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APA_WTR_matching_cells_comptest.m
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figure(5); clf
all_animal_name = {'Hipp18240'};%animals{animalLoop};
pop_sec_res = 60;
cell_sec_res = 3;
pall = cell(2,1);
rng('default')
for aLoop = 1:length(all_animal_name)%:2
animal_name = all_animal_name{aLoop};%animals{animalLoop};
experiment_folder = 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\';
dir_list_fname = '_directory_list.csv';
dir_file = sprintf('%s%s/%s%s', experiment_folder, animal_name, animal_name, dir_list_fname);
% processedDir = sprintf('%s%s/processed_files/', experiment_folder, animal_name);
contourDir = sprintf('%s%s/matching_contours/', experiment_folder, animal_name);
matchingfname = sprintf('%smatching_matrix.mat', contourDir);
DAT_Dir = sprintf('%sDAT_files/', experiment_folder);
AnimalDir = setup_imaging_Sessionfiles(animal_name, dir_file, experiment_folder);%, DAT_Dir, processedDir, contourDir);
nsess = AnimalDir.numSess;
load(matchingfname);
sess_corr1 = NaN(nsess);
% subplot(1,3,1)
colors = viridis(nsess*2);
colors = colors(floor(nsess/2):floor(nsess/2)+nsess-1, :);
light_colors = colors + (1-colors)/3;
numcells2use = [];
sess_corr = NaN(nsess);
pall{aLoop} = NaN(nsess,30);
for s1 = 1:nsess
for s2 = s1+1:nsess
%%
if s1~=s2
%%
fname1 = AnimalDir.processedFile{s1};
cname1 = AnimalDir.contourFile{s1};
fname2 = AnimalDir.processedFile{s2};
f1 = load(fname1);
c1 = load(cname1);
f2 = load(fname2);
im = zeros(size(c1.contours,2), size(c1.contours,3), 3);
for i = 1:size(c1.contours,1)
r = squeeze(c1.contours(i,:,:)).*rand(1);
g = squeeze(c1.contours(i,:,:)).*rand(1);
b = squeeze(c1.contours(i,:,:)).*rand(1);
im = im + cat(3, r,g,b);
end
matched = cellmap(:,s1)>0 & cellmap(:,s2)>0;
cells_s1 = cellmap(matched,s1);
cells_s2 = cellmap(matched,s2);
spks1 = f1.ms.neuron.S_matw(cells_s1, :);
spks2 = f2.ms.neuron.S_matw(cells_s2, :);
spks1 = normalize_rows(spks1);
spks2 = normalize_rows(spks2);
nsamples = min(size(spks1,2), size(spks2,2));
spks1 = spks1(:, 1:nsamples);
spks2 = spks2(:, 1:nsamples);
if ~isempty(numcells2use)
[~, randord] = sort(rand(sum(matched), 1));
spks1 = spks1(randord(1:numcells2use), :);
spks2 = spks2(randord(1:numcells2use), :);
end
pop_dt1 = round(1/median(f1.ms.dt))*pop_sec_res;
pop_dt2 = round(1/median(f2.ms.dt))*pop_sec_res;
pcell_dt1 = round(1/median(f1.ms.dt))*cell_sec_res;
pcell_dt2 = round(1/median(f2.ms.dt))*cell_sec_res;
[timecorr1, ~, ~] = Fenton_pop_stability(spks1, pop_sec_res, f1.ms.timestamps(1:nsamples)./1000, false);
[timecorr2, ~, ~] = Fenton_pop_stability(spks2, pop_sec_res, f2.ms.timestamps(1:nsamples)./1000, false);
% [popcorr3, ~, cellcorr3, ~, ~, ~] = Fenton_pop_stability(cat(2, spks1, spks2), pop_dt1, false);
[cellcorr1, ~, ~] = Fenton_cell_corr(spks1, cell_sec_res, f1.ms.timestamps(1:nsamples)./1000, false);
[cellcorr2, ~, ~] = Fenton_cell_corr(spks2, cell_sec_res, f2.ms.timestamps(1:nsamples)./1000, false);
c1 = triu(cellcorr1,1);
c2 = triu(cellcorr2,1);
lowerinds = c1==0 & c2==0;
sess_corr(s1,s2) = corr(c1(~lowerinds), c2(~lowerinds));
%% place fields
p1 = f1.ms.room.pfields_smooth(cells_s1, :, :);
p2 = f2.ms.room.pfields_smooth(cells_s2, :, :);
room_betweencorr = NaN(sum(matched), 1);
for ploop = 1:sum(matched)
p1sub = squeeze(p1(ploop,:,:));
p2sub = squeeze(p2(ploop,:,:));
valid_inds = ~isnan(p1sub.*p2sub);
room_betweencorr(ploop) = corr(p1sub(valid_inds), p2sub(valid_inds));
end
p1 = f1.ms.arena.pfields_smooth(cells_s1, :, :);
p2 = f2.ms.arena.pfields_smooth(cells_s2, :, :);
arena_betweencorr = NaN(sum(matched), 1);
for ploop = 1:sum(matched)
p1sub = squeeze(p1(ploop,:,:));
p2sub = squeeze(p2(ploop,:,:));
valid_inds = ~isnan(p1sub.*p2sub);
arena_betweencorr(ploop) = corr(p1sub(valid_inds), p2sub(valid_inds));
end
else
fname1 = AnimalDir.processedFile{s1};
f1 = load(fname1);
matched = cellmap(:,s1)>0;
spks1 = f1.ms.neuron.S_matw(cellmap(matched,s1), :);
spks1 = normalize_rows(spks1);
nsamples = size(spks1,2);
spks1 = spks1(:, 1:nsamples);
if ~isempty(numcells2use)
[~, randord] = sort(rand(sum(matched), 1));
spks1 = spks1(randord(1:numcells2use), :);
end
pop_dt1 = round(1/median(f1.ms.dt))*pop_sec_res;
pcell_dt1 = round(1/median(f1.ms.dt))*cell_sec_res;
[popcorr1, ~, ~] = Fenton_pop_stability(spks1, pop_sec_res, f1.ms.timestamps./1000, false);
popcorr1_within = popcorr1;
figure;
set(gcf, 'Name', sprintf('Animal - %s, s1=%d', animal_name, s1))
imagesc(popcorr1, [-.2 1]);
colorbar
end
p1 = nanmean(popcorr1_within,1);
pall{aLoop}(s1,:) = p1;
% p2 = nanmean(popcorr2,1);
% p3 = nanmean(popcorr3,1);
nt = length(p1);
% subplot(5,5, 5*(s1-1)+s2)
figure(5);
subplot(1,2,1)
hold on
plot(1:nt, p1, 'Color', colors(s1,:))
% plot(nt+1:2*nt, p2, 'Color', light_colors(s2,:))
drawnow
end
end
% imagesc(popcorr3, [-.1, 1])
% imagesc(cellcorr3, [-.1, 1])
if strcmp(animal_name, 'TestMouse1')
sess_corr1 = sess_corr;
else
sess_corr2 = sess_corr;
end
% plot(1:nt, nanmean(pall{aLoop},1), 'Color', mean(colors,1), 'LineWidth', 3)
end
subplot(1,2,1)
axis([-1, nt+1, -.1, 1.1])
xv = [0:15:30];
set(gca, 'XTick', xv, 'XTickLabel', xv)
xlabel('Session Time (min)')
ylabel('Population stability (corr)')
%%
% for i = 1:2
a = sess_corr1(~isnan(sess_corr1)); b = sess_corr2(~isnan(sess_corr2));
subplot(1,2,2); cla
hold on
aa = mean(a); bb = mean(b);
colors = viridis(20);
colors = colors(6:10,:);
c1 = mean(colors,1);
colors = plasma(20);
colors = colors(13:17,:);
c2 = mean(colors,1);
bar(0, aa, 'FaceColor', c1/1.5);
scatter(zeros(length(a),1), a, 'MarkerFaceColor', c1, 'MarkerEdgeColor', 'k');
bar(1, bb, 'FaceColor', c2/1.5);
scatter( ones(length(b),1), b, 'MarkerFaceColor', c2, 'MarkerEdgeColor', 'k')
set(gca, 'XTickLabel', {'Mouse1', 'Mouse2'}, 'XTick', [0 1])
axis([-1.5 2.5 0 1.1])
axis square
xlabel('')
ylabel('PCO (corr)')
%%
%%
matchingfile = 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\matching_contours\matching_matrix.mat';
ddir = 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\';
filenames = ...
{'2022_09_16_H17_03_07_TR9';...
'2022_09_16_H17_41_09_WTR10';...
'2022_09_16_H18_19_23_TR11'};
% filenames = ...
% {'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]';...
% 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\[email protected]'};
pop_sec_res = 30;
cell_sec_res = 5;
spd_thresh = 5; %cm/sec
numRand = 10;
figdir = 'C:\Users\gjb326\Desktop\RecordingData\GarrettBlair\APA_water\Hipp18240\WTR_manip\processed_files\temp_output\';
temp = load(matchingfile);
% cellmap = temp.cellmap;
cellmap = temp.cellmap(:,3:5);
nsess = size(cellmap,2);
sess_corr = NaN(nsess);
sess_absdiff = NaN(nsess);
time_corr_mean = NaN(nsess,1);
time_corr_std = NaN(nsess,1);
num_shared = [cellmap'>0] * [cellmap>0];
min_shared = min(num_shared(:));
numcells2use = [];%
if isempty(numcells2use)
numRand = 1;
end
figure(95);
clf
room_pfieldcorr = cell(nsess);
arena_pfieldcorr = cell(nsess);
for s1 = 1:nsess
for s2 = s1:nsess
%%
fname1 = sprintf('%sprocessed_files\\%[email protected]', ddir, filenames{s1});
cname1 = sprintf('%smatching_contours\\%s_contours.mat', ddir, filenames{s1});
temp = load(fname1, 'ms');
ms1 = temp.ms;
temp = load(cname1, 'contours');
conts1 = temp.contours;
if s1==s2
% single_sess_anaylsis
spks1 = ms1.neuron.S_matw;
spks1 = normalize_rows(spks1);
[timecorr1, ~, ~] = Fenton_pop_stability(spks1, pop_sec_res, ms1.timestamps./1000, false);
time_corr_mean(s1) = nanmean(timecorr1(:));
time_corr_std(s1) = nanstd(timecorr1(:))*2;
else
% cross_sess_anaylsis
fname2 = sprintf('%sprocessed_files\\%[email protected]', ddir, filenames{s2});
cname2 = sprintf('%smatching_contours\\%s_contours.mat', ddir, filenames{s2});
temp = load(fname2, 'ms');
ms2 = temp.ms;
temp = load(cname2, 'contours');
conts2 = temp.contours;
matched = cellmap(:,s1)>0 & cellmap(:,s2)>0;
matched = sum(cellmap>0,2)==nsess; % use cell matched across all sessions
cells_s1 = cellmap(matched,s1);
cells_s2 = cellmap(matched,s2);
conts1 = conts1(cells_s1,:,:);
conts2 = conts2(cells_s2,:,:);
spks1 = ms1.neuron.S_matw(cells_s1, :);
spks2 = ms2.neuron.S_matw(cells_s2, :);
pfields1_room = ms1.room.pfields_smooth(cells_s1, :, :);
pfields2_room = ms2.room.pfields_smooth(cells_s2, :, :);
pfields1_arena = ms1.arena.pfields_smooth(cells_s1, :, :);
pfields2_arena = ms2.arena.pfields_smooth(cells_s2, :, :);
%%
for pfieldLoop = 1:sum(matched)
%%
% room_alpha = ms1.room.pfield_alpha.*ms2.room.pfield_alpha;
% arena_alpha = ms1.arena.pfield_alpha.*ms2.arena.pfield_alpha;
p1 = squeeze(pfields1_room(pfieldLoop,:,:));
p2 = squeeze(pfields2_room(pfieldLoop,:,:));
validinds = ~isnan(p1.*p2);
cc = corr(p1(validinds), p2(validinds));
figure([s1*100+s2]); clf;
subplot(2,2,1)
imagesc(p1, 'AlphaData', ms1.room.pfield_alpha); axis image off
title(sprintf('Sess%d Room, %1.3f', s1, cc))
subplot(2,2,2)
imagesc(p2, 'AlphaData', ms2.room.pfield_alpha); axis image off
title(sprintf('Sess%d Room, %1.3f', s2, cc))
room_pfieldcorr{s1, s2} = cat(1, room_pfieldcorr{s1, s2}, cc);
p1 = squeeze(pfields1_arena(pfieldLoop,:,:));
p2 = squeeze(pfields2_arena(pfieldLoop,:,:));
validinds = ~isnan(p1.*p2);
cc = corr(p1(validinds), p2(validinds));
subplot(2,2,3)
imagesc(p1, 'AlphaData', ms1.arena.pfield_alpha); axis image off
title(sprintf('Sess%d Arena, %1.3f', s1, cc))
subplot(2,2,4)
imagesc(p2, 'AlphaData', ms2.arena.pfield_alpha); axis image off
title(sprintf('Sess%d Arena, %1.3f', s2, cc))
arena_pfieldcorr{s1, s2} = cat(1, arena_pfieldcorr{s1, s2}, cc);
temp = getframe(gcf);
imwrite(temp.cdata, sprintf('%s\\sess%d-sess%d_seg%d_Arena.png', figdir, s1, s2, pfieldLoop))
end
figure(95);
subplot(nsess, nsess, sub2ind([nsess, nsess], s1, s2)); hold on
violinplot([room_pfieldcorr{s1,s2}, arena_pfieldcorr{s1,s2}])
spks1 = normalize_rows(spks1);
spks2 = normalize_rows(spks2);
nsamples = min(size(spks1,2), size(spks2,2)); % minimum samles
spks1 = spks1(:, 1:nsamples);
spks2 = spks2(:, 1:nsamples);
pop_dt1 = round(1/median(ms1.dt))*pop_sec_res;
pop_dt2 = round(1/median(ms2.dt))*pop_sec_res;
pcell_dt1 = round(1/median(ms1.dt))*cell_sec_res;
pcell_dt2 = round(1/median(ms2.dt))*cell_sec_res;
spd1 = ms1.arena.speed_smooth;
spd2 = ms2.arena.speed_smooth;
c = NaN(numRand,1);
d = NaN(numRand,1);
for randloop = 1:numRand
if ~isempty(numcells2use)
[~, randord] = sort(rand(sum(matched), 1));
spks1_sub = spks1(randord(1:numcells2use), :);
spks2_sub = spks2(randord(1:numcells2use), :);
elseif numcells2use<0 % negavtive for random
[~, randord] = sort(rand(sum(matched), 1));
spks1_sub = spks1(randord(1:numcells2use), :);
[~, randord] = sort(rand(sum(matched), 1));
spks2_sub = spks2(randord(1:numcells2use), :);
else
spks1_sub = spks1;
spks2_sub = spks2;
end
spks1_sub(:,spd1<=spd_thresh) = 0;
spks2_sub(:,spd2<=spd_thresh) = 0;
spks1_sub = spks1_sub>0;
spks2_sub = spks2_sub>0;
% [timecorr1, ~, ~] = Fenton_pop_stability(spks1_sub, pop_sec_res, ms1.timestamps(1:nsamples)./1000, false);
% [timecorr2, ~, ~] = Fenton_pop_stability(spks2_sub, pop_sec_res, ms2.timestamps(1:nsamples)./1000, false);
% joint_t = ms1.timestamps(1:nsamples)./1000;
% joint_t = [joint_t; joint_t(end) + cell_sec_res + ms2.timestamps(1:nsamples)./1000];
% [timecorr3, ~, ~] = Fenton_pop_stability([spks1, spks2], pop_sec_res, joint_t, false);
% [popcorr3, ~, cellcorr3, ~, ~, ~] = Fenton_pop_stability(cat(2, spks1, spks2), pop_dt1, false);
[cellcorr1, ~, ~] = Fenton_cell_corr(spks1_sub, cell_sec_res, ms1.timestamps(1:nsamples)./1000, false);
[cellcorr2, ~, ~] = Fenton_cell_corr(spks2_sub, cell_sec_res, ms2.timestamps(1:nsamples)./1000, false);
c1 = triu(cellcorr1,1);
c2 = triu(cellcorr2,1);
lowerinds = c1==0 & c2==0;
corrs1 = c1(~lowerinds);
corrs2 = c2(~lowerinds);
[corrs1_sort, ord] = sort(corrs1, 'descend');
corrs2_sort = corrs2(ord);
tops = floor(length(ord)/4);
corrs2_sort = corrs2_sort(1:tops);
corrs1_sort = corrs1_sort(1:tops);
pw_diff = corrs1_sort - corrs2_sort;
c(randloop) = corr(corrs1_sort, corrs2_sort);
d(randloop) = median((pw_diff));
end
sess_corr(s1,s2) = nanmean(c);
sess_corr(s2,s1) = sess_corr(s1,s2);
sess_absdiff(s1,s2) = nanmean(d);
sess_absdiff(s2,s1) = sess_absdiff(s1,s2);
end
end
end
figure(92); clf; subplot(121); imagesc(sess_corr, [0 1]); subplot(122); imagesc(sess_absdiff);
figure; subplot(131); imagesc(timecorr1, [-.2 .6]); subplot(132); imagesc(timecorr2, [-.2 .6]); subplot(133); imagesc(timecorr3, [-.2 .6]);
figure(94); clf; subplot(131); imagesc(cellcorr1, [-.3 .3]); subplot(132); imagesc(cellcorr2, [-.3 .3]); subplot(133); imagesc(cellcorr1-cellcorr2, [-.3 .3]);
% figure; hold on; plot(corrs1_sort); plot(corrs2_sort)
%%
figure(10); clf;
set(gcf, 'Name', 'ARENA FRAME / ROOM FRAME')
% colormap(plasma)
% figure(11); clf
% set(gcf, 'Name', 'ROOM FRAME')
colormap(viridis)
figure(10);
for i = 1:ds
subplot_tight(2, ds+1, i, [.001 .001])
p = squeeze(p_a(i,:,:));
% p = squeeze(ms.arena.pfields(i,:,:));
p = p./max(p(:));
ii = imagesc(p, [0 .5]);
ii.AlphaData = ms.arena.pfield_alpha;
axis square off
end
% figure(11);
for i = 1:ds
subplot_tight(2, ds+1, ds+i+1, [.001 .001])
p = squeeze(p_r(i,:,:));
% p = squeeze(ms.room.pfields(i,:,:));
p = p./max(p(:));
ii = imagesc(p, [0 .5]);
ii.AlphaData = ms.room.pfield_alpha;
% ii = imagesc(p, 'AlphaData', ms.room.pfield_alpha);
% ii.Parent.ALim = [0 .5];
axis square off
end
p = p*NaN;
subplot_tight(2, ds+1, [ds+1, 2*(ds+1)], [.1 .001]); cla
ii = imagesc(p, [0 .5]);
ii.AlphaData = NaN*ms.arena.pfield_alpha;
colorbar
axis off
figure(11); clf;
set(gcf, 'Name', 'ca traces (blk) and inferred spks (red)')
hold on;
stacked_traces(c_sub, .9, {'k-'})
stacked_traces(s_sub, .9, {'r-'})
ts = ms.timestamps./1000;
mts = mod(ts, 60.001);
xd = find(mts(1:end-1) > mts(2:end));
xl = abs(round(ts(xd)));
set(gca, 'XTickLabel', xl, 'XTick', xd);
xlabel('Time (sec)')