% Input correlations into MATLAB manually as compareACEcorr

%%%% For significance: testing of random sets of same size
% Test 1000 random sets and calculate EC for each for current set
% size
setsizes = [3;6;15;28;45;78;105;210];

MC = zeros(10000000, length(setsizes));

count = 0;
for s = 1:length(setsizes)
    size = setsizes(s)

    count = count+1;
    for trial = 1:10000000
        trial;
        % Select random correlation data
        indices = round(rand(size, 1).*(length(compareACEcorr)-1))+1;
        correlations = compareACEcorr(indices);
          
        % Calculate correlation at top 5% of all TFs and input here:
        D = 0.512;

        % Find the number of pairs in test set with correlation greater
        % than this cutoff
        p = length(find(correlations > D));

        % Motif coherence is the pairs above threshold / total pairs
        MC(trial, count) = p/length(correlations);
    end
end

% Enter experimental MC for each structural class with [set size MC] to
% evaluate significance

expMC = [28	0.071428571
78	0.115384615
105	0.133333333
6	0.166666667
6	0.166666667
210	0.195238095
15	0.2
15	0.2
78	0.282051282
45	0.311111111
3	0.333333333
3	0.333333333
3	0.333333333
3	0.333333333
6	0.5
15	0.533333333
15	0.8

];

% Find pval = fraction of random sets of same set size with MC >
% test set

for i =1:length(expMC(:,1))
    setsize = expMC(i, 1);
    testMC = expMC(i,2);
    randcol = find(setsizes == setsize);
    pval_more(i) = length(find(MC(:,randcol)>=testMC))/10000000;
end

pval_more