%% BODIPY Percent Positive Area Quantification

%Author: Jordan Rolsma - University of California, Riverside

%Features:

    %Calculates BODIPY (or other stain) percent positive area
    %Outputs figures with text including area (in px) and percent area 
    %quantification

%Housekeeping
clearvars
close all
clc

%Identifies image files in the working directory
IM_FILE_all = dir(['*.png']);
IM_FILE_all_new = struct2cell(IM_FILE_all);

for file = [1:length(IM_FILE_all_new)] %Number of image files you want Matlab to read in the directory
    
    %File name
    BaseFileName = sprintf('%s', IM_FILE_all_new{1,file}(1:end-4));

    %Read in each file
    I = imread(IM_FILE_all_new{1,file});

    %Filtering
    se = strel('disk',5); %Morphological structuring element
    %Note: Higher numbers are more sensitive, values may vary depending on 
    %your data set, but ensure that the number remains constant across the
    %entire analyzed data set. It is highly recommended to sample files
    %across your entire set to determine an appropriate element value
    %prior to analyzing your entire data set.

    I = imtophat(I,se);

    %Grayscale and binarization
    G = im2gray(I);
    BW = imbinarize(G);
    total{file} = bwarea(BW);

    %Calculate area of image
    imsize = size(I);
    imarea = imsize(:,1)*imsize(:,2);

    %Percentage calculation (two options)
    percent{file} = (total{1,file}./imarea)*100; %Note: this can be used
    %if total cell area is not available AND cells are ~100% confluent.
    %If cell area is available, it is highly recommended to check output 
    %values against the line below to ensure consistency.

    %%%%or

    percent{file} = (total{1,file}./cellarea)*100; %This line is recommended
    %but requires you to have cell area (i.e., you used fixable BODIPY and
    %have fluorescence images defining cell borders or have cell 
    %brightfield images with clearly defined cell borders). If you have 
    %those images available, you can run those files through lines 12-46 to
    %obtain cell area.


    %Figure generation
    figure
    set(gcf,'units','inches','position',[1 1 2 2],'Color','w','PaperSize',[2 2],'PaperPosition',[0 0 2 2])
    haxA = axes('units','inches','position',[0 0 2 2]);
    imagesc(BW)
    colormap(gray)
    set(gca,'visible','off'); axis equal; hold on
    text(0,100,['Area (px):' num2str(total{file})],'color','r','FontSize',12,'FontWeight','bold');
    text(0,200,['Percent:' num2str(percent{file})],'color','r','FontSize',12,'FontWeight','bold');
    hold off
    print(sprintf('%s_quant%d',BaseFileName),'-dpng');

end

%Saves output values for all image files in the directory in a mat file
save(sprintf('20231101_ASCMAHSs1T3_bodipy_AMPK_CC_5uM_14d_quant_data_final.mat'))

%Convert mat to csv (optional, if desired)

%Housekeeping
clc
clear vars
close all

load 20231101_ASCMAHSs1T3_bodipy_AMPK_CC_5uM_14d_quant_data_final.mat

total = length(percent);

for i = 1:total

    percent_bodipy{i} = (percent{i});

end

percent_bodipy_array = cell2mat(percent_bodipy);
avg_percent_bodipy = (sum(percent_bodipy_array))./length(percent_bodipy_array)

%Saves data as a csv file
writetable(cell2table([percent_bodipy avg_percent_bodipy]),'20231101_ASCMAHSs1T3_bodipy_AMPK_CC_5uM_14d_quant_data_final_percentbodipy.csv')