2024_Hasegawa_Huang_et_al_CODE/Supplementary/figS3/figS3B_BarChart.m

%% fig. S3B - BarChart
clear all
close all
% ==========================================================================
%% Sensory responsive cell barchart
% ==========================================================================
%Prepare variables
load('figS3B_BarChart.mat');
NofMouse = 8;

% ============================================================================================
%% Proportion of cells from each mouse for each cluster - Bar chart (mean)+ SEM - For figures
% ============================================================================================
for iLearning = 1:2 %Initial or Reversal
    RatioData = [];
    Mean4Bar  = [];
    SEM4Bar   = [];
    
    switch iLearning
        case 1 %Initial Learning
            fig1 = figure('Name', 'InitialLearning-GoNogoCellRatio');
            RatioData = CellRatio_Initial;
            title('Proportion of cell types - Go - Ini');
            hold on;
            
            Mean4Bar = [mean(RatioData{1,iLearning}(:,:),1); mean(RatioData{1,iLearning+1}(:,:),1)];
            SEM4Bar  = [std(RatioData{1,iLearning}(:,:),0,1)/sqrt(NofMouse); std(RatioData{1,iLearning+1}(:,:),0,1)];
            
        case 2 %Reversal Learning
            fig2 = figure('Name', 'ReversalLearning-GoNogoCellRatio');
            RatioData = CellRatio_Reversal;
            title('Proportion of cell types - Go - Rev');
            hold on;
            
            Mean4Bar = [mean(RatioData{1,iLearning-1}(:,:),1); mean(RatioData{1,iLearning}(:,:),1)];
            SEM4Bar  = [std(RatioData{1,iLearning-1}(:,:),0,1)/sqrt(NofMouse); std(RatioData{1,iLearning}(:,:),0,1)];
    end %switch
    
    %Plot bar chart
    b = bar(1:size(Mean4Bar,2), Mean4Bar, 'grouped','FaceColor', 'none', 'EdgeColor', 'k');
    hold on;
    
    % Calculate the number of groups and number of bars in each group
    [nbars,ngroups] = size(Mean4Bar);
    
    % Get the x coordinate of the bars
    x = nan(nbars, ngroups);
    for i = 1:nbars
        x(i,:) = b(i).XEndPoints;
    end
    
    %Plot error bar
    er = errorbar(x, Mean4Bar, SEM4Bar);
    %er is 1x4 struct structure
    for e=1:size(Mean4Bar,2)
        er(1,e).Color = [0 0 0];
        er(1,e).LineStyle = 'none';
        er(1,e).MarkerEdgeColor = 'none';
    end
    xticks([1 2 3 4]);
    xticklabels({'Go', 'Nogo', 'Both', 'Non'});
    box off
    
    ylim([0 0.8]);
    yticks([0 0.2 0.4 0.6 0.8 1.0]);
    ylabel('Proportion of cells')
    
    %Add scatter plot--------------------------------------------------------------------------
    if iLearning == 1 %First 4 mice are auditory reward group. The last 2 mice are visual reward group.
        Group1Color = [1 0 0];
        Group2Color = [0 1 0];
        disp('DotColor: Group1(Aud-1st) is Red, Group2(Vis-1st) is Green')
        
    elseif iLearning == 2 %First 4 mice are visual reward group. The last 2 mice are auditory reward group.
        Group1Color = [0 1 0];
        Group2Color = [1 0 0];
        disp('DotColor: Group1(Aud-1st) is Green, Group2(Vis-1st) is Red')
    end
    
    for ii = 1:2 %1 = Naive, 2 = Expert
        %Group1 - Aud-reward 1st
        Group1Cluster = [];
        Group1Cluster = RatioData{1,ii}(1:4, :);
        sz = 20;
        ScatterID = repmat(x(ii,:), size(Group1Cluster,1),1);
        scatter(ScatterID(:),Group1Cluster(:), [], Group1Color, 'filled','MarkerFaceAlpha',0.5','jitter','on','jitterAmount',0.075);
        
        %Group2 - Vis-reward 1st
        Group2Cluster = [];
        Group2Cluster = RatioData{1,ii}(5:8, :);
        ScatterID = repmat(x(ii,:), size(Group2Cluster,1),1);
        scatter(ScatterID(:),Group2Cluster(:), [], Group2Color, 'filled','MarkerFaceAlpha',0.5','jitter','on','jitterAmount',0.075);
    end% ii
end %for iLearning