function conductPCA(od_imgs, scan_reference_values, scan_parameter_values, doPlot, doSave, saveDir)

%% Performs PCA on optical density images, visualizes and optionally saves results.
%
% Inputs:
%   od_imgs               - cell array of OD images
%   scan_reference_values           - array of unique control parameter values
%   scan_parameter_values - array mapping each image to a control parameter
%   doPlot                - logical, true to plot figures
%   doSave                - logical, true to save figures
%   saveDir               - directory to save figures if doSave is true
%
% Requires:
%   +Calculator/computeCumulants.m

    if nargin < 4, doPlot = true; end
    if nargin < 5, doSave = false; end
    if nargin < 6, saveDir = pwd; end
    
    %% PCA computation
    allImgs3D = cat(3, od_imgs{:});
    [Nx, Ny]  = size(allImgs3D(:,:,1));
    Xall      = reshape(allImgs3D, [], numel(od_imgs))';
    [coeff, score, ~, ~, explained] = pca(Xall);
    
    figCount = 1;
    
    %% --- Figure 1: PC1 Image ---
    if doPlot
        pc1_vector = coeff(:,1);
        pc1_image = reshape(pc1_vector, Nx, Ny);
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
        imagesc(pc1_image); axis image off; colormap(Colormaps.coolwarm()); colorbar;
        title(sprintf('First Principal Component (PC1) Image - Explains %.2f%% Variance', explained(1)));
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Image.png')); end
        figCount = figCount + 1;
    end
    
    %% --- Figure 2: PC1 Scores Scatter ---
    if doPlot
        numGroups = numel(scan_reference_values);
        colors = lines(numGroups);
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]); hold on;
        for g = 1:numGroups
            idx = scan_parameter_values == scan_reference_values(g);
            scatter(repmat(scan_reference_values(g), sum(idx),1), score(idx,1), 36, colors(g,:), 'filled');
        end
        xlabel('Control Parameter'); ylabel('PC1 Score');
        title('Evolution of PC1 Scores'); grid on;
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Scatter.png')); end
        figCount = figCount + 1;
    end
    
    %% --- Figure 3: PC1 Distributions ---
    if doPlot
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
        hold on;
        for g = 1:numGroups
            idx = scan_parameter_values == scan_reference_values(g);
            data = score(idx,1);
            histogram(data, 'Normalization', 'pdf', 'FaceColor', colors(g,:), 'FaceAlpha', 0.3);
            [f, xi] = ksdensity(data);
            plot(xi, f, 'Color', colors(g,:), 'LineWidth', 2);
        end
        xlabel('PC1 Score'); ylabel('Probability Density');
        title('PC1 Score Distributions by Group');
        legend(arrayfun(@num2str, scan_reference_values, 'UniformOutput', false), 'Location', 'Best');
        grid on;
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Distributions.png')); end
        figCount = figCount + 1;
    end
    
    %% --- Figure 4: Boxplot of PC1 Scores ---
    if doPlot
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
        boxplot(score(:,1), scan_parameter_values);
        xlabel('Control Parameter'); ylabel('PC1 Score');
        title('PC1 Score Boxplots by Group'); grid on;
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_Boxplot.png')); end
        figCount = figCount + 1;
    end
    
    %% --- Figure 5: Mean ± SEM of PC1 Scores ---
    if doPlot
        meanScores = arrayfun(@(g) mean(score(scan_parameter_values == g,1)), scan_reference_values);
        semScores  = arrayfun(@(g) std(score(scan_parameter_values == g,1))/sqrt(sum(scan_parameter_values == g)), scan_reference_values);
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
        errorbar(scan_reference_values, meanScores, semScores, '-o', 'LineWidth', 2);
        xlabel('Control Parameter'); ylabel('Mean PC1 Score ± SEM');
        title('Mean ± SEM of PC1 Scores'); grid on;
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_MeanSEM.png')); end
        figCount = figCount + 1;
    end
    
    %% --- Figure 6: Binder Cumulant ---
    if doPlot
        binderVals = arrayfun(@(g) ...
            Calculator.computeCumulants(score(scan_parameter_values == g,1)), ...
            scan_reference_values);
        figure(figCount); clf; set(gcf, 'Color', 'w', 'Position', [100 100 950 750]);
        plot(scan_reference_values, binderVals, '-o', 'LineWidth', 2);
        xlabel('Control Parameter'); ylabel('Binder Cumulant (PC1)');
        title('Binder Cumulant of PC1 Scores'); grid on;
        if doSave, saveas(gcf, fullfile(saveDir, 'PC1_BinderCumulant.png')); end
    end
    
    %% --- ANOVA Test ---
    p = anova1(score(:,1), arrayfun(@num2str, scan_parameter_values, 'UniformOutput', false), 'off');
    fprintf('ANOVA p-value for PC1 score differences between groups: %.4e\n', p);

end