%% nchoosek and cumulative probability distribution function ploting % Inputs: ROIs ---------------------------ROI matrix for all cells % ROIsize ------------------------rows columns % T ------------------------------reward or punishment cells % Outputs: CDF figure %% All cells nCells = size(ROIs,2); % define cell number C = nchoosek(1:nCells,2); % pairwise for j = 1:nCells ROIarray = ROIs(:,j); ROImatrix = reshape(ROIarray,ROIsize(1),ROIsize(2)); ROImatrix(ROImatrix>0) = 1; stats = regionprops(ROImatrix,'centroid'); Centroids(j,:) = stats.Centroid; end % pairwise calculation for i = 1:size(C,1) ind1 = C(i,1); ind2 = C(i,2); Distance(i) = norm(Centroids(ind1,:) - Centroids(ind2,:)); % or Distance(i) = pdist([(Centroids(ind1,:);Centroids(ind2,:))],'euclidean'); end figure; Distance = Distance.*1.667; % pixel to micro meter h = cdfplot(Distance); %% Reward cells Centroids1 = Centroids(T==2,:); C1 = nchoosek(1:size(Centroids1,1),2); % pairwise % pairwise calculation for i = 1:size(C1,1) ind1 = C1(i,1); ind2 = C1(i,2); Distance1(i) = norm(Centroids1(ind1,:) - Centroids1(ind2,:)); % or Distance(i) = pdist([(Centroids(ind1,:);Centroids(ind2,:))],'euclidean'); end hold on; Distance1 = Distance1.*1.667; % pixel to micro meter cdfplot(Distance1); %% Punishment cells Centroids2 = Centroids(T==3,:); C2 = nchoosek(1:size(Centroids2,1),2); % pairwise % pairwise calculation for i = 1:size(C2,1) ind1 = C2(i,1); ind2 = C2(i,2); Distance2(i) = norm(Centroids2(ind1,:) - Centroids2(ind2,:)); % or Distance(i) = pdist([(Centroids(ind1,:);Centroids(ind2,:))],'euclidean'); end hold on; Distance2 = Distance2.*1.667; % pixel to micro meter cdfplot(Distance2); xlabel('Pairwise neuronal distance (?m)') ylabel('Cumulative probability') %% Two-sample Kolmogorov-Smirnov test [h1,p1] = kstest2(Distance,Distance1) [h2,p2] = kstest2(Distance,Distance2) % legend('All','reward','punishment','Location','NW') % h.BinWidth = 0.1; % nanmean(NoiseCorrcoef) % save m6 NoiseCorrcoef %% probility density ploting figure; ksdensity(Distance) hold on; ksdensity(Distance1) ksdensity(Distance2)