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LD_peak_selection_71_units.m
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%% Code below adapted from get_clean_peaks_and_data.m
%after saving the data with new_data_set.m, we isolate the peaks and trials for the
%new analysis of the refined data
%we also save the data we want to plot (only the clean data)
%this script is the data cleaning and selection pipeline
%written by Loic Daumail
%edited on 06-05-2020
%edited on 10/30/2022 to remove all peak selection criteria, eccept the
%minimum of 10 trials condition.
newdatadir = 'C:\Users\daumail\OneDrive - Vanderbilt\Documents\LGN_data_042021\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\';
channelfilename = [newdatadir 'refined_dataset'];
data_file = load(channelfilename);
%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]) = [];
f = {'DE0_NDE50','DE50_NDE0','DE50_NDE50'};
%% find peak locations of smoothed data, to further allow us to isolate peak values of unfiltered data in order to analyze them on R and fit a LMER
channum = 1: length(data_file.new_data);
xabs = -199:1300;
nyq = 500;
mean_filtered_dSUA = struct();
clean_high_SUA = struct();
clean_origin_data = struct();
data_peaks = struct();
peaks_locs = struct();
clear i
for i = channum
filename = [data_file.new_data(i).channel_data.filename, f{2}];
filename = erase(filename, '.mat');
blankcontrast = data_file.new_data(i).channel_data.contrast == 0 & data_file.new_data(i).channel_data.fixedc == 0;
highcontrast = data_file.new_data(i).channel_data.contrast >= 0.5 & data_file.new_data(i).channel_data.fixedc == 0;
trialidx = 1:length(data_file.new_data(i).channel_data.sdftr_chan(1,:));
raw_bs = nan(length(xabs), length(trialidx));
filtered_dSUA = nan(length(xabs), length(trialidx));
origin_data = nan(length(xabs)+401, length(trialidx));
all_norm_lpdSUA= nan(length(xabs),length(trialidx));
% powerstim = nan(length(trialidx),1025);
% freqstim = nan(length(trialidx),1025);
% fourhzpowerstim =nan(length(trialidx),1);
% bsl = nan(1, length(trialidx));
% mean_wnd1 = nan(1,length(trialidx));
all_pks = nan(4,length(data_file.new_data(i).channel_data.sdftr_chan(1,highcontrast)));
for tridx = trialidx
%
all_data = data_file.new_data(i).channel_data.sdftr_chan(401:1900,tridx);
origin_data(:,tridx) = data_file.new_data(i).channel_data.sdftr_chan(:,tridx);
raw_bs(:,tridx) = all_data(1:end)- mean(all_data(1:200));
lpc = 4.5; %low pass cutoff
lWn = lpc/nyq;
[bwb,bwa] = butter(4,lWn,'low');
lpdSUA = filtfilt(bwb,bwa, raw_bs(:,tridx));
%
filtered_dSUA(:,tridx) = lpdSUA;
% %all_norm_lpdSUA(:,tridx) = (lpdSUA - min(lpdSUA))/(max(lpdSUA)- min(lpdSUA));
% mean_wnd1(tridx) = mean(lpdSUA(201:480)); %mean spiking response of a trial over 280ms following stim onset
%
% %%% power
%
%
% [powerstim(tridx,:), freqstim(tridx,:)] = calcFFT(all_data(200:1350)); %fourrier transform
%
% %find the index of the frequency vector closest to 4hz and point to the
% %power value of this index for every trial, and store the value in
% %fourhzpower
% [val,index] = min(abs(4-freqstim(tridx,:))); %index of value closest to 4Hz
% fourhzpowerstim(tridx,1) = powerstim(tridx,index); %get power at that index, assuming this is 4Hz
%
end
%%%%%%%%%%% %reject trials below Mean + 1.96*STD in the blank condition %%%%%%
%power related variables
% power0 = fourhzpowerstim(blankcontrast);
% power5 = fourhzpowerstim(highcontrast);
%
%spiking activity related variables
% mean_wnd1_5 =mean_wnd1(highcontrast);
filtered_dSUA_high = filtered_dSUA(:, highcontrast);
origin_data_high = origin_data(:, highcontrast);
%first peak location related variables
% sua_bsl = mean(filtered_dSUA_high(1:200,:),1);
% for tr = 1:length(power5)
% if mean_wnd1_5(tr) > mean(sua_bsl)+1.96*std(sua_bsl) && power5(tr) > mean(power0)+1.96*std(power0) %
%
% filtered_dSUA_high(:,tr) = filtered_dSUA_high(:,tr);
% origin_data_high(:,tr) = origin_data_high(:,tr);
% else
%
% filtered_dSUA_high(:,tr) = nan(length(filtered_dSUA_high(:,tr)),1);
% origin_data_high(:,tr) = nan(length(origin_data_high(:,tr)),1);
% end
% end
%determine the first peak location for each trial of a given single
%unit
all_locsdSUA_trials = nan(6,length(filtered_dSUA_high(1,:)));
clear trial
for trial = 1:length(filtered_dSUA_high(1,:))
for ln = 1:550
if filtered_dSUA_high(200+ln,trial) < filtered_dSUA_high(200+ln+1,trial) && ~all(isnan(filtered_dSUA_high(:,trial)))
locsdSUA_trial_struct = findpeaks_Loic(filtered_dSUA_high(200+ln:1499,trial));
locsdSUA_trial = locsdSUA_trial_struct.loc;
%if peak1 is too small, peak2 becomes peak1
% if filtered_dSUA_high(locsdSUA_trial(1)+200+ln,trial) >= 0.4*filtered_dSUA_high(locsdSUA_trial(2)+200+ln)
%store first peak location
all_locsdSUA_trials(1:length(locsdSUA_trial),trial) = locsdSUA_trial(1:end)+200+ln;
%else
% all_locsdSUA_trials(1:length(locsdSUA_trial(2:end)),trial) = locsdSUA_trial(2:end)+200+ln;
% end
break
end
end
if nnz(~isnan(all_locsdSUA_trials(:,trial))) >= 4 && ~all(isnan(all_locsdSUA_trials(:,trial)))
%adjust location to the first data point of lpsu (+ln),
all_pks(:,trial) = filtered_dSUA_high(all_locsdSUA_trials(1:4,trial), trial);
filtered_dSUA_high(:,trial) = filtered_dSUA_high(:,trial);
all_locsdSUA_trials(:,trial) = all_locsdSUA_trials(:,trial);
origin_data_high(:,trial) = origin_data_high(:,trial);
else
filtered_dSUA_high(:,trial) = nan(length(filtered_dSUA_high(:,trial)),1);
all_locsdSUA_trials(:,trial) = nan(size(all_locsdSUA_trials(:,trial)));
origin_data_high(:,trial) = nan(length(origin_data_high(:,trial)),1);
end
if ~all(isnan(all_locsdSUA_trials(:,trial))) && (all_locsdSUA_trials(4,trial) ~= 1500)
%adjust location to the first data point of lpsu (+ln),
all_pks(:,trial) = filtered_dSUA_high(all_locsdSUA_trials(1:4,trial), trial);
filtered_dSUA_high(:,trial) = filtered_dSUA_high(:,trial);
all_locsdSUA_trials(:,trial) = all_locsdSUA_trials(:,trial);
origin_data_high(:,trial) = origin_data_high(:,trial);
else
all_pks(:,trial) = nan(length(all_pks(:,trial)),1);
filtered_dSUA_high(:,trial) = nan(length(filtered_dSUA_high(:,trial)),1);
all_locsdSUA_trials(:,trial) = nan(size(all_locsdSUA_trials(:,trial)));
origin_data_high(:,trial) = nan(length(origin_data_high(:,trial)),1);
end
end
%{
figure(); plot(-199:1300, filtered_dSUA_high(1:1500,1))
hold on
plot(all_locsdSUA_trials(1:4,1)-200, all_pks(:,1))
set(gca,'box','off')
%}
%%% reject outlier peaks and the corresponding trials in
%%% filtered_dSUA_high
%reject if there is a peak 1 outlier, if the max peak value in the
%baseline is an outlier
% First find peaks before stimulus onset
% bsl_peaks = nan(1, length(filtered_dSUA_high(1,:)));
% clear tr
% for tr = 1:length(filtered_dSUA_high(1,:))
%
% for loc = 1:200
% if filtered_dSUA_high(loc,tr) < filtered_dSUA_high(loc+1,tr) && ~all(isnan(filtered_dSUA_high(:,tr)))
% if length(filtered_dSUA_high(loc:200,tr)) >= 3
% if ~isempty(findpeaks_Loic(filtered_dSUA_high(loc:200,tr)))
% bsl_peak_locs_struct = findpeaks_Loic(filtered_dSUA_high(loc:200,tr));
% bsl_peak_locs = bsl_peak_locs_struct.loc;
%
% bsl_peaks(1,tr) = max(filtered_dSUA_high(bsl_peak_locs+loc,tr));
% else
% bsl_peaks(1,tr) = NaN;
% end
% end
% break
% end
% end
% end
%
% out_bsl_peaks = isoutlier(bsl_peaks);
% p1outliers = isoutlier(all_pks(1,:));
% clear tr
% for tr = 1:length(filtered_dSUA_high(1,:))
% %exclude trials
% if p1outliers(tr) == 0 && ~all(isnan(all_pks(:,tr))) && out_bsl_peaks(tr) ==0
%
% filtered_dSUA_high(:,tr) = filtered_dSUA_high(:, tr);
% all_pks(:, tr) = all_pks(:,tr);
% all_locsdSUA_trials(:,tr) = all_locsdSUA_trials(:,tr);
% origin_data_high(:,tr) = origin_data_high(:, tr);
%
% else
% filtered_dSUA_high(:,tr) = nan(length(filtered_dSUA_high(:,tr)),1);
% all_pks(:,tr) = nan(length(all_pks(:,tr)),1);
% all_locsdSUA_trials(:,tr) = nan(size(all_locsdSUA_trials(:,tr)));
% origin_data_high(:,tr) = nan(length(origin_data_high(:,tr)),1);
% end
% end
filtered_dSUA_high = filtered_dSUA_high(:,~all(isnan(filtered_dSUA_high))); % for nan - cols
all_locsdSUA_trials = all_locsdSUA_trials(:,~all(isnan(all_locsdSUA_trials)));
all_pks = all_pks(:, ~all(isnan(all_pks)));
origin_data_high = origin_data_high(:,~all(isnan(origin_data_high)));
if length(filtered_dSUA_high(1,:)) >=10
clean_high_SUA(i).namelist = filtered_dSUA_high;
clean_origin_data(i).unit = origin_data_high;
peaks_locs(i).locs = all_locsdSUA_trials;
elseif length(filtered_dSUA_high(1,:)) <10
%all_pks(:,:) = nan(length(all_pks(:,1)),length(all_pks(1,:)));
all_pks(:,:) = [];
clean_high_SUA(i).namelist = [];
clean_origin_data(i).unit = [];
peaks_locs(i).locs = [];
end
data_peaks(i).namelist = all_pks(:,~all(isnan(all_pks)));
all_pks = all_pks(:,~all(isnan(all_pks)));
%channelfilename = [newdatadir 'su_peaks_03032020_corrected\individual_units\' filename];
%save(strcat(channelfilename, '.mat'), 'all_pks');
end
%allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\all_data_peaks'];
%save(strcat(allfilename, '.mat'), 'data_peaks');
% allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\clean_SUA_sup_50_03052020'];
% save(strcat(allfilename, '.mat'), 'clean_high_SUA');
% allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\clean_SUA_locs_03052020'];
% save(strcat(allfilename, '.mat'), 'peaks_locs');
allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\clean_origin_sup_50_10302022'];
save(strcat(allfilename, '.mat'), 'clean_origin_data');
allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\filt_SUA_sup_50_10302022'];
save(strcat(allfilename, '.mat'), 'clean_high_SUA');
allfilename = [newdatadir 'su_peaks_03032020_corrected\all_units\filt_SUA_locs_10302022'];
save(strcat(allfilename, '.mat'), 'peaks_locs');
%count number of remaining units after preproc
cnt =0;
for i =1:length(data_peaks)
if ~isempty(data_peaks(i).namelist)
cnt = cnt+1;
end
end
%% code below adapted from get_origin_peaks.m
%following the script get_clean_peaks_and_data.m (data cleaning and
%selection pipeline)
%this script was written to isolate peaks of the origin data in order to
%perform the statistical analysis of the peaks
%some lines were commented out and replaced in order to also isolate
%normalized peak values, averaged across trials in order to plot the
%normalized average peak values of each unit on R
%Written by Loic Daumail, last edited on 6/29/2020, then 10/23/2022
%newdatadir = 'C:\Users\daumail\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\all_units\';
newdatadir = 'C:\Users\daumail\OneDrive - Vanderbilt\Documents\LGN_data_042021\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020_corrected\all_units\';
channelfilename = [newdatadir 'clean_origin_sup_50_10302022'];
data_file = load(channelfilename);
channelfilename = [newdatadir 'filt_SUA_sup_50_10302022'];
filt_data_file = load(channelfilename);
locsfilename = [newdatadir 'filt_SUA_locs_10302022'];
all_locsdSUA = load(locsfilename);
gendatadir = 'C:\Users\daumail\OneDrive - Vanderbilt\Documents\LGN_data_042021\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\';
channelfilename = [gendatadir 'refined_dataset'];
gen_data_file = load(channelfilename);
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]) = [];
f = {'DE0_NDE50','DE50_NDE0','DE50_NDE50'};
xabs = -199:1300;
nyq = 500;
channum = 1: length(data_file.clean_origin_data);
mean_origin_dSUA = struct();
mean_filtered_dSUA = struct();
suas_trials = struct();
peak_vals = struct();
mean_peak_vals = struct();
mean_peaks = nan(4,length(channum));
up_dist = nan(1, length(channum),4);
max_low_dist = nan(1, length(channum));
all_locsdSUA_filtered = nan(1,length(channum),4);
filenames = cell(length(channum),2);
for i = channum
if ~isempty(data_file.clean_origin_data(i).unit)
trialidx = 1:length(data_file.clean_origin_data(i).unit(1,:));
origin_dSUA = data_file.clean_origin_data(i).unit(401:1900,:); %- mean(data_file.clean_origin_data(i).unit(401:600,:),1);
%create normalized origin trials data to plot average peaks for each unit with R
norm_unit = nan(size(origin_dSUA));
clear tr
for tr =trialidx
min_unit =min(origin_dSUA(:,tr),[],1);
max_unit = max(origin_dSUA(:,tr),[],1);
norm_unit(:,tr) = (origin_dSUA(:,tr)-min_unit)./(max_unit - min_unit);
end
filtered_dSUA = filt_data_file.clean_high_SUA(i).namelist;
%determine the peak location of interest for each trial of a given single
%unit
all_locsdSUA_trials = all_locsdSUA.peaks_locs(i).locs;
up_dist_trials = nan(4,length(trialidx));
clear pn
for pn = 1:4
locs_peak = all_locsdSUA_trials(pn, :);
up_dist_trials(pn,:)= length(xabs)- locs_peak;
end
%get the max distance between the peakalign and the stimulus onset
max_low_dist_unit = max(all_locsdSUA_trials,[],'all');
%create new matrix with the length(max(d)+max(xabs - d))
new_dist_unit = max_low_dist_unit + max(up_dist_trials,[],'all');
fp_locked_trials = nan(new_dist_unit,length(origin_dSUA(1,:)),4);
filtered_fp_locked_trials = nan(new_dist_unit,length(filtered_dSUA(1,:)),4);
clear n pn
for pn =1:4
for n =trialidx
lower_unit_bound =max_low_dist_unit-all_locsdSUA_trials(pn,n)+1;
upper_unit_bound =max_low_dist_unit-all_locsdSUA_trials(pn,n)+length(xabs);
%origin data of the statistical analysis
fp_locked_trials(lower_unit_bound:upper_unit_bound,n,pn) = origin_dSUA(:,n);
%normalized data for the plotting
% fp_locked_trials(lower_unit_bound:upper_unit_bound,n,pn) = norm_unit(:,n);
filtered_fp_locked_trials(lower_unit_bound:upper_unit_bound,n,pn) = filtered_dSUA(:,n);
end
end
%get the aligned data if it exists for the unit
suas_trials(i).aligned= fp_locked_trials;
max_low_dist(i) = max_low_dist_unit;
clear pn
for pn = 1:4
%peak data for the stats
peak_vals(i).peak(pn,:)= max(suas_trials(i).aligned(max_low_dist(i)-1-124:max_low_dist(i)-1+125,:,pn), [],1);
end
%mean peaks for the R plots
mean_peaks(:,i) = mean(peak_vals(i).peak,2);
else
%peak data for the stats
peak_vals(i).peak = [];
%peak data for the R plots
mean_peaks(:,i) = nan(4,1);
end
filename = [gen_data_file.new_data(i).channel_data.filename, f{2}];
filename = erase(filename, '.mat');
filenames(i,1) = cellstr(filename);
filenames(i,2) = cellstr(layer(i));
peaks = peak_vals(i).peak;
channelfilename = [gendatadir 'su_peaks_10302022\orig_peak_values\' filename];
%save(strcat(channelfilename, '.mat'), 'peaks');
end
mean_peak_vals.peak = mean_peaks;
allfilename = [gendatadir 'su_peaks_10302022\orig_peak_values\all_units\all_raw_data_peaks'];
save(strcat(allfilename, '.mat'), 'peak_vals');
allfilename = [gendatadir 'su_peaks_10302022\orig_peak_values\all_units\all_raw_mean_data_peaks'];
save(strcat(allfilename, '.mat'), 'mean_peaks');
% Convert cell to a table and use first row as variable names
T = cell2table(filenames);
savefilename = [gendatadir 'su_peaks_10302022\orig_peak_values\all_units\filenames_layers'];
% Write the table to a CSV file
writetable(T,strcat(savefilename, '.csv'))
save(strcat(savefilename, '.csv'), 'filenames');