubnet.m
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gensubnet.m
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rng('default');
minleafsize = 5;
eps = 1e-5;
maxiter = 30;
%maxn = 10;
nrep = 1;
%ntrees = 30;
kfold = 5;
ds = gdscnet;
allfeats = readtable('manth/allfeats.csv');
kflnofs = zeros(height(seldrugs), 1);
kflfs = kflnofs;
kfldiff = kflnofs;
fout = fopen(sprintf('subnets/diffkfold_rng_def_%.2f_%d_%d.csv', alpha, maxn, ntrees), 'w');
fprintf(fout, 'No,DRUG,FRACN,FRACP,TPNOFS,FPNOFS,TNNOFS,FNNOFS,TPFS,FPFS,TNFS,FNFS,TPDIFF,FPDIFF,TNDIFF,FNDIFF\n');
for i=1:height(seldrugs)
drug = seldrugs.DRUG_NAME{i};
thaa = [seldrugs.AATH1(i); seldrugs.AATH2(i)];
idxd = find(strcmpi(gdscnet.allDrugs, drug));
idxneg = gdscnet.AAMat(idxd, :) <= thaa(1);
idxpos = gdscnet.AAMat(idxd, :) >= thaa(2);
tbl = readtable(sprintf('manth/ranking_%s.csv', drug));
posfeats = allfeats.FEATURE(strcmpi(allfeats.DRUG_NAME, drug) & strcmp(allfeats.SIGN, '+'));
negfeats = allfeats.FEATURE(strcmpi(allfeats.DRUG_NAME, drug) & strcmp(allfeats.SIGN, '-'));
for j=1:length(posfeats)
gg = posfeats{j};
gg = strsplit(gg, '-');
gg = strjoin(gg(1:end-1), '-');
posfeats{j} = gg;
end
for j=1:length(negfeats)
gg = negfeats{j};
gg = strsplit(gg, '-');
gg = strjoin(gg(1:end-1), '-');
negfeats{j} = gg;
end
mutpgenes = tbl.MUTP;
mutngenes = tbl.MUTN;
cnvpgenes = tbl.CNVP;
if(~iscell(cnvpgenes))
cnvpgenes = cell(0);
end
cnvngenes = tbl.CNVN;
gexpgenes = tbl.GEXP;
gexngenes = tbl.GEXN;
netpgenes = tbl.NETP;
netngenes = tbl.NETN;
mutpgenes = trimlist(mutpgenes, maxn);
mutngenes = trimlist(mutngenes, maxn);
cnvpgenes = trimlist(cnvpgenes, maxn);
cnvngenes = trimlist(cnvngenes, maxn);
gexpgenes = trimlist(gexpgenes, maxn);
gexngenes = trimlist(gexngenes, maxn);
netpgenes = trimlist(netpgenes, maxn);
netngenes = trimlist(netngenes, maxn);
idxnode = ismember(netpgenes, ds.mutGenes) & ~ismember(netpgenes, mutngenes);
mutpgenes = union(mutpgenes, netpgenes(idxnode), 'stable');
idxnode = ismember(netngenes, ds.mutGenes) & ~ismember(netngenes, mutpgenes);
mutngenes = union(mutngenes, netngenes(idxnode), 'stable');
idxnode = ismember(netpgenes, ds.cnvGenes) & ~ismember(netpgenes, cnvngenes);
cnvpgenes = union(cnvpgenes, netpgenes(idxnode), 'stable');
idxnode = ismember(netngenes, ds.cnvGenes) & ~ismember(netngenes, cnvpgenes);
cnvngenes = union(cnvngenes, netngenes(idxnode), 'stable');
idxnode = ismember(netpgenes, ds.dgexGenes) & ~ismember(netpgenes, gexngenes);
gexpgenes = union(gexpgenes, netpgenes(idxnode), 'stable');
idxnode = ismember(netngenes, ds.dgexGenes) & ~ismember(netngenes, gexpgenes);
gexngenes = union(gexngenes, netngenes(idxnode), 'stable');
allgenes = union(mutpgenes, mutngenes);
allgenes = union(allgenes, cnvpgenes);
allgenes = union(allgenes, cnvngenes);
allgenes = union(allgenes, gexpgenes);
allgenes = union(allgenes, gexngenes);
allgenes = union(allgenes, netpgenes);
allgenes = union(allgenes, netngenes);
nall = length(allgenes);
nodes = ismember(net.nodes, allgenes);
[comp, n, subgraph] = subconncomp(net.mat, nodes);
uc = unique(comp);
hh = histc(comp, uc);
[maxcomp, imax] = max(hh);
allgeneslab = allgenes;
for j=1:nall
gg = allgenes{j};
idxg = strcmp(ds.mutGenes, gg);
ssp = sum(sum(ds.mutMat(idxg, idxpos)));
ssn = sum(sum(ds.mutMat(idxg, idxneg)));
msg = '';
if(ssp * ssn > 0)
msg = strcat(msg, sprintf('(%.0f-%.0f)', ssp/sum(idxpos)*100, ssn/sum(idxneg)*100));
else
msg = strcat(msg, '()');
end
idxg = strcmp(ds.cnvGenes, gg);
ssp = sum(sum(ds.cnvMat(idxg, idxpos)~=0));
ssn = sum(sum(ds.cnvMat(idxg, idxneg)~=0));
if(ssp * ssn > 0)
msg = strcat(msg, sprintf('(%.0f-%.0f)', ssp/sum(idxpos)*100, ssn/sum(idxneg)*100));
else
msg = strcat(msg, '()');
end
allgeneslab{j} = strcat(allgenes{j}, msg);
end
% subnet = graph(subgraph, allgeneslab);
% p = plot(subnet);
% p.MarkerSize = 10;
%
% highlight(p, find(ismember(allgenes, posfeats)), 'NodeColor', 'g')
% highlight(p, find(ismember(allgenes, negfeats)), 'NodeColor', 'r')
%
% fig = gcf;
% fig.Position(3:4) = 3*fig.Position(3:4);
% layout(p, 'force');
% dpfeats = strcat(posfeats, '(+) ');
% dnfeats = strcat(negfeats, '(-) ');
% dfeats = strcat(strjoin(dpfeats), ' ', strjoin(dnfeats));
% msg = sprintf('%s, N = %d, MaxC = %d, %s', upper(drug), nall, maxcomp, dfeats);
% disp(msg);
% title(msg);
% saveas(gcf, sprintf('subnets/%s_subnet.png',drug));
% close all hidden;
% Train classification tree without feature selection
featmat = [];
featmat = [featmat; ds.mutMat(:, [find(idxneg) find(idxpos)])];
featmat = [featmat; ds.cnvMat(:, [find(idxneg) find(idxpos)])];
featmat = [featmat; ds.dgexMat(:, [find(idxneg) find(idxpos)])];
alltissues = unique(ds.cellTissues);
tissmat = zeros(length(alltissues), length(ds.cellNames));
for j=1:length(alltissues)
tt = alltissues{j};
tissmat(j, :) = strcmpi(ds.cellNames, tt)';
end
featmat = [featmat; tissmat(:, [find(idxneg) find(idxpos)])];
featmat = featmat';
classtarget = [zeros(sum(idxneg), 1); ones(sum(idxpos), 1)];
pnames = strcat(ds.mutGenes, '-MUT');
pnames = [pnames; strcat(ds.cnvGenes, '-CNV')];
pnames = [pnames; strcat(ds.dgexGenes, '-GEX')];
pnames = [pnames; strcat('tissue_is_', alltissues)];
tpnofs = zeros(nrep, 1);
fpnofs = zeros(nrep, 1);
tnnofs = zeros(nrep, 1);
fnnofs = zeros(nrep, 1);
for j=1:nrep
tt = templateTree('MinLeafSize', minleafsize);
treemdl = fitcensemble(featmat, classtarget, 'Learners', tt, 'Method', 'GentleBoost', 'NumLearningCycles', ntrees, 'CrossVal', 'on', 'KFold', kfold, 'PredictorNames', pnames);
label = kfoldPredict(treemdl);
tpnofs(j) = sum(classtarget == 1 & label == 1);
fpnofs(j) = sum(classtarget == 0 & label == 1);
tnnofs(j) = sum(classtarget == 0 & label == 0);
fnnofs(j) = sum(classtarget == 1 & label == 0);
end
% Train classification tree WITH feature selection
idxmut = ismember(ds.mutGenes, allgenes);
idxcnv = ismember(ds.cnvGenes, allgenes);
idxgex = ismember(ds.dgexGenes, allgenes);
featmat = [];
featmat = [featmat; ds.mutMat(idxmut, [find(idxneg) find(idxpos)])];
featmat = [featmat; ds.cnvMat(idxcnv, [find(idxneg) find(idxpos)])];
featmat = [featmat; ds.dgexMat(idxgex, [find(idxneg) find(idxpos)])];
featmat = [featmat; tissmat(:, [find(idxneg) find(idxpos)])];
featmat = featmat';
classtarget = [zeros(sum(idxneg), 1); ones(sum(idxpos), 1)];
pnames = strcat(ds.mutGenes(idxmut), '-MUT');
pnames = [pnames; strcat(ds.cnvGenes(idxcnv), '-CNV')];
pnames = [pnames; strcat(ds.dgexGenes(idxgex), '-GEX')];
pnames = [pnames; strcat('tissue_is_', alltissues)];
tpfs = zeros(nrep, 1);
fpfs = zeros(nrep, 1);
tnfs = zeros(nrep, 1);
fnfs = zeros(nrep, 1);
for j=1:nrep
tt = templateTree('MinLeafSize', minleafsize);
treemdl = fitcensemble(featmat, classtarget, 'Learners', tt, 'Method', 'GentleBoost', 'NumLearningCycles', ntrees, 'CrossVal', 'on', 'KFold', kfold, 'PredictorNames', pnames);
label = kfoldPredict(treemdl);
tpfs(j) = sum(classtarget == 1 & label == 1);
fpfs(j) = sum(classtarget == 0 & label == 1);
tnfs(j) = sum(classtarget == 0 & label == 0);
fnfs(j) = sum(classtarget == 1 & label == 0);
end
% Classification tree but diffuse mutation + CNV first
% Diffuse mutations
mutgenes = union(mutpgenes, mutngenes);
idxmut = cellfun(@(x) find(ismember(ds.mutGenes, x)), mutgenes);
mutmat = ds.mutMat(idxmut, [find(idxneg) find(idxpos)]);
idxmutnet = cell2mat(values(net.name2node, mutgenes));
adjmat = net.mat(idxmutnet, idxmutnet);
adjmat = single((adjmat + adjmat')>0);
nSample = sum(idxpos)+sum(idxneg);
diffmutMat = netSmooth(mutmat', adjmat, alpha, eps, maxiter)';
diffmutMat = double(diffmutMat >= (1-alpha));
% Diffuse Amplifications
cnvgenes = union(cnvpgenes, cnvngenes);
idxcnv = cellfun(@(x) find(ismember(ds.cnvGenes, x)), cnvgenes);
cnvmat = single(ds.cnvMat(idxcnv, [find(idxneg) find(idxpos)]) > 0); % Find only 1's
idxcnvnet = cell2mat(values(net.name2node, cnvgenes));
adjmat = net.mat(idxcnvnet, idxcnvnet);
adjmat = single((adjmat + adjmat')>0);
diffAmpMat = netSmooth(cnvmat', adjmat, alpha, eps, maxiter)';
% Diffuse Deletions
cnvgenes = union(cnvpgenes, cnvngenes);
idxcnv = cellfun(@(x) find(ismember(ds.cnvGenes, x)), cnvgenes);
cnvmat = single(ds.cnvMat(idxcnv, [find(idxneg) find(idxpos)]) < 0); % Find only -1's
idxcnvnet = cell2mat(values(net.name2node, cnvgenes));
adjmat = net.mat(idxcnvnet, idxcnvnet);
adjmat = single((adjmat + adjmat')>0);
diffDelMat = netSmooth(cnvmat', adjmat, alpha, eps, maxiter)';
diffCNVMat = double(abs(diffAmpMat - diffDelMat) >= (1-alpha)) .* sign((diffAmpMat - diffDelMat));
featmat = [];
featmat = [featmat; diffmutMat];
featmat = [featmat; diffCNVMat];
featmat = [featmat; ds.dgexMat(idxgex, [find(idxneg) find(idxpos)])];
featmat = [featmat; tissmat(:, [find(idxneg) find(idxpos)])];
featmat = featmat';
% Zero/randomise
% featmat = zeros(size(featmat));
pnames = strcat(mutgenes, '-MUT');
pnames = [pnames; strcat(ds.cnvGenes(idxcnv), '-CNV')];
pnames = [pnames; strcat(ds.dgexGenes(idxgex), '-GEX')];
pnames = [pnames; strcat('tissue_is_', alltissues)];
tpdiff = zeros(nrep, 1);
fpdiff = zeros(nrep, 1);
tndiff = zeros(nrep, 1);
fndiff = zeros(nrep, 1);
for j=1:nrep
tt = templateTree('MinLeafSize', minleafsize);
treemdl = fitcensemble(featmat, classtarget, 'Learners', tt, 'Method', 'GentleBoost', 'NumLearningCycles', ntrees, 'CrossVal', 'on', 'KFold', kfold, 'PredictorNames', pnames);
label = kfoldPredict(treemdl);
tpdiff(j) = sum(classtarget == 1 & label == 1);
fpdiff(j) = sum(classtarget == 0 & label == 1);
tndiff(j) = sum(classtarget == 0 & label == 0);
fndiff(j) = sum(classtarget == 1 & label == 0);
end;
fprintf(fout, '%d,%s,%.2f,%.2f,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d\n', ...
i, drug, sum(idxneg)/(sum(idxneg)+sum(idxpos)), sum(idxpos)/(sum(idxneg)+sum(idxpos)), mean(tpnofs), mean(fpnofs), mean(tnnofs), mean(fnnofs), mean(tpfs), mean(fpfs), mean(tnfs), mean(fnfs), mean(tpdiff), mean(fpdiff), mean(tndiff), mean(fndiff));
fprintf('%d,%s,%.2f,%.2f,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d\n', ...
i, drug, sum(idxneg)/(sum(idxneg)+sum(idxpos)), sum(idxpos)/(sum(idxneg)+sum(idxpos)), mean(tpnofs), mean(fpnofs), mean(tnnofs), mean(fnnofs), mean(tpfs), mean(fpfs), mean(tnfs), mean(fnfs), mean(tpdiff), mean(fpdiff), mean(tndiff), mean(fndiff))
end
fclose(fout);