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lmer_results_anal.asv
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%this script was written after get_clean_peaks_and_data.m in order to
%analyze the lmer results and plot the clean data.
%loading the clean data
newdatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020\all_units\';
channelfilename = [newdatadir 'clean_SUA_sup_50'];
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'};
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_03032020.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
%% Rough plots of peaks with pvalues (not very representative, as mean unit activity)
channum = 1: length(data_file.clean_high_SUA);
%for n = 1:3
for chan = 1:12:length(channum)
h = figure;
xabs = -199:1300;
idx = [1 3 5 7 9 11 2 4 6 8 10 12];
nyq = 500;
all_mean_data = nan(length(xabs), length(1:12));
clear i ;
for i = 1:12
mean_data = mean(data_file.clean_high_SUA(chan+i-1).namelist(1:1500,:),2);
lpc = 4.5; %low pass cutoff
lWn = lpc/nyq;
[bwb,bwa] = butter(4,lWn,'low');
if ~all(isnan(mean_data))
lpsu = filtfilt(bwb,bwa, mean_data);
sp = subplot(length(1:6), 2, idx(i));
plot(xabs, lpsu)
hold on
plot([0 0], ylim,'k')
hold on
plot([1150 1150], ylim,'k')
if i == length(6)/2
ylh = ylabel({'\fontsize{9}Contacts','\fontsize{9}Spike Rate (spikes/s)'});
end
if i < 6 || (i >= 7)&&(i < 12)
set(sp, 'XTick', [])
end
ylabelh = text(max(xabs), mean(lpsu,1), strcat(num2str(chan+i-1),' | ', layer(chan+i-1)),'HorizontalAlignment','left','FontName', 'Arial','FontSize', 10);
for npeak = 1:4
for len = 231:480 %from 200 + 30 (lgn response onset)
if lpsu(len) < lpsu(len+1)
locs = findpeaks(lpsu(len:1450));
break
end
end
if length(locs.loc) >= 4
%adjust location to the first data point of lpsu (+len), then adjust
%to xabs (-200)
xlocation = locs.loc(npeak)+len-200;
end
text(xlocation, mean(lpsu,1), strcat(num2str(sprintf('%.2f', pvalues(chan+i-1,npeak)))),'HorizontalAlignment','center','FontName', 'Arial','FontSize', 7);
end
end
end
set(gca, 'linewidth',2)
set(gca,'box','off')
sgtitle({f{2}, 'all good 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(f{2}, sprintf('x_%d_better_raw_data_peakspvalues_2dec', chan+i-1)));
%saveas(gcf, strcat(filename, '.png'));
end
%%
%% compute proportion of significant adaptation per peak and proportion of neurons adapting for a certain amount of
%peak from peak 2 to 4
channeldir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\su_peaks_03032020\orig_peak_values\all_units\';
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_orig_03032020.csv'];
pvalues = dlmread(pvalfilename, ',', 1,1);
peakvals = load([channeldir 'all_data_peaks']);
%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]) = [];
layer_idx = find(strcmp(layer, 'M'));
f = {'DE0_NDE50','DE50_NDE0','DE50_NDE50'};
all_locs = nan(4,length(layer_idx));
all_pks = nan(4,length(layer_idx));
all_mean_data = nan(4, length(layer_idx));
cntpk2 = 0;
cntpk3 = 0;
cntpk4 = 0;
cntpk2pk3 = 0;
cntpk2pk3pk4 = 0;
cntpk3pk4 =0;
cntincpk2 =0;
cntincpk3 =0;
cntincpk4 =0;
cntnspk2 =0;
cntnspk3 =0;
cntnspk4=0;
for nunit = 1:length(layer_idx)
if ~isempty(peakvals.peak_vals(layer_idx(nunit)).peak)
mean_data = nanmean(peakvals.peak_vals(layer_idx(nunit)).peak,2);
all_mean_data(:,nunit) = mean_data;
if all_mean_data(2,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),2) < .05
cntpk2 = cntpk2 +1;
end
if all_mean_data(3,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),3) < .05
cntpk3 = cntpk3 +1;
end
if all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntpk4 = cntpk4 +1;
end
if all_mean_data(2,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),2) < .05 && ...
all_mean_data(3,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),3) < .05
cntpk2pk3 = cntpk2pk3 +1;
end
if all_mean_data(2,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),2) < .05 && ...
all_mean_data(3,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),3) < .05 ...
&& all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntpk2pk3pk4 = cntpk2pk3pk4 +1;
end
if all_mean_data(3,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),3) < .05 ...
&& all_mean_data(4,nunit) < all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntpk3pk4 = cntpk3pk4 +1;
end
if all_mean_data(2,nunit) > all_mean_data(1,nunit) && pvalues(layer_idx(nunit),2) < .05
cntincpk2 = cntincpk2 +1;
end
if all_mean_data(3,nunit) > all_mean_data(1,nunit) && pvalues(layer_idx(nunit),3) < .05
cntincpk3 = cntincpk3 +1;
end
if all_mean_data(4,nunit) > all_mean_data(1,nunit) && pvalues(layer_idx(nunit),4) < .05
cntincpk4 = cntincpk4 +1;
end
if pvalues(layer_idx(nunit),2) > .05
cntnspk2 = cntnspk2 +1;
end
if pvalues(layer_idx(nunit),3) > .05
cntnspk3 = cntnspk3 +1;
end
if pvalues(layer_idx(nunit),4) > .05
cntnspk4 = cntnspk4 +1;
end
end
end
all_mean_data = all_mean_data(:, ~all(isnan(all_mean_data)));
percentpk2 = cntpk2*100/length(all_mean_data(1,:));
percentpk3 = cntpk3*100/length(all_mean_data(1,:));
percentpk4 = cntpk4*100/length(all_mean_data(1,:));
percentpk2pk3 = cntpk2pk3*100/length(all_mean_data(1,:));
percentpk2pk3pk4 = cntpk2pk3pk4*100/length(all_mean_data(1,:));
percentpk3pk4 = cntpk3pk4*100/length(all_mean_data(1,:));
percentincpk2 = cntincpk2*100/length(all_mean_data(1,:));
percentincpk3 = cntincpk3*100/length(all_mean_data(1,:));
percentincpk4 = cntincpk4*100/length(all_mean_data(1,:));
percentnspk2 = cntnspk2*100/length(all_mean_data(1,:));
percentnspk3 = cntnspk3*100/length(all_mean_data(1,:));
percentnspk4 = cntnspk4*100/length(all_mean_data(1,:));
ncells =length(all_mean_data(1,:));