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CEP1_PCA_DBSCAN_refine_GMM_inspect_combined.m
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CEP1_PCA_DBSCAN_refine_GMM_inspect_combined.m
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clear; clc;
save_fold = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures';
dbscan_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\DBSCAN.mat';
load(dbscan_res_file);
bLab{1} = {'001', '002', '005', '016'};
bLab{2} = {'001', '003', '004', '009', '010', '012'};
bLab{3} = {'007', '012', '013', '021', '022', '024'};
bLoc{1} = 'CEP1 Structures/Control CEP1 Structures/Ctrl_CEP1-EGFP_';
bLoc{2} = 'CEP1 Structures/Septin7-Knockdown CEP1 Structures/Sept7-KD_CEP1-EGFP_';
bLoc{3} = 'CEP1 Structures/100uM FCF CEP1 Structures/100uM FCF_CEP1-EGFP_';
phen_data = zeros(size(cl_idx));
cnt = 0;
cnt2 = 0;
for uu = 1:3
ulab = bLab{uu};
for w = 1:length(ulab)
disp([uu,w]);
loc = [bLoc{uu} ulab{w}];
load([loc '.mat']);
ndat = size(features1,1);
sel_indx = filt_vect(cnt+1:cnt+ndat);
ndat2 = sum(sel_indx);
phen_data(cnt2+1:cnt2+ndat2) = uu;
cnt = cnt + ndat;
cnt2 = cnt2 + ndat2;
end
end
cl_idx_comb = cl_idx_sel;
ref_cl_4_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\PCA_DBSCAN_refine_GMM_cluster_4.mat';
ref_cl_3_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\PCA_DBSCAN_refine_GMM_cluster_3.mat';
ref_cl_8_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\PCA_DBSCAN_refine_GMM_cluster_8.mat';
ref_cl_6_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\PCA_DBSCAN_refine_GMM_cluster_6.mat';
ref_cl_7_res_file = 'E:\Septin_structure_analysis\updated_pictures\data7\CEP1_structures\PCA_DBSCAN_refine_GMM_cluster_7.mat';
load(ref_cl_4_res_file);
cl_n = max(unique(sort(cl_idx_comb)));
cnt = 1;
for i = 1:length(cl_idx_comb)
if cl_idx_comb(i) == cl_id_sel
if ref_idx(cnt) == 2
cl_idx_comb(i) = cl_n+1;
end
cnt = cnt+1;
end
end
load(ref_cl_3_res_file);
cl_n = max(unique(sort(cl_idx_comb)));
cnt = 1;
for i = 1:length(cl_idx_comb)
if cl_idx_comb(i) == cl_id_sel
if ref_idx(cnt) == 2
cl_idx_comb(i) = cl_n+1;
end
cnt = cnt+1;
end
end
load(ref_cl_8_res_file);
cl_n = max(unique(sort(cl_idx_comb)));
cnt = 1;
for i = 1:length(cl_idx_comb)
if cl_idx_comb(i) == cl_id_sel
if ref_idx(cnt) == 2
cl_idx_comb(i) = cl_n+1;
end
cnt = cnt+1;
end
end
load(ref_cl_6_res_file);
cl_n = max(unique(sort(cl_idx_comb)));
cnt = 1;
for i = 1:length(cl_idx_comb)
if cl_idx_comb(i) == cl_id_sel
if ref_idx(cnt) == 2
cl_idx_comb(i) = cl_n+1;
end
cnt = cnt+1;
end
end
load(ref_cl_7_res_file);
cl_n = max(unique(sort(cl_idx_comb)));
cnt = 1;
for i = 1:length(cl_idx_comb)
if cl_idx_comb(i) == cl_id_sel
if ref_idx(cnt) == 2
cl_idx_comb(i) = cl_n+1;
end
cnt = cnt+1;
end
end
cell_data = zeros(size(cl_idx_comb));
cnt = 0;
cnt2 = 0;
cell_n = 1;
for uu = 1:3
ulab = bLab{uu};
for w = 1:length(ulab)
disp([uu,w]);
loc = [bLoc{uu} ulab{w}];
load([loc '.mat']);
ndat = size(features1,1);
sel_indx = filt_vect(cnt+1:cnt+ndat);
ndat2 = sum(sel_indx);
cell_data(cnt2+1:cnt2+ndat2) = cell_n;
cnt = cnt + ndat;
cnt2 = cnt2 + ndat2;
cell_n = cell_n + 1;
end
end
cl_num = max(cl_idx_comb);
cell_num = max(cell_data);
dens_data = zeros(cell_num, cl_num);
leg_plts = [];
cm = turbo(3);
fig = figure('Position', [50 50 1500 700]);
hold on;
grid on;
box on;
for uu = 1:3
ulab = bLab{uu};
dens_vals = cell(1, cl_num);
dens_mean = zeros(1, cl_num);
dens_CI_1 = zeros(1, cl_num);
dens_CI_2 = zeros(1, cl_num);
for cl_id = 1:cl_num
for cell_i = unique(cell_data(phen_data == uu))'
struct_i_idx = (phen_data == uu) & (cl_idx_comb == cl_id) & (cell_data == cell_i);
struct_all_idx = (phen_data == uu) & (cell_data == cell_i);
dens_vals{cl_id} = [dens_vals{cl_id}, sum(struct_i_idx)/sum(struct_all_idx)];
end
dens_mean(cl_id) = mean(dens_vals{cl_id});
ts = tinv([0.025 0.975], length(dens_vals{cl_id})-1);
SEM = std(dens_vals{cl_id})/sqrt(length(dens_vals{cl_id}));
dens_CI_1(cl_id) = dens_mean(cl_id) + ts(1)*SEM;
dens_CI_2(cl_id) = dens_mean(cl_id) + ts(2)*SEM;
eb = errorbar(cl_id+uu/15, dens_mean(cl_id), ...
dens_CI_1(cl_id)-dens_mean(cl_id), dens_CI_2(cl_id)-dens_mean(cl_id), ...
'Marker', '.', 'MarkerSize', 20, 'LineStyle', 'none', 'LineWidth', 1, 'Color', cm(uu,:));
leg_plts(uu) = eb;
drawnow;
end
end
xticks([1:cl_num]+1/5);
xticklabels([1:cl_num]);
leg_names = {'CEP1 Structures Control', ...
'CEP1 Structures Septin7-Knockdown', ...
'CEP1 Structures 100uM FCF'};
legend(leg_plts, leg_names);
xlabel('Cluster id');
ylabel('Structure density');
set(gca, 'FontSize', 12);
saveas(fig, fullfile(save_fold, 'PCA_DBSCAN_refine_GMM_comined_statistics.png'));
ylim([0, 0.2]);
saveas(fig, fullfile(save_fold, 'PCA_DBSCAN_refine_GMM_comined_statistics2.png'));
cl_num = max(cl_idx_comb);
cm = jet(cl_num);
cnt = 0;
cnt2 = 0;
fig = figure('Position',get(0,'Screensize'));
for uu = 1:3
ulab = bLab{uu};
cell_ids = unique(cell_data(phen_data == uu));
for w = 1:length(cell_ids)
cell_i = cell_ids(w);
loc = [bLoc{uu} ulab{w}];
load([loc '.mat']);
mkdir([loc '_PCA_DBSCAN_refine_GMM_combined_plot']);
ndat = size(features1,1);
im_obj_ids = unique(sort(Lexcl(Lexcl>0)));
sel_indx = filt_vect(cnt+1:cnt+ndat);
im_obj_sel_ids = im_obj_ids(sel_indx);
ndat2 = sum(sel_indx);
struct_all_idx = (phen_data == uu) & (cell_data == cell_i);
cl_cell_idx_comb = cl_idx_comb(struct_all_idx);
IM_rgb = zeros(size(Lexcl, 1), size(Lexcl, 2), 3);
for cl_id_i = 1:cl_num
im_indx = ismember(Lexcl(:), im_obj_sel_ids(cl_cell_idx_comb == cl_id_i));
IM_r = zeros(size(Lexcl));
IM_g = zeros(size(Lexcl));
IM_b = zeros(size(Lexcl));
IM_r(im_indx) = cm(cl_id_i,1);
IM_g(im_indx) = cm(cl_id_i,2);
IM_b(im_indx) = cm(cl_id_i,3);
%IM_rgb = cat(3, IM_r, IM_g, IM_b);
IM_rgb(:,:,1) = IM_rgb(:,:,1) + IM_r;
IM_rgb(:,:,2) = IM_rgb(:,:,2) + IM_g;
IM_rgb(:,:,3) = IM_rgb(:,:,3) + IM_b;
IM_rgb_i = zeros(size(Lexcl, 1), size(Lexcl, 2), 3);
IM_rgb_i(:,:,1) = IM_r;
IM_rgb_i(:,:,2) = IM_g;
IM_rgb_i(:,:,3) = IM_b;
%IM = zeros(size(Lexcl));
%IM(im_indx) = 1;
clf;
hold on;
axis ij;
axis image;
axis off;
image(IM_rgb_i);
title(strcat('cluster=', num2str(cl_id_i)));
drawnow;
saveas(fig, [loc '_PCA_DBSCAN_refine_GMM_combined_plot', '\cluster=' num2str(cl_id_i) '.png']);
end
clf;
hold on;
axis ij;
axis image;
axis off;
image(IM_rgb);
title('all clusters');
drawnow;
saveas(fig, [loc '_PCA_DBSCAN_refine_GMM_combined_plot', '\all clusters.png']);
cnt = cnt + ndat;
cnt2 = cnt2 + ndat2;
end
end
dens_data = zeros(cell_num, cl_num);
dens_phen = zeros(cell_num, 1);
cell_counter = 1;
for uu = 1:3
ulab = bLab{uu};
cell_ids = unique(cell_data(phen_data == uu));
for w = 1:length(cell_ids)
cell_i = cell_ids(w);
ndat = size(features1,1);
for cl_id = 1:cl_num
struct_i_idx = (phen_data == uu) & (cl_idx_comb == cl_id) & (cell_data == cell_i);
struct_all_idx = (phen_data == uu) & (cell_data == cell_i);
dens_data(cell_counter, cl_id) = sum(struct_i_idx)/sum(struct_all_idx);
end
dens_phen(cell_counter) = uu;
cell_counter = cell_counter + 1;
end
end
f_vals = dens_data;
f_vals = (dens_data - mean(dens_data,1))./std(dens_data,1);
[pcaval, score, latent, ~, explained] = pca(f_vals, 'Algorithm','svd');
explained_cum = cumsum(explained);
f_N = size(f_vals, 2);
p_N = size(f_vals, 1);
pca_sel_n = sum(explained_cum < 90);
fig = figure('Position', [50 50 1000 500]);
hold on;
grid on;
box on;
plot(1:f_N, explained_cum, 'Color', [1 0 0], 'LineWidth', 3);
plot([1, f_N], [90 90], 'Color', [0 0 0], 'LineWidth', 1);
xlim([1 f_N]);
xlabel('Principal component');
ylabel('Cumulative variance explained %');
title({strcat('number of PCs that explain more then 90% of variance:', num2str(pca_sel_n)), ...
'(will be used for clustering with DBSCAN)'});
drawnow;
saveas(fig, fullfile(save_fold, 'PCA_DBSCAN_refine_GMM_comined_cells_structure_density_pca_variance_explained.png'));
c_N = 1000;
cm = turbo(c_N);
c_data = zeros(length(dens_phen), 3);
for i = 1:length(dens_phen)
id = fix(dens_phen(i)/max(dens_phen)*(c_N-1)) + 1;
c_data(i,:) = cm(id,:);
end
phen_ids_1 = find(dens_phen == 1);
phen_ids_2 = find(dens_phen == 2);
phen_ids_3 = find(dens_phen == 3);
sz = 500;
fig = figure('Position', [50 50 800 800]);
hold on;
grid on;
box on;
scatter(score(:,1), score(:,2), 'Marker', 'o', ...
'SizeData', sz, 'CData', c_data, 'LineWidth', 2);
sc1 = scatter(score(phen_ids_1(1),1), score(phen_ids_1(1),2), 'Marker', 'o', ...
'SizeData', sz, 'CData', c_data(phen_ids_1(1),:), 'LineWidth', 2);
sc2 = scatter(score(phen_ids_2(1),1), score(phen_ids_2(1),2), 'Marker', 'o', ...
'SizeData', sz, 'CData', c_data(phen_ids_2(1),:), 'LineWidth', 2);
sc3 = scatter(score(phen_ids_3(1),1), score(phen_ids_3(1),2), 'Marker', 'o', ...
'SizeData', sz, 'CData', c_data(phen_ids_3(1),:), 'LineWidth', 2);
legend([sc1, sc2, sc3], leg_names, 'Location', 'NorthWest');
xlabel('PC1');
ylabel('PC2');
drawnow;
saveas(fig, fullfile(save_fold, 'PCA_DBSCAN_refine_GMM_comined_cells_structure_density_pca_first_two_components.png'));
save(fullfile(save_fold, 'PCA_DBSCAN_refine_GMM_comined.mat'), ...
'phen_data' , 'cl_idx_comb', 'cell_data', 'cl_num', 'cell_num', ...
'dens_data', 'dens_phen');