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drgAnalyzeLFPDiscriminantBatchCaMKIIGrp.m
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function drgAnalyzeLFPDiscriminantBatchCaMKIIGrp
%Analyzes the linear discriminant analysis performed by drgLFPDiscriminantBatch
%Takes as a in input the 'Discriminant_*.mat' output file from drgLFPDiscriminantBatch
%Performs an analysis of the timecourse for percent correct for LDA and for
%the PCA
warning('off')
close all
clear all
t_odor_arrival=0.1;
which_display=3;
mice_excluded=[];
[fname,pname,nCancel] = uigetfile({'Discriminant_*.mat'},'Select the perceptron LFP batch output file ...');
if nCancel
inputPath = [pname,fname];
pnameStart = pname;
%save('timeCorr_cfg.mat','pnameStart','-append');
else
error('Cancelled')
end
discriminant_name=[pname fname];
load(discriminant_name)
t_pac=handles_out.t_power';
t_power=handles_out.t_power';
handles.drg=handles_out.drg;
figNo=0;
% %Define the windows for analysis
% window_start=[-1 1.5];
% window_end=[0 2.5];
% no_wins=2;
%This is the window for odor
%
odor_from=1.5;
odor_to=2.5;
%This is the window for reinforcement
reinf_from=4.4;
reinf_to=5;
%This is the window for the AUC
AUC_from=0.5;
AUC_to=2.5;
%Plot average percent correct for the LDA for peak and trough for
%wavelet power referenced to PAC phase
t=handles_out.t_power;
pcorr_out=[];
lick_out=[];
if length(handles_out.drgbchoices.PACnames)==3
these_PACii=[1 3];
else
these_PACii=[1 2];
end
for PACii=these_PACii
p_correct_stats=[];
ii_stats=0;
p_corr_dt_peak_stats=[];
ii_p_stats=0;
p_corr_dt_trough_stats=[];
ii_t_stats=0;
glm_ii_pcdt=0;
glm_pcorr_dt=[];
glm_pcorr_dt_peak=[];
glm_ii_pcdt_p=0;
glm_pcorr_dt_trough=[];
glm_ii_pcdt_t=0;
for percent_correct_ii=1:2
for groupNo=1:max(handles_out.drgbchoices.group_no)
%Gather all the data
no_mice=0;
no_mice_included=0;
all_discriminant_correct_peak=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
all_discriminant_correct_trough=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
all_discriminant_correct_shuffled_peak=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
all_discriminant_correct_shuffled_trough=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
for mouseNo=1:length(handles_out.discriminant_PACwavepower)
try
if handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).discriminant_calculated==1
per_ii=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).no_trials;
no_mice=no_mice+1;
if (per_ii>=20)&(sum(no_mice==mice_excluded)==0)
no_mice_included=no_mice_included+1;
pcorr_out.PACii(PACii).pcorr(percent_correct_ii).group(groupNo).mouseNos(no_mice_included)=mouseNo;
all_discriminant_correct_peak(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).discriminant_correct_peak;
all_discriminant_correct_trough(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).discriminant_correct_trough;
all_discriminant_correct_shuffled_peak(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).discriminant_correct_shuffled_peak;
all_discriminant_correct_shuffled_trough(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).discriminant_correct_shuffled_trough;
all_dimensionality_peak(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).dimensionality_peak;
all_dimensionality_trough(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).dimensionality_trough;
end
end
catch
end
end
fprintf(1, ['The number of mice included in the LDA analysis for this odor pair is %d\n\n\n'], no_mice_included)
no_mice_per(percent_correct_ii)=no_mice_included;
%Plot percent correct for the LDA and save the data for
%the ranksum
figNo=figNo+1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.61 .4 .38 .38])
hold on
ax=gca;ax.LineWidth=3;
%Note that I merge the shuffled for both peak and
%trough for the average plot
all_discriminant_correct_shuffled=zeros(2*no_mice_included,length(t));
all_discriminant_correct_shuffled(1:no_mice_included,:)=all_discriminant_correct_shuffled_peak(1:no_mice_included,:);
all_discriminant_correct_shuffled(no_mice_included+1:end,:)=all_discriminant_correct_shuffled_trough(1:no_mice_included,:);
mean_dcsh=mean(all_discriminant_correct_shuffled,1)';
if size(all_discriminant_correct_shuffled,1)>2
CIdcsh = bootci(1000, {@mean, all_discriminant_correct_shuffled})';
CIdcsh(:,1)=mean_dcsh-CIdcsh(:,1);
CIdcsh(:,2)=CIdcsh(:,2)-mean_dcsh;
[hlCR, hpCR] = boundedline(t,mean_dcsh, CIdcsh, 'k');
else
plot(t,mean_dcsh,'-k')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_shuffled_peak(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
dataAUC=[];
for mouseNo=1:no_mice_included
dataAUC=[dataAUC (mean(all_discriminant_correct_shuffled_peak(mouseNo,(t>=AUC_from)&(t<=AUC_to)),2)-50)/50];
end
pcorr_out.PACii(PACii).shuffled_peak.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
glm_pcorr_dt.data(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=data;
glm_pcorr_dt.dataAUC(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=dataAUC;
glm_pcorr_dt.group(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=groupNo;
glm_pcorr_dt.perCorr(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=1;
glm_pcorr_dt.pts(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=3;
glm_ii_pcdt=glm_ii_pcdt+length(data);
datat=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_shuffled_trough(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
datat=[datat mean(all_discriminant_correct_shuffled_trough(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
for mouseNo=1:no_mice_included
dataAUC=[dataAUC (mean(all_discriminant_correct_shuffled_trough(mouseNo,(t>=AUC_from)&(t<=AUC_to)),2)-50)/50];
end
pcorr_out.PACii(PACii).shuffled_trough.pcorr(percent_correct_ii).group(groupNo).odor_data=datat;
pcorr_out.PACii(PACii).shuffled.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
ii_stats=ii_stats+1;
p_correct_stats(ii_stats).data=data;
p_correct_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' handles_out.drgbchoices.per_lab{percent_correct_ii} ' shuffled'];
glm_pcorr_dt.data(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=data;
glm_pcorr_dt.dataAUC(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=dataAUC;
glm_pcorr_dt.group(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=groupNo;
glm_pcorr_dt.perCorr(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=1;
glm_pcorr_dt.pts(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=3;
glm_ii_pcdt=glm_ii_pcdt+length(data);
%Now plot the percent correct for the trough
all_discriminant_correct_trough=all_discriminant_correct_trough(1:no_mice_included,:);
mean_dc_trough=mean(all_discriminant_correct_trough,1)';
if size(all_discriminant_correct_trough,1)>2
CIdc_trough = bootci(1000, {@mean, all_discriminant_correct_trough})';
CIdc_trough(:,1)=mean_dc_trough-CIdc_trough(:,1);
CIdc_trough(:,2)=CIdc_trough(:,2)-mean_dc_trough;
[hlCR, hpCR] = boundedline(t,mean_dc_trough, CIdc_trough, 'b');
else
plot(t,mean_dc_trough,'b')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_trough(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
dataAUC=[];
for mouseNo=1:no_mice_included
dataAUC=[dataAUC (mean(all_discriminant_correct_trough(mouseNo,(t>=AUC_from)&(t<=AUC_to)),2)-50)/50];
end
pcorr_out.PACii(PACii).trough.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
pcorr_out.PACii(PACii).trough.pcorr(percent_correct_ii).group(groupNo).odor_dataAUC=dataAUC;
ii_stats=ii_stats+1;
p_correct_stats(ii_stats).data=data;
p_correct_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' handles_out.drgbchoices.per_lab{percent_correct_ii} ' trough'];
glm_pcorr_dt.data(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=data;
glm_pcorr_dt.dataAUC(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=dataAUC;
glm_pcorr_dt.group(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=groupNo;
glm_pcorr_dt.perCorr(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=0;
glm_pcorr_dt.pts(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=2;
glm_ii_pcdt=glm_ii_pcdt+length(data);
glm_pcorr_dt_trough.data(glm_ii_pcdt_t+1:glm_ii_pcdt_t+length(data))=data;
glm_pcorr_dt_trough.dataAUC(glm_ii_pcdt_t+1:glm_ii_pcdt_t+length(data))=dataAUC;
glm_pcorr_dt_trough.group(glm_ii_pcdt_t+1:glm_ii_pcdt_t+length(data))=groupNo;
glm_pcorr_dt_trough.perCorr(glm_ii_pcdt_t+1:glm_ii_pcdt_t+length(data))=percent_correct_ii;
glm_ii_pcdt_t=glm_ii_pcdt_t+length(data);
ii_t_stats=ii_t_stats+1;
p_corr_dt_trough_stats(ii_t_stats).data=data;
p_corr_dt_trough_stats(ii_t_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}];
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_trough(mouseNo,(t>=reinf_from)&(t<=reinf_to)),2)];
end
pcorr_out.PACii(PACii).trough.pcorr(percent_correct_ii).group(groupNo).reinf_data=data;
%Now plot the percent correct for the peak
all_discriminant_correct_peak=all_discriminant_correct_peak(1:no_mice_included,:);
mean_dc_peak=mean(all_discriminant_correct_peak,1)';
if size(all_discriminant_correct_peak,1)>2
CIdc_peak = bootci(1000, {@mean, all_discriminant_correct_peak})';
CIdc_peak(:,1)=mean_dc_peak-CIdc_peak(:,1);
CIdc_peak(:,2)=CIdc_peak(:,2)-mean_dc_peak;
[hlCR, hpCR] = boundedline(t,mean_dc_peak, CIdc_peak, 'r');
else
plot(t,mean_dc_peak,'r')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_peak(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
dataAUC=[];
for mouseNo=1:no_mice_included
dataAUC=[dataAUC (mean(all_discriminant_correct_peak(mouseNo,(t>=AUC_from)&(t<=AUC_to)),2)-50)/50];
end
pcorr_out.PACii(PACii).peaks.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
pcorr_out.PACii(PACii).peaks.pcorr(percent_correct_ii).group(groupNo).odor_dataAUC=dataAUC;
ii_stats=ii_stats+1;
p_correct_stats(ii_stats).data=data;
p_correct_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}...
' peak'];
glm_pcorr_dt.data(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=data;
glm_pcorr_dt.dataAUC(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=dataAUC;
glm_pcorr_dt.PACii(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=PACii;
glm_pcorr_dt.group(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=groupNo;
glm_pcorr_dt.perCorr(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=0;
glm_pcorr_dt.pts(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=1;
glm_ii_pcdt=glm_ii_pcdt+length(data);
glm_pcorr_dt_peak.data(glm_ii_pcdt_p+1:glm_ii_pcdt_p+length(data))=data;
glm_pcorr_dt_peak.dataAUC(glm_ii_pcdt_p+1:glm_ii_pcdt_p+length(data))=dataAUC;
glm_pcorr_dt_peak.group(glm_ii_pcdt_p+1:glm_ii_pcdt_p+length(data))=groupNo;
glm_pcorr_dt_peak.perCorr(glm_ii_pcdt_p+1:glm_ii_pcdt_p+length(data))=percent_correct_ii;
glm_ii_pcdt_p=glm_ii_pcdt_p+length(data);
ii_p_stats=ii_p_stats+1;
p_corr_dt_peak_stats(ii_p_stats).data=data;
p_corr_dt_peak_stats(ii_p_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}];
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_correct_peak(mouseNo,(t>=reinf_from)&(t<=reinf_to)),2)];
end
pcorr_out.PACii(PACii).peaks.pcorr(percent_correct_ii).group(groupNo).reinf_data=data;
%Odor on markers
plot([0 0],[0 100],'-k')
odorhl=plot([0 2.5],[10 10],'-k','LineWidth',5);
plot([2.5 2.5],[0 100],'-k')
title(['LDA for theta/' handles_out.drgbchoices.PACnames{PACii} ' ' handles_out.drgbchoices.group_no_names{groupNo} ' ' handles_out.drgbchoices.per_lab{percent_correct_ii}])
xlabel('Time (sec)')
ylabel(['% correct ' handles_out.drgbchoices.per_lab{percent_correct_ii}])
legend('Shuffled','Trough','Peak')
% if figNo==13
% pfft=0;
% end
end
end
%Bar graph plot for odor for peak percent correct
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data)),pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_data,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([45 100])
title(['LDA percent correct for odor for peak theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Bar graph plot for odor for peak percent correct AUC
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC)),pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).odor_dataAUC,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([0 0.9])
title(['LDA AUC percent correct for odor for peak theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Bar graph plot for reinforcement for peak percent correct
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data)),pcorr_out.PACii(PACii).peaks.pcorr(per_ii).group(grNo).reinf_data,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([45 100])
title(['LDA percent correct for reinforcement for peak theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Bar graph plot for odor for trough percent correct
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data)),pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_data,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([45 100])
title(['LDA percent correct for odor for trough theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Bar graph plot for odor for trough percent correct AUC
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC)),pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).odor_dataAUC,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([45 100])
title(['LDA AUC percent correct for odor for trough theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Bar graph plot for reinforcment for trough percent correct
%Plot the average
figNo = figNo +1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.1 .5 .7 .4])
hold on
ax=gca;ax.LineWidth=3;
bar_offset = 0;
for per_ii=2:-1:1
for grNo=1:max(handles_out.drgbchoices.group_no)
bar_offset = bar_offset +1;
switch grNo
case 1
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data),'g','LineWidth', 3,'EdgeColor','none')
case 2
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data),'b','LineWidth', 3,'EdgeColor','none')
case 3
bar(bar_offset,mean(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data),'y','LineWidth', 3,'EdgeColor','none')
end
try
CI = bootci(1000, {@mean, pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data},'type','cper');
plot([bar_offset bar_offset],CI,'-k','LineWidth',3)
plot(bar_offset*ones(1,length(pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data)),pcorr_out.PACii(PACii).trough.pcorr(per_ii).group(grNo).reinf_data,'o','MarkerFaceColor', [0.7 0.7 0.7],'MarkerEdgeColor',[0 0 0],'MarkerSize',5)
catch
end
end
bar_offset = bar_offset + 2;
end
ylim([45 100])
title(['LDA percent correct for reinforcement for trough theta/' handles_out.drgbchoices.PACnames{PACii} ])
xticks([1 2 3 5 6 7])
xticklabels({'nwt', 'nH', 'nKO', 'pwt', 'pH', 'pKO'})
ylabel('Percent correct')
%Perform the glm
fprintf(1, ['\n\nglm for mean percent correct peak Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
tbl = table(glm_pcorr_dt_peak.data',glm_pcorr_dt_peak.group',glm_pcorr_dt_peak.perCorr',...
'VariableNames',{'mean_pc','group','naive_proficient'});
mdl = fitglm(tbl,'mean_pc~group+naive_proficient+group*naive_proficient'...
,'CategoricalVars',[2,3])
%Do ranksum/t test
fprintf(1, ['\n\nRanksum or t-test p values for mean percent correct peak Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
try
[output_data] = drgMutiRanksumorTtest(p_corr_dt_peak_stats);
fprintf(1, '\n\n')
catch
end
%Perform the glm
fprintf(1, ['\n\nglm for mean percent correct trough Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
tbl = table(glm_pcorr_dt_trough.data',glm_pcorr_dt_trough.group',glm_pcorr_dt_trough.perCorr',...
'VariableNames',{'mean_pc','group','naive_proficient'});
mdl = fitglm(tbl,'mean_pc~group+naive_proficient+group*naive_proficient'...
,'CategoricalVars',[2,3])
%Do ranksum/t test
fprintf(1, ['\n\nRanksum or t-test p values for mean percent correct trough Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
try
[output_data] = drgMutiRanksumorTtest(p_corr_dt_peak_stats);
fprintf(1, '\n\n')
catch
end
%Perform the glm
fprintf(1, ['\n\nglm for mean percent correct for Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
tbl = table(glm_pcorr_dt.data',glm_pcorr_dt.group',glm_pcorr_dt.perCorr',glm_pcorr_dt.pts',...
'VariableNames',{'mean_pc','group','perCorr','peak_trough_shuffled'});
mdl = fitglm(tbl,'mean_pc~group+perCorr+peak_trough_shuffled+group*peak_trough_shuffled*perCorr'...
,'CategoricalVars',[2,3,4])
%Do ranksum/t test
fprintf(1, ['\n\nRanksum or t-test p values for mean percent correct for Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
try
[output_data] = drgMutiRanksumorTtest(p_correct_stats);
fprintf(1, '\n\n')
catch
end
%Perform the glm
fprintf(1, ['\n\nglm for mean AUC percent correct for Theta/' handles_out.drgbchoices.PACnames{PACii} '\n'])
tbl = table(glm_pcorr_dt.dataAUC',glm_pcorr_dt.group',glm_pcorr_dt.perCorr',glm_pcorr_dt.pts',...
'VariableNames',{'mean_pc','group','perCorr','peak_trough_shuffled'});
mdl = fitglm(tbl,'mean_pc~group+perCorr+peak_trough_shuffled+group*peak_trough_shuffled*perCorr'...
,'CategoricalVars',[2,3,4])
end
%Now plot log(p) and find decision times
disc_time=[];
for PACii=these_PACii
glm_d_time=[];
glm_d_t_ii=0;
t_det_stats=[];
ii_stats=0;
for percent_correct_ii=1:2
for groupNo=1:max(handles_out.drgbchoices.group_no)
%Gather all the data
no_mice=0;
no_mice_included=0;
all_discriminant_p_val_lick=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
all_discriminant_p_val_peak=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
all_discriminant_p_val_trough=zeros(max(handles_out.drgbchoices.mouse_no),length(t));
for mouseNo=1:length(handles_out.discriminant_PACwavepower)
try
if handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).discriminant_calculated==1
per_ii=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).no_trials;
no_mice=no_mice+1;
if (per_ii>=20)&(sum(no_mice==mice_excluded)==0)
no_mice_included=no_mice_included+1;
pval_out.PACii(PACii).pcorr(percent_correct_ii).group(groupNo).mouseNos(no_mice_included)=mouseNo;
disc_time.PACii(PACii).pcorr(percent_correct_ii).group(groupNo).mouseNos(no_mice_included)=mouseNo;
all_discriminant_p_val_lick(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).p_val_lick;
all_discriminant_p_val_peak(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).p_val_peak;
all_discriminant_p_val_trough(no_mice_included,:)=handles_out.discriminant_PACwavepower(mouseNo).group(groupNo).percent_correct(percent_correct_ii).PACii(PACii).p_val_trough;
end
end
catch
end
end
no_mice_per(percent_correct_ii)=no_mice_included;
%Plot the p value timecourse and compute the decision time
figNo=figNo+1;
try
close(figNo)
catch
end
hFig=figure(figNo);
set(hFig, 'units','normalized','position',[.61 .4 .38 .38])
hold on
ax=gca;ax.LineWidth=3;
all_discriminant_p_val_lick=log10(all_discriminant_p_val_lick(1:no_mice_included,:));
all_discriminant_p_val_peak=log10(all_discriminant_p_val_peak(1:no_mice_included,:));
all_discriminant_p_val_trough=log10(all_discriminant_p_val_trough(1:no_mice_included,:));
mean_p_val_licks=nanmean(all_discriminant_p_val_lick,1)';
if size(all_discriminant_p_val_lick,1)>2
CIlicks = bootci(1000, {@nanmean, all_discriminant_p_val_lick})';
CIlicks(:,1)=mean_p_val_licks-CIlicks(:,1);
CIlicks(:,2)=CIlicks(:,2)-mean_p_val_licks;
[hlick, hpCR] = boundedline(t,mean_p_val_licks, CIlicks, 'k');
else
plot(t,mean_p_val_licks,'-k')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_p_val_lick(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
pval_out.PACii(PACii).lick.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
wing=1;
t_detect=zeros(1,no_mice_included);
jj_start=find(t>=t_odor_arrival,1,'first');
%Find the discrimination time
for ii=1:no_mice_included
this_p_val_licks=[];
this_p_val_licks=all_discriminant_p_val_lick(ii,:);
found_disc_t=0;
while (found_disc_t==0)&(jj_start<length(t))
ii_next=find(this_p_val_licks(1,jj_start:end)<=log10(0.05),1,'first');
if isempty(ii_next)
jj_start=length(t);
ii_next=1;
else
if jj_start+ii_next+wing>length(t)
found_disc_t=1;
else
if sum(this_p_val_licks(1,jj_start+ii_next:jj_start+ii_next+wing)>log10(0.05))>=1
jj_start=jj_start+ii_next;
else
found_disc_t=1;
end
end
end
end
t_detect(ii)=t(jj_start+ii_next-1)-t_odor_arrival;
end
fprintf(1, ['Lick discrimination time (sec) %d\n'],mean(t_detect))
lick_t_detect=t_detect;
ii_stats=ii_stats+1;
t_det_stats(ii_stats).data=t_detect;
t_det_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}...
' licks'];
glm_d_time.data(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=t_detect;
glm_d_time.group(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=groupNo;
glm_d_time.perCorr(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=0;
glm_d_time.ptl(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=1;
glm_d_t_ii=glm_d_t_ii+length(t_detect);
disc_time.PACii(PACii).licks.pcorr(percent_correct_ii).group(groupNo).data=t_detect;
mean_p_val_troughs=nanmean(all_discriminant_p_val_trough,1)';
if size(all_discriminant_p_val_trough,1)>2
CItroughs = bootci(1000, {@nanmean, all_discriminant_p_val_trough})';
CItroughs(:,1)=mean_p_val_troughs-CItroughs(:,1);
CItroughs(:,2)=CItroughs(:,2)-mean_p_val_troughs;
[hltrough, hpCR] = boundedline(t,mean_p_val_troughs, CItroughs, 'b');
else
plot(t,mean_p_val_troughs,'-b')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_p_val_trough(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
pval_out.PACii(PACii).trough.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
t_detect=zeros(1,no_mice_included);
jj_start=find(t>=t_odor_arrival,1,'first');
%Find the discrimination time
for ii=1:no_mice_included
this_p_val_troughs=[];
this_p_val_troughs=all_discriminant_p_val_trough(ii,:);
found_disc_t=0;
while (found_disc_t==0)&(jj_start<length(t))
ii_next=find(this_p_val_troughs(1,jj_start:end)<=log10(0.05),1,'first');
if isempty(ii_next)
jj_start=length(t);
ii_next=1;
else
if jj_start+ii_next+wing>length(t)
found_disc_t=1;
else
if sum(this_p_val_troughs(1,jj_start+ii_next:jj_start+ii_next+wing)>log10(0.05))>=1
jj_start=jj_start+ii_next;
else
found_disc_t=1;
end
end
end
end
t_detect(ii)=t(jj_start+ii_next-1)-t_odor_arrival;
end
trough_t_detect=t_detect;
fprintf(1, ['Trough discrimination time (sec) %d\n'],mean(t_detect))
ii_stats=ii_stats+1;
t_det_stats(ii_stats).data=t_detect;
t_det_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}...
' troughs'];
disc_time.PACii(PACii).trough.pcorr(percent_correct_ii).group(groupNo).data=t_detect;
glm_d_time.data(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=t_detect;
glm_d_time.group(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=groupNo;
glm_d_time.perCorr(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=0;
glm_d_time.ptl(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=2;
glm_d_t_ii=glm_d_t_ii+length(t_detect);
mean_p_val_peaks=nanmean(all_discriminant_p_val_peak,1)';
if size(all_discriminant_p_val_peak,1)>2
CIpeaks = bootci(1000, {@nanmean, all_discriminant_p_val_peak})';
CIpeaks(:,1)=mean_p_val_peaks-CIpeaks(:,1);
CIpeaks(:,2)=CIpeaks(:,2)-mean_p_val_peaks;
[hpeak, hpCR] = boundedline(t,mean_p_val_peaks, CIpeaks, 'r');
else
plot(t,mean_p_val_peaks,'-r')
end
data=[];
for mouseNo=1:no_mice_included
data=[data mean(all_discriminant_p_val_peak(mouseNo,(t>=odor_from)&(t<=odor_to)),2)];
end
pval_out.PACii(PACii).peaks.pcorr(percent_correct_ii).group(groupNo).odor_data=data;
t_detect=zeros(1,no_mice_included);
jj_start=find(t>=t_odor_arrival,1,'first');
%Find the discrimination time
for ii=1:no_mice_included
this_p_val_peaks=[];
this_p_val_peaks=all_discriminant_p_val_peak(ii,:);
found_disc_t=0;
while (found_disc_t==0)&(jj_start<length(t))
ii_next=find(this_p_val_peaks(1,jj_start:end)<=log10(0.05),1,'first');
if isempty(ii_next)
jj_start=length(t);
ii_next=1;
else
if jj_start+ii_next+wing>length(t)
found_disc_t=1;
else
if sum(this_p_val_peaks(1,jj_start+ii_next:jj_start+ii_next+wing)>log10(0.05))>=1
jj_start=jj_start+ii_next;
else
found_disc_t=1;
end
end
end
end
t_detect(ii)=t(jj_start+ii_next-1)-t_odor_arrival;
end
fprintf(1, ['Peak discrimination time (sec) %d\n'],mean(t_detect))
peak_t_detect=t_detect;
ii_stats=ii_stats+1;
t_det_stats(ii_stats).data=t_detect;
t_det_stats(ii_stats).description=[handles_out.drgbchoices.group_no_names{groupNo} ' ' ...
handles_out.drgbchoices.per_lab{percent_correct_ii}...
' peaks'];
disc_time.PACii(PACii).peaks.pcorr(percent_correct_ii).group(groupNo).data=t_detect;
glm_d_time.data(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=t_detect;
glm_d_time.group(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=groupNo;
glm_d_time.perCorr(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=percent_correct_ii;
% glm_pcorr_dt.shuffled(glm_ii_pcdt+1:glm_ii_pcdt+length(data))=0;
glm_d_time.ptl(glm_d_t_ii+1:glm_d_t_ii+length(t_detect))=3;
glm_d_t_ii=glm_d_t_ii+length(t_detect);
plot(t,mean_p_val_licks,'-k')
plot(t,mean_p_val_troughs,'-b')
plot(t,mean_p_val_peaks,'-r')
plot([t(1) t(end)],[log10(0.05) log10(0.05)],'-r')