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former_align_clean_data.m
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newdatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_02252020_2\all_units\';
channelfilename = [newdatadir 'clean_SUA_sup_50'];
data_file = load(channelfilename);
xabs = -199:1300;
nyq = 500;
channum = 1: length(data_file.clean_high_SUA);
mean_filtered_dSUA = struct();
suas_trials = struct();
up_dist = nan(1, length(channum));
all_locsdSUA_filtered = nan(1,length(channum));
for i = channum
if ~isempty(data_file.clean_high_SUA(i).namelist)
trialidx = 1:length(data_file.clean_high_SUA(i).namelist(1,:));
filtered_dSUA = data_file.clean_high_SUA(i).namelist;
% filtered_dSUA = filtered_dSUA(:,~all(isnan(filtered_dSUA))); % for nan - cols
bsl_peaks = nan(1, length(filtered_dSUA(1,:)));
for tr = trialidx
for loc = 1:200
if filtered_dSUA(loc,tr) < filtered_dSUA(loc+1,tr)
bsl_peak_locs = findpeaks(filtered_dSUA(loc:200,tr));
bsl_peaks(1,tr) = max(filtered_dSUA(bsl_peak_locs.loc+loc,tr));
break
end
end
end
%if length(filtered_dSUA(1,:)) >=10
%store unaligned data
suas_trials(i).unaligned = filtered_dSUA;
%determine the first peak location for each trial of a given single
%unit
all_locsdSUA_trials = nan(1,length(filtered_dSUA(1,:)));
peak1 = nan(1, length(filtered_dSUA(1,:)));
for trial = 1:length(filtered_dSUA(1,:))
for ln = 1:550
if ~all(isnan(filtered_dSUA(:,trial))) && filtered_dSUA(200+ln,trial) < filtered_dSUA(200+ln+1,trial)
locsdSUA_trial = findpeaks(filtered_dSUA(200+ln:1350,trial));
if filtered_dSUA(locsdSUA_trial.loc(1)+200+ln,trial) > 0.4*filtered_dSUA(locsdSUA_trial.loc(2)+200+ln)
%store first peak location
all_locsdSUA_trials(:,trial) = locsdSUA_trial.loc(1)+200+ln;
else
all_locsdSUA_trials(:,trial) = locsdSUA_trial.loc(2)+200+ln;
end
%get distribution of peak 1 across trials
peak1(1,trial) = filtered_dSUA(all_locsdSUA_trials(1,trial),trial);
break
end
end
end
up_dist_trials= length(xabs)- all_locsdSUA_trials;
max_low_dist_unit = max(all_locsdSUA_trials(1,:));
%create new matrix with the length(max(d)+max(xabs - d))
new_dist_unit = max_low_dist_unit + max(up_dist_trials);
fp_locked_trials = nan(new_dist_unit,length(filtered_dSUA(1,:)));
%find peak1 outliers
outlier = isoutlier(peak1);
%find bsl peaks outliers
out_bsl_peaks = isoutlier(bsl_peaks);
clear n
for n = 1:length(filtered_dSUA(1,:))
if ~isnan(all_locsdSUA_trials(n)) && outlier(n) == 0 %&& out_bsl_peaks(n) == 0
lower_unit_bound =max_low_dist_unit-all_locsdSUA_trials(n)+1;
upper_unit_bound =max_low_dist_unit-all_locsdSUA_trials(n)+length(xabs);
fp_locked_trials(lower_unit_bound:upper_unit_bound,n) = filtered_dSUA(:,n);
end
end
fp_locked_trials_out = fp_locked_trials(:,~all(isnan(fp_locked_trials))); % for nan - cols
%compute the mean single unit activity if more than 10 trials
mean_filtered_dSUA(i).mean_unit = mean(fp_locked_trials_out,2); % for nan - cols
%get the aligned data if it exists for the unit
suas_trials(i).aligned = fp_locked_trials_out;
% elseif length(filtered_dSUA(1,:)) <10
% mean_filtered_dSUA(i).mean_unit = nan(length(fp_locked_trials(:,1)),1);
% suas_trials(i).unaligned = nan(1,1);
% end
end
%{
figure();
x = 1:length(fp_locked_trials_out(:,1));
plot(x,fp_locked_trials)
hold on
plot(x, mean(fp_locked_trials_out,2),'LineWidth',1, 'Color', 'black')
%mean_aligned = mean(fp_locked_trials_out,2);
%nanmean_aligned = nanmean(fp_locked_trials_out,2);
%}
%{
%%%%%%% align mean single units to first peak %%%%
%start finding the peaks at the first non NaN location
for len1 =1:800
if ~isnan(mean_filtered_dSUA(i).mean_unit(len1))
break
end
end
for len2 = len1+30:1550
if ~all(isnan(mean_filtered_dSUA(i).mean_unit)) && mean_filtered_dSUA(i).mean_unit(len2) < mean_filtered_dSUA(i).mean_unit(len2+1)
locsdSUA_filtered = findpeaks(mean_filtered_dSUA(i).mean_unit(len2:end));
if mean_filtered_dSUA(i).mean_unit(locsdSUA_filtered.loc(1)+len2) > 0.6*mean_filtered_dSUA(i).mean_unit(locsdSUA_filtered.loc(2)+len2)
%store first peak location
all_locsdSUA_filtered(:,i) = locsdSUA_filtered.loc(1)+len2;
else
all_locsdSUA_filtered(:,i) = locsdSUA_filtered.loc(2)+len2;
end
break
end
end
%compute the distance between the first peak and the last datapoint and store
%in a matrix
up_dist(:,i)= length(mean_filtered_dSUA(i).mean_unit)- all_locsdSUA_filtered(i);
%}
end
pvaluesdir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\lmer_results_peaks\';
pvalfilename = [pvaluesdir 'lmer_results_rawbs.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
%% Plots of peaks with pvalues
%exclude 160517, (first unit, left empty, it is a K neuron)
%Reject 180806 p1 uclust17, M cell, as doesn't seem well triggered (46)
%Reject 181207 (B) uclust22, M cell, as doesn't seem well triggered (55)
layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
'','','M','M','M','P','M','M','M','M','P','P'};
layer([1,46,55]) = [];
channum = 1: length(data_file.clean_high_SUA);
for chan = 1:15
mean_unaligned = mean(suas_trials(chan).unaligned,2);
unaligned = suas_trials(chan).unaligned;
mean_aligned = mean(suas_trials(chan).aligned,2);
aligned = suas_trials(chan).aligned;
if ~isempty(mean_unaligned) && ~isempty(mean_aligned)
h = figure;
xabs = -199:1300;
subplot(2, 1,1);
plot(xabs, unaligned)
hold on
plot(xabs, mean_unaligned,'LineWidth',1, 'Color', 'black')
hold on
plot([0 0], ylim,'k')
hold on
plot([1150 1150], ylim,'k')
xalign = 1:length(suas_trials(chan).aligned(:,1));
subplot(2, 1,2);
plot(xalign, aligned)
hold on
plot(xalign, mean_aligned,'LineWidth',1, 'Color', 'black')
ylh = ylabel({'\fontsize{9}Contacts','\fontsize{9}Spike Rate (spikes/s)'});
set(gca, 'linewidth',2)
set(gca,'box','off')
sgtitle({sprintf('%s | %s', num2str(chan), char(layer(chan))), 'responses, p<0.05, associated to adaptation pvalues'}, 'Interpreter', 'none')
xlabel('Time from -50ms from stimulus onset (ms)')
set(gcf,'Units','inches')
set(gcf,'position',[1 1 8.5 11])
% filename = strcat('C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\plots\',strcat( sprintf('bscorr_data_aligned_unaligned_P1040P2_1ms_stimonset_unit_%d_exclude4peaks', chan)));
%saveas(gcf, strcat(filename, '.png'));
end
end