write_compare.m

function tbl = write_compare(fid, stats_tbl, sum_tbl, vname, title, regions,types)
    sgrp = cellfun(@(x,y)([x ':' y]), stats_tbl.region, stats_tbl.type, 'UniformOutput',false);
    [p,tbl,stats] = kruskalwallis(stats_tbl.(vname), sgrp, 'off');
    [c,~,~,gn] = multcompare(stats, 'CType','dunn-sidak', 'Display','off');
    
    m = size(c,1);
    pt = zeros(m,1);
    for i=1:m
        bga = strcmp(sgrp, gn{c(i,1)});
        bgb = strcmp(sgrp, gn{c(i,2)});
        
        pt(i) = 1 - (1-ranksum(stats_tbl.(vname)(bga),stats_tbl.(vname)(bgb)))^m;
    end

    gt = {};
    midx = [];

    %% Assumed regions/types (to get proper sorting)
%     regions = {'Mesoderm';'Ectoderm'};
%     types = {'Control';'VEGFR Inhibition'};
    for i=1:length(regions)
        for j=1:(length(types)-1)
            t2 = types((j+1):end);
            np = cellfun(@(x)({[regions{i} ':' types{j}], [regions{i} ':' x]}), t2, 'UniformOutput',false);
            
            gt = [gt; vertcat(np{:})];
        end
    end

    for i=1:length(types)
        for j=1:(length(regions)-1)
            r2 = regions((j+1):end);
            np = cellfun(@(x)({[regions{j} ':' types{i}], [x ':' types{i}]}), r2, 'UniformOutput',false);
            
            gt = [gt; vertcat(np{:})];
        end
    end

    fprintf(fid, '%s\n', title);
    fprintf(fid, 'Kruskal-Wallis, %g\n\n', p);
    
%     fprintf(fid, '\n');
    fprintf(fid, 'Pair-wise (Dunn-Sidak), Difference, P-Value\n');
    for i=1:size(gt,1)
        ia = find(strcmpi(gn,gt{i,1}));
        ib = find(strcmpi(gn,gt{i,2}));
        
        ma = sum_tbl.median(ia);
        mb = sum_tbl.median(ib);
        
        bridx = all((c(:,1:2)==[ia,ib]), 2);
        fprintf(fid, '%s to %s,%f,%g\n', gt{i,1},gt{i,2}, mb-ma, c(bridx,6));
    end
    fprintf(fid, '\n\n');
end