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Figures/Fig3/fig3.m

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+clc; clear; close all; load('all_threshold_perf.mat');
+%% small
+warning('off');
+clc; close all; clearvars -except total_AUPRC total_AUROC
+n = 6;
+
+x = linspace(0,5,100);
+figure(1); subplot(121);
+h = stdshade(total_AUROC(:,1:n)',0.15,'b',x);
+xlim([0 5]);
+h.LineWidth = 3;
+title('AUROC','FontSize',16);
+xlabel('\alpha','FontSize',14);
+ylabel('AUROC','FontSize',14);
+set(gca,'fontsize',14);
+
+
+figure(1);subplot(122);
+h = stdshade(total_AUPRC(:,1:n)',0.15,'r',x);
+h.LineWidth = 3;
+xlim([0 5]);
+title('AUPR','FontSize',16);
+xlabel('\alpha','FontSize',14);
+ylabel('AUPR','FontSize',14);
+set(gca,'fontsize',14);
+sgt = sgtitle('100 Neurons');
+sgt.FontSize = 20;
+sgt.FontWeight = 'bold';
+%% normal
+x = linspace(0,5,100);
+figure(2); subplot(121);
+h = stdshade(total_AUROC(:,n+1:end)',0.15,'b',x);
+xlim([0 5]);
+h.LineWidth = 3;
+title('AUROC','FontSize',16);
+xlabel('\alpha','FontSize',14);
+ylabel('AUROC','FontSize',14);
+set(gca,'fontsize',14);
+
+
+figure(2);subplot(122);
+h = stdshade(total_AUPRC(:,n+1:end)',0.15,'r',x);
+h.LineWidth = 3;
+xlim([0 5]);
+title('AUPR','FontSize',16);
+xlabel('\alpha','FontSize',14);
+ylabel('AUPR','FontSize',14);
+set(gca,'fontsize',14);
+sgt = sgtitle('1000 Neurons');
+sgt.FontSize = 20;
+sgt.FontWeight = 'bold';

Figures/Fig3/stdshade.m

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+function [lineOut, fillOut] = stdshade(amatrix,alpha,acolor,F,smth)
+% usage: stdshading(amatrix,alpha,acolor,F,smth)
+% plot mean and sem/std coming from a matrix of data, at which each row is an
+% observation. sem/std is shown as shading.
+% - acolor defines the used color (default is red) 
+% - F assignes the used x axis (default is steps of 1).
+% - alpha defines transparency of the shading (default is no shading and black mean line)
+% - smth defines the smoothing factor (default is no smooth)
+% smusall 2010/4/23
+if exist('acolor','var')==0 || isempty(acolor)
+    acolor='r'; 
+end
+if exist('F','var')==0 || isempty(F)
+    F=1:size(amatrix,2);
+end
+if exist('smth','var'); if isempty(smth); smth=1; end
+else smth=1; %no smoothing by default
+end  
+if ne(size(F,1),1)
+    F=F';
+end
+amean = nanmean(amatrix,1); %get man over first dimension
+if smth > 1
+    amean = boxFilter(nanmean(amatrix,1),smth); %use boxfilter to smooth data
+end
+astd = nanstd(amatrix,[],1); % to get std shading
+astd = (astd./sqrt(size(amatrix,1))); % SEM
+% astd = nanstd(amatrix,[],1)/sqrt(size(amatrix,1)); % to get sem shading
+if exist('alpha','var')==0 || isempty(alpha) 
+    fillOut = fill([F fliplr(F)],[amean+astd fliplr(amean-astd)],acolor,'linestyle','none');
+    acolor='k';
+else
+    fillOut = fill([F fliplr(F)],[amean+astd fliplr(amean-astd)],acolor, 'FaceAlpha', alpha,'linestyle','none');
+end
+if ishold==0
+    check=true; else check=false;
+end
+hold on;
+lineOut = plot(F,amean,'Color',acolor,'linewidth',1.5); %% change color or linewidth to adjust mean line
+if check
+    hold off;
+end
+end
+function dataOut = boxFilter(dataIn, fWidth)
+% apply 1-D boxcar filter for smoothing
+fWidth = fWidth - 1 + mod(fWidth,2); %make sure filter length is odd
+dataStart = cumsum(dataIn(1:fWidth-2),2);
+dataStart = dataStart(1:2:end) ./ (1:2:(fWidth-2));
+dataEnd = cumsum(dataIn(length(dataIn):-1:length(dataIn)-fWidth+3),2);
+dataEnd = dataEnd(end:-2:1) ./ (fWidth-2:-2:1);
+dataOut = conv(dataIn,ones(fWidth,1)/fWidth,'full');
+dataOut = [dataStart,dataOut(fWidth:end-fWidth+1),dataEnd];
+end

Figures/Fig4/Fig4.m

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+clear; clc; close all;
+
+load('all_perf_eval.mat');
+%%
+AUROC_100 = [F_AUROC(1:6,:),S_AUROC(1:6,:)];
+AUROC_1000 = [F_AUROC(7:end,:),S_AUROC(7:end,:)];
+
+AUPRC_100 = [F_AUPRC(1:6,:),S_AUPRC(1:6,:)];
+AUPRC_1000 = [F_AUPRC(7:end,:),S_AUPRC(7:end,:)];
+%%
+close all;
+color = ['b' 'r'];
+figure(1);
+subplot(1,2,1);
+b = boxplot(AUROC_100,'whisker',1000,'Symbol','','Labels',{'FARCI','Sutera et al.'}); ylim([0.7 1]); yticks([0.7 0.8 0.9 1]); box off; 
+title('100 Neurons','FontSize',14);
+uistack(b,'top');
+set(gca,'fontsize',14);
+
+h = findobj(gca,'Tag','Box'); 
+for j=1:size(h,1) 
+patch(get(h(j),'XData'),get(h(j),'YData'),color(j),'FaceAlpha',.5);
+uistack(h(j),'top');
+end 
+
+
+hAx=gca;                                   % retrieve the axes handle
+xtk=hAx.XTick; 
+hold on
+for i = 1:6
+
+    a = plot(xtk,AUROC_100(i,:),'--','Color',[0.75 0.75 0.75]);
+
+end
+
+uistack(b,'top');
+subplot(1,2,2);
+b = boxplot(AUROC_1000,'whisker',1000,'Symbol','','Labels',{'FARCI','Sutera et al.'}); ylim([0.7 1]); box off; set(gca,'ytick',[]); 
+title('1000 Neurons','FontSize',14);
+set(gca,'fontsize',14);
+
+h = findobj(gca,'Tag','Box'); 
+for j=1:size(h,1) 
+patch(get(h(j),'XData'),get(h(j),'YData'),color(j),'FaceAlpha',.5);
+end 
+
+hAx=gca;                                   % retrieve the axes handle
+xtk=hAx.XTick; 
+hold on
+for i = 1:10
+
+    a = plot(xtk,AUROC_1000(i,:),'--','Color',[0.75 0.75 0.75]);
+%     uistack(a,'bottom');
+end
+
+ha=get(gcf,'Children');
+
+set(ha(1),'position',[0.5 0.1 0.4 0.8]);
+set(ha(2),'position',[0.1 0.1 0.4 0.8]);
+uistack(ha(1),'top');
+sgtitle('AUROC');
+%%
+
+figure(2);
+subplot(1,2,1);
+boxplot(AUPRC_100,'whisker',1000,'Symbol','','Labels',{'FARCI','Sutera et al.'}); ylim([0.2 1]); box off; 
+title('100 Neurons','FontSize',16);
+set(gca,'fontsize',14);
+
+h = findobj(gca,'Tag','Box'); 
+for j=1:size(h,1) 
+patch(get(h(j),'XData'),get(h(j),'YData'),color(j),'FaceAlpha',.5);
+end 
+
+hAx=gca;                                   % retrieve the axes handle
+xtk=hAx.XTick; 
+hold on
+for i = 1:6
+
+    plot(xtk,AUPRC_100(i,:),'--','Color',[0.75 0.75 0.75]);
+
+end
+
+subplot(1,2,2);
+boxplot(AUPRC_1000,'whisker',1000,'Symbol','','Labels',{'FARCI','Sutera et al.'}); ylim([0.2 1]); box off; set(gca,'ytick',[]); 
+title('1000 Neurons','FontSize',16);
+set(gca,'fontsize',14);
+
+h = findobj(gca,'Tag','Box'); 
+for j=1:size(h,1) 
+patch(get(h(j),'XData'),get(h(j),'YData'),color(j),'FaceAlpha',.5);
+end 
+
+hAx=gca;                                   % retrieve the axes handle
+xtk=hAx.XTick; 
+hold on
+for i = 1:10
+
+    plot(xtk,AUPRC_1000(i,:),'--','Color',[0.75 0.75 0.75]);
+
+end
+
+ha=get(gcf,'Children');
+set(ha(1),'position',[0.5 0.1 0.4 0.8]);
+set(ha(2),'position',[0.1 0.1 0.4 0.8]);
+
+sgtitle('AUPR');

Figures/Fig5/Fig5AB.m

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+clear; clc; close all;
+
+load('subsampling_results (normal1).mat');
+%% AUROC
+x = sub_size;
+y1 = F_AUROC;
+figure;
+xi = linspace(min(x), max(x), 150);                  
+yi = interp1(x, y1, xi, 'spline', 'extrap');
+p1 = plot(xi, yi, 'LineWidth',3);
+hold on
+xticks([200 400 600 800 1000]);
+
+%
+
+y2 = S_AUROC;
+
+xi = linspace(min(x), max(x), 150);                  
+yi = interp1(x, y2, xi, 'spline', 'extrap');
+p2 = plot(xi, yi,'--', 'LineWidth',3);
+
+
+hold on
+% errorbar(x,y1,F_AUROC_err,'.','Color','k');
+% errorbar(x,y2,F_AUROC_err,'.','Color','k');
+%
+
+plot(x, y2, 'o','MarkerEdgeColor',p2.Color,'MarkerFaceColor',p2.Color,'MarkerSize',8);
+plot(x, y1, 'o','MarkerEdgeColor',p1.Color,'MarkerFaceColor',p1.Color,'MarkerSize',8);
+
+title('AUROC','FontSize',16);
+xlabel('Number of subsampled neurons','FontSize',14); 
+ylabel('AUROC','FontSize',14);
+set(gca,'fontsize',14);
+L = legend('FARCI','Sutera et al. (2015)','FontSize',14,'Location','southeast');
+set(L,'Box','off');
+
+%% AUPRC
+x = sub_size;
+y1 = F_AUPRC;
+figure;
+xi = linspace(min(x), max(x), 150);                  
+yi = interp1(x, y1, xi, 'spline', 'extrap');
+p1 = plot(xi, yi, 'LineWidth',3);
+hold on
+xticks([200 400 600 800 1000]);
+yticks([0.2 0.3 0.4 0.5]);
+
+%
+
+y2 = S_AUPRC;
+
+xi = linspace(min(x), max(x), 150);                  
+yi = interp1(x, y2, xi, 'spline', 'extrap');
+p2 = plot(xi, yi,'--', 'LineWidth',3);
+
+
+hold on
+% errorbar(x,y1,F_AUPRC_err,'.','Color','k');
+% errorbar(x,y2,F_AUPRC_err,'.','Color','k');
+%
+
+plot(x, y2, 'o','MarkerEdgeColor',p2.Color,'MarkerFaceColor',p2.Color,'MarkerSize',8);
+plot(x, y1, 'o','MarkerEdgeColor',p1.Color,'MarkerFaceColor',p1.Color,'MarkerSize',8);
+
+title('AUPR','FontSize',16);
+xlabel('Number of subsampled neurons','FontSize',14); 
+ylabel('AUPR','FontSize',14);
+set(gca,'fontsize',14);
+L = legend('FARCI','Sutera et al. (2015)','FontSize',14,'Location','southeast');
+set(L,'Box','off');
+

Figures/Fig5/Fig5CD.m

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+clear; clc; close all;
+addpath('all_subsamplings');
+%% AUROC
+clc; close all; clear;
+x = [50 200 400 600 800 1000];
+y = [];
+y1_all = [];
+z = [];
+for i = 1:10
+    filename = strcat('Normal',num2str(i));
+    load(filename);
+
+    y1 = mean(total_AUROC,2);
+    y1 = y1(end:-1:1);
+    xi = linspace(min(x), max(x), 950);                  
+%     yi = interp1(x, y1, xi, 'spline', 'extrap');
+    yi = interp1(x, y1, xi,'pchip');
+    y = [y, yi'];
+    y1_all = [y1_all,y1];
+    z = [z, total_AUROC];
+end
+y1_all = y1_all';
+labels = {'50';'200';'400';'600';'800';'1000'};
+
+figure(1);
+ax = violinplot(y1_all,labels);
+title('AUROC','FontSize',16);
+xlabel('Number of subsampled neurons','FontSize',14);
+ylabel('AUROC','FontSize',14);
+set(gca,'fontsize',14);
+box on
+%% AUPR
+clc; close all; clear;
+x = [50 200 400 600 800 1000];
+y = [];
+y1_all = [];
+for i = 1:10
+    filename = strcat('Normal',num2str(i));
+    load(filename);
+
+    y1 = mean(total_AUPRC,2);
+    y1 = y1(end:-1:1);
+    xi = linspace(min(x), max(x), 950);                  
+%     yi = interp1(x, y1, xi, 'spline', 'extrap');
+    yi = interp1(x, y1, xi,'pchip');
+    y = [y, yi'];
+    y1_all = [y1_all,y1];
+
+end
+y1_all = y1_all';
+labels = {'50';'200';'400';'600';'800';'1000'};
+
+figure(2);
+ax = violinplot(y1_all,labels);
+title('AUPR','FontSize',16);
+xlabel('Number of subsampled neurons','FontSize',14);
+ylabel('AUPR','FontSize',14);
+set(gca,'fontsize',14);
+box on