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drgCaImAn_SVZ_entire_session_shuffling.m
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function handles_out=drgCaImAn_SVZ_entire_session_shuffling(handles_choices)
%This program trains several decoding algorithms with the post odorant and then determines what happens throughout the entire timecouse
%The user enters the choices entered under exist('handles_choices')==0
%
% processing_algorithm= 1 and 2 were used for troublehsooting and do not
% produce reliable results because of overtraining, use
% processing_algoritm=3, that was vetted for our manuscript
%
%
% the input is a pre_per file version 2
if exist('handles_choices')==0
clear all
close all
%Load file
[pre_perFileName,pre_perPathName] = uigetfile({'*pre_per.mat'},'Select the .m file with all the choices for analysis');
processing_algorithm=3; %Use 3
k_fold=5; %Only used for processing_algorithm=2,
post_time=5; %The decoding model will be trained with all points in post_time sec interval starting post_shift secs after odor on
post_shift=0.5; %Set to 0 if you want to train with odor on points
pre_time=5; %Used to calculate the decoding accuracy pre_time sec before post_shift
MLalgo_to_use=[6]; %Vector with the decoding algorithms you want to use
ii_cost=3;
p_threshold=1.1; %This limits the ROIs used in the decoding model to those whose p value in a ttest for the two odors in <=p_threshold
dt_p_threshold=20; %Time to be used after the odor on for the p_threshold t_test
show_figures=1; %Show the figures
perm_before=0; %Permute the labels before running the decoder
handles_choices.pre_perFileName=pre_perFileName;
handles_choices.pre_perPathName=pre_perPathName;
handles_choices.processing_algorithm=processing_algorithm;
handles_choices.post_time=post_time;
handles_choices.k_fold=k_fold;
handles_choices.post_shift=post_shift;
handles_choices.MLalgo_to_use=MLalgo_to_use;
handles_choices.pre_time=pre_time;
handles_choices.p_threshold=p_threshold;
handles_choices.dt_p_threshold=dt_p_threshold;
handles_choices.show_figures=show_figures;
handles_choices.ii_cost=ii_cost;
handles_choices.perm_before=perm_before;
else
pre_perPathName=handles_choices.pre_per_PathName;
pre_perFileName=handles_choices.pre_per_FileName;
processing_algorithm=handles_choices.processing_algorithm;
post_time=handles_choices.post_time;
k_fold=handles_choices.k_fold;
post_shift=handles_choices.post_shift;
MLalgo_to_use=handles_choices.MLalgo_to_use;
pre_time=handles_choices.pre_time;
p_threshold=handles_choices.p_threshold;
dt_p_threshold=handles_choices.dt_p_threshold;
show_figures=handles_choices.show_figures;
ii_cost=handles_choices.ii_cost;
if isfield(handles_choices,'perm_before')
perm_before=handles_choices.perm_before;
else
perm_before=0;
end
end
tic
warning('off')
%Restart random seeds
rng('shuffle');
convert_z=0; %Convert dFF traces to z
dt_span=40; %Seconds for per trial traces centered on odor on
moving_mean_n=30; %Points used to calculate moving mean for the prediction label figure
no_shuffles=10; %Number of shuffles for per trial shuffling
window_no=2;
time_windows=[-1 0;
2 4.1];
load([pre_perPathName pre_perFileName])
fprintf(1, ['\ndrgCaImAn_SVZ_entire_session run for ' pre_perFileName '\n\n']);
fprintf(1, 'post_time = %d, p_threshold= %d, post_shift= %d, cost %d\n',post_time,p_threshold,post_shift, ii_cost);
if convert_z==1
for trace_no=1:size(traces,1)
traces(trace_no,:)=traces(trace_no,:)/std(traces(trace_no,:));
end
end
classifier_names{1}='Linear Discriminant';
classifier_names{2}='Support Vector Machine';
classifier_names{3}='Naive Bayes Classifier';
classifier_names{4}='Neural Network';
classifier_names{5}='Decision tree';
classifier_names{6}='Binomial glm';
handles_out.handles_choices=handles_choices;
handles_out.pre_perFileName=pre_perFileName;
handles_out.pre_perPathName=pre_perPathName;
handles_out.post_time=post_time;
handles_out.k_fold=k_fold;
handles_out.post_shift=post_shift;
handles_out.pre_time=pre_time;
handles_not_out.MLalgo_to_use=MLalgo_to_use;
figNo=0;
%time has the time for the dF/F traces(ROI,time)
if show_figures==1
figNo=figNo+1;
try
close(figNo)
catch
end
hFig = figure(figNo);
set(hFig, 'units','normalized','position',[.05 .1 .85 .8])
hold on
% Determine the y spacing of the traces
y_shift=1.2*(prctile(traces(:),95)-prctile(traces(:),5));
%Plot the traces and do z normalization
%For S+ and S- plot odor on and reinforcement
for epoch=1:handles.dropcData.epochIndex
%Epoch 2 is odor on, 3 is odor off
plot_epoch=(handles.dropcData.epochEvent(epoch)==2)||(handles.dropcData.epochEvent(epoch)==3);
if plot_epoch
if handles.dropcData.epochTypeOfOdor(epoch)==handles.dropcProg.splusOdor
plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
'-r','LineWidth',1)
else
plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
'-b','LineWidth',1)
end
end
end
for trNo=1:no_traces
% for trNo=1:20
plot(time,traces(trNo,:)+y_shift*trNo,'-k','LineWidth',1)
end
ylim([-y_shift*0.2 (no_traces+2)*y_shift])
xlabel('time(sec)')
title(['All dFF timecourses ' num2str(size(traces,1)) ' ROIs'])
end
%epochs is a vector of the length of time that gives information on
%behavior
% 1=Final Valve
% 6=Hit (on for the duration of odor on)
% 7=Miss
% 8=FA
% 9=CR
%For example Hit||Miss shows S+ odor application times (red)
%and FA||CR gives S- (blue)
%Post points
Nall=size(traces,1);
dt=median(time(2:end)-time(1:end-1));
ii_p_threshold=ceil(dt_p_threshold/dt);
no_points_post=floor(post_time/dt);
no_points_post_shift=floor(post_shift/dt);
no_points_pre=floor(pre_time/dt);
measurements_post=[];
measurements_pre=[];
epochs_sp_post=zeros(1,length(time));
epochs_sp_pre=zeros(1,length(time));
epochs_sm_post=zeros(1,length(time));
epochs_sm_pre=zeros(1,length(time));
which_model_for_traces_loo=no_odor_trials*ones(1,size(traces,2));
%Do both S+ and S-
at_end=0;
this_ii=0;
ii_post=0;
ii_pre=0;
ii=0;
trial_no=0;
ii_sp_post=0;
ii_sm_post=0;
ii_which_model=0;
dt_post_which_model=floor(20/dt); %Points that model will be used beyond the training period
ii_span=ceil(dt_span/dt);
dFF_per_trial_sp=[];
dFF_per_trial_sm=[];
dFFs_sp_per_trial_per_ROI=[];
dFFs_sm_per_trial_per_ROI=[];
hit_per_trial=[];
miss_per_trial=[];
cr_per_trial=[];
fa_per_trial=[];
%training_decisions is 1 if S+ and 2 if S-
%epochs has masks for the following epochs
% 1 - FV on
% 2 - odor on
% 3 - odor off
% 4 - reinforcement on
% 5 - reinforcement off
% 6 - Hit
% 7 - Miss
% 8 - FA
% 9 - CR
while (at_end==0)
%6 is hit, 7 is miss, 8 FA and 9 CR
next_ii_sp=find((epochs(this_ii+1:end)==7)|(epochs(this_ii+1:end)==6),1,'first');
delta_ii_sp=find(epochs(this_ii+next_ii_sp:end)~=epochs(this_ii+next_ii_sp),1,'first');
next_ii_sm=find((epochs(this_ii+1:end)==8)|(epochs(this_ii+1:end)==9),1,'first');
delta_ii_sm=find(epochs(this_ii+next_ii_sm:end)~=epochs(this_ii+next_ii_sm),1,'first');
if (isempty(next_ii_sp))&(isempty(next_ii_sm))
at_end=1;
else
if isempty(next_ii_sm)
%This is S+
if epochs(this_ii+1+next_ii_sp)==6
hit_per_trial=[hit_per_trial 1];
miss_per_trial=[miss_per_trial 0];
else
hit_per_trial=[hit_per_trial 0];
miss_per_trial=[miss_per_trial 1];
end
cr_per_trial=[cr_per_trial 0];
fa_per_trial=[fa_per_trial 0];
next_ii=next_ii_sp;
if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)&(no_points_post_shift+this_ii+next_ii+ii_span<=length(epochs))
measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
training_decisions_post(ii_post+1:ii_post+no_points_post,:)=1;
ii_sp_post=ii_sp_post+1;
dFFs_sp_per_trial_per_ROI(ii_sp_post,:,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+ii_p_threshold);
dFFs_this_trial=traces(:,no_points_post_shift+this_ii+next_ii-ii_span:no_points_post_shift+this_ii+next_ii+ii_span);
dFF_per_trial_sp(ii_sp_post,:,:)=dFFs_this_trial;
trial_no=trial_no+1;
decisions_per_trial(trial_no)=1;
which_model_for_traces_loo(1,ii_which_model+1:no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model)=trial_no;
ii_which_model=no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model;
ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
epochs_sp_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
epochs_sp_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
this_ii=this_ii+next_ii+delta_ii_sp-1;
ii_post=ii_post+no_points_post;
ii_pre=ii_pre+no_points_pre;
else
if (no_points_post_shift+this_ii+next_ii+no_points_post>length(epochs))||(no_points_post_shift+this_ii+next_ii-ii_span>0)&(no_points_post_shift+this_ii+next_ii+ii_span>length(epochs))
at_end=1;
else
this_ii=this_ii+next_ii+delta_ii_sp-1;
end
end
end
if isempty(next_ii_sp)
%This is S-
if epochs(this_ii+1+next_ii_sm)==9
cr_per_trial=[cr_per_trial 1];
fa_per_trial=[fa_per_trial 0];
else
cr_per_trial=[cr_per_trial 0];
fa_per_trial=[fa_per_trial 1];
end
hit_per_trial=[hit_per_trial 0];
miss_per_trial=[miss_per_trial 0];
next_ii=next_ii_sm;
if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)...
&(no_points_post_shift+this_ii+next_ii+ii_span<=length(epochs))&(no_points_post_shift+this_ii+next_ii-ii_span>0)
measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
training_decisions_post(ii_post+1:ii_post+no_points_post,:)=0;
ii_sm_post=ii_sm_post+1;
dFFs_sm_per_trial_per_ROI(ii_sm_post,:,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+ii_p_threshold);
dFFs_this_trial=traces(:,no_points_post_shift+this_ii+next_ii-ii_span:no_points_post_shift+this_ii+next_ii+ii_span);
dFF_per_trial_sm(ii_sm_post,:,:)=dFFs_this_trial;
trial_no=trial_no+1;
decisions_per_trial(trial_no)=0;
which_model_for_traces_loo(1,ii_which_model+1:no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model)=trial_no;
ii_which_model=no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model;
ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
epochs_sm_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
epochs_sm_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
this_ii=this_ii+next_ii+delta_ii_sm-1;
ii_post=ii_post+no_points_post;
ii_pre=ii_pre+no_points_pre;
else
if (no_points_post_shift+this_ii+next_ii+no_points_post>length(epochs))||(no_points_post_shift+this_ii+next_ii-ii_span>0)&(no_points_post_shift+this_ii+next_ii+ii_span>length(epochs))
at_end=1;
else
this_ii=this_ii+next_ii+delta_ii_sm-1;
end
end
end
if (~isempty(next_ii_sp))&(~isempty(next_ii_sm))
if next_ii_sm<next_ii_sp
%This is S-
next_ii=next_ii_sm;
if epochs(this_ii+1+next_ii_sm)==9
cr_per_trial=[cr_per_trial 1];
fa_per_trial=[fa_per_trial 0];
else
cr_per_trial=[cr_per_trial 0];
fa_per_trial=[fa_per_trial 1];
end
hit_per_trial=[hit_per_trial 0];
miss_per_trial=[miss_per_trial 0];
if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)...
&(no_points_post_shift+this_ii+next_ii+ii_span<=length(epochs))&(no_points_post_shift+this_ii+next_ii-ii_span>0)
measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
training_decisions_post(ii_post+1:ii_post+no_points_post,:)=0;
ii_sm_post=ii_sm_post+1;
dFFs_sm_per_trial_per_ROI(ii_sm_post,:,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+ii_p_threshold);
dFFs_this_trial=traces(:,no_points_post_shift+this_ii+next_ii-ii_span:no_points_post_shift+this_ii+next_ii+ii_span);
dFF_per_trial_sm(ii_sm_post,:,:)=dFFs_this_trial;
trial_no=trial_no+1;
decisions_per_trial(trial_no)=0;
which_model_for_traces_loo(1,ii_which_model+1:no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model)=trial_no;
ii_which_model=no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model;
ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
epochs_sm_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
epochs_sm_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
this_ii=this_ii+next_ii+delta_ii_sm-1;
ii_post=ii_post+no_points_post;
ii_pre=ii_pre+no_points_pre;
else
if (no_points_post_shift+this_ii+next_ii+no_points_post>length(epochs))||(no_points_post_shift+this_ii+next_ii-ii_span>0)&(no_points_post_shift+this_ii+next_ii+ii_span>length(epochs))
at_end=1;
else
this_ii=this_ii+next_ii+delta_ii_sm-1;
end
end
else
%This is S+
next_ii=next_ii_sp;
if epochs(this_ii+1+next_ii_sp)==6
hit_per_trial=[hit_per_trial 1];
miss_per_trial=[miss_per_trial 0];
else
hit_per_trial=[hit_per_trial 0];
miss_per_trial=[miss_per_trial 1];
end
cr_per_trial=[cr_per_trial 0];
fa_per_trial=[fa_per_trial 0];
if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)...
&(no_points_post_shift+this_ii+next_ii+ii_span<=length(epochs))&(no_points_post_shift+this_ii+next_ii-ii_span>0)
measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
training_decisions_post(ii_post+1:ii_post+no_points_post,:)=1;
ii_sp_post=ii_sp_post+1;
dFFs_sp_per_trial_per_ROI(ii_sp_post,:,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+ii_p_threshold);
dFFs_this_trial=traces(:,no_points_post_shift+this_ii+next_ii-ii_span:no_points_post_shift+this_ii+next_ii+ii_span);
dFF_per_trial_sp(ii_sp_post,:,:)=dFFs_this_trial;
trial_no=trial_no+1;
decisions_per_trial(trial_no)=1;
which_model_for_traces_loo(1,ii_which_model+1:no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model)=trial_no;
ii_which_model=no_points_post_shift+this_ii+next_ii+no_points_post-1+dt_post_which_model;
ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
epochs_sp_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
epochs_sp_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
this_ii=this_ii+next_ii+delta_ii_sp-1;
ii_post=ii_post+no_points_post;
ii_pre=ii_pre+no_points_pre;
else
if (no_points_post_shift+this_ii+next_ii+no_points_post>length(epochs))||(no_points_post_shift+this_ii+next_ii-ii_span>0)&(no_points_post_shift+this_ii+next_ii+ii_span>length(epochs))
at_end=1;
else
this_ii=this_ii+next_ii+delta_ii_sp-1;
end
end
end
end
end
end
%Calculate percent correct
handles_out.percent_correct=100*(sum(hit_per_trial)+sum(cr_per_trial))/length(hit_per_trial);
fprintf(1, 'percent correct behavior = %d\n',handles_out.percent_correct);
which_model_for_traces_loo(which_model_for_traces_loo>trial_no)=trial_no;
%
% %Do S+
% at_end=0;
% this_ii=0;
% ii_post=0;
% ii_pre=0;
% ii=0;
% trial_no=0;
%
% while (at_end==0)
% next_ii=find((epochs(this_ii+1:end)==7)|(epochs(this_ii+1:end)==6),1,'first');
% if ~isempty(next_ii)
% if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)
% measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
% trial_no=trial_no+1;
% which_model_for_traces_loo(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=trial_no;
% ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
% epochs_sp_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
% measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
% epochs_sp_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
% this_ii=this_ii+next_ii+no_points_post;
% ii_post=ii_post+no_points_post;
% ii_pre=ii_pre+no_points_pre;
% else
% at_end=1;
% end
% else
% at_end=1;
% end
% end
%
% training_decisions_post=ones(1,size(measurements_post,1));
% ii_sp_post=size(measurements_post,1);
%
%
% %Do S-
% at_end=0;
% this_ii=0;
% ii=0;
%
%
% while (at_end==0)
% next_ii=find((epochs(this_ii+1:end)==8)|(epochs(this_ii+1:end)==9),1,'first');
% if ~isempty(next_ii)
% if (no_points_post_shift+this_ii+next_ii+no_points_post<length(epochs))&(no_points_post_shift+this_ii+next_ii-no_points_pre>0)
% measurements_post(ii_post+1:ii_post+no_points_post,:)=traces(:,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)';
% trial_no=trial_no+1;
% which_model_for_traces_loo(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=trial_no;
% ii_pointer_to_td(ii_post+1:ii_post+no_points_post)=no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1;
% epochs_sm_post(1,no_points_post_shift+this_ii+next_ii:no_points_post_shift+this_ii+next_ii+no_points_post-1)=1;
% measurements_pre(ii_pre+1:ii_pre+no_points_pre,:)=traces(:,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)';
% epochs_sm_pre(1,no_points_post_shift+this_ii+next_ii-no_points_pre:no_points_post_shift+this_ii+next_ii-1)=1;
% this_ii=this_ii+next_ii+no_points_post;
% ii_post=ii_post+no_points_post;
% ii_pre=ii_pre+no_points_pre;
% else
% at_end=1;
% end
% else
% at_end=1;
% end
% end
%
% ii_sm_post=ii_post-ii_sp_post;
%
% training_decisions_post=[training_decisions_post zeros(1,size(measurements_post,1)-ii_sp_post)];
%Now let's limit the ROIs to those below p_threshold
p_values=ones(1,size(dFFs_sm_per_trial_per_ROI,2));
for iiROI=1:size(dFFs_sm_per_trial_per_ROI,2)
dFF_sm=zeros(size(dFFs_sm_per_trial_per_ROI,1),size(dFFs_sm_per_trial_per_ROI,3));
dFF_sm(:,:)=dFFs_sm_per_trial_per_ROI(:,iiROI,:);
dFF_sp=zeros(size(dFFs_sp_per_trial_per_ROI,1),size(dFFs_sp_per_trial_per_ROI,3));
dFF_sp(:,:)=dFFs_sp_per_trial_per_ROI(:,iiROI,:);
[h,p_values(iiROI)]=ttest2(mean(dFF_sp,2),mean(dFF_sm,2));
end
p_value_mask=logical(p_values<=p_threshold);
%Trim the number of ROIs in all matrices
noROIs_before_trimming=size(measurements_post,2);
dFF_per_trial_sp=dFF_per_trial_sp(:,p_value_mask,:);
dFF_per_trial_sm=dFF_per_trial_sm(:,p_value_mask,:);
measurements_post=measurements_post(:,p_value_mask);
measurements_pre=measurements_pre(:,p_value_mask);
traces=traces(p_value_mask,:);
no_traces=size(traces,1);
%Save odor times
handles_out.sp_times=[];
handles_out.sp_times_ii=0;
handles_out.sm_times=[];
handles_out.sm_times_ii=0;
%For S+ and S- plot odor on and reinforcement
for epoch=1:handles.dropcData.epochIndex
%Epoch 2 is odor on, 3 is odor off
plot_epoch=(handles.dropcData.epochEvent(epoch)==2)||(handles.dropcData.epochEvent(epoch)==3);
if plot_epoch
if handles.dropcData.epochTypeOfOdor(epoch)==handles.dropcProg.splusOdor
handles_out.sp_times_ii=handles_out.sp_times_ii+1;
handles_out.sp_times(handles_out.sp_times_ii)=handles.dropcData.epochTime(epoch);
% plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
% '-r','LineWidth',1)
else
handles_out.sm_times_ii=handles_out.sm_times_ii+1;
handles_out.sm_times(handles_out.sm_times_ii)=handles.dropcData.epochTime(epoch);
% plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
% '-b','LineWidth',1)
end
end
end
%Plot the trimmed traces
%time has the time for the dF/F traces(ROI,time)
if show_figures==1
figNo=figNo+1;
try
close(figNo)
catch
end
hFig = figure(figNo);
set(hFig, 'units','normalized','position',[.05 .1 .85 .8])
hold on
% Determine the y spacing of the traces
y_shift=1.2*(prctile(traces(:),95)-prctile(traces(:),5));
%Plot the traces and do z normalization
%For S+ and S- plot odor on and reinforcement
for epoch=1:handles.dropcData.epochIndex
%Epoch 2 is odor on, 3 is odor off
plot_epoch=(handles.dropcData.epochEvent(epoch)==2)||(handles.dropcData.epochEvent(epoch)==3);
if plot_epoch
if handles.dropcData.epochTypeOfOdor(epoch)==handles.dropcProg.splusOdor
plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
'-r','LineWidth',1)
else
plot([handles.dropcData.epochTime(epoch) handles.dropcData.epochTime(epoch)], [0 (no_traces+2)*y_shift],...
'-b','LineWidth',1)
end
end
end
for trNo=1:no_traces
% for trNo=1:20
plot(time,traces(trNo,:)+y_shift*trNo,'-k','LineWidth',1)
end
ylim([-y_shift*0.2 (no_traces+2)*y_shift])
xlabel('time(sec)')
title(['dFF timecourses after p value trimming ' num2str(size(measurements_post,2)) ' ROIs'])
end
%Euclidean distance
%For each time point
for ii_t=1:size(dFF_per_trial_sp,3)
jj=0;
d=[];
for ii_sp=1:size(dFF_per_trial_sp,1)
for ii_sm=1:size(dFF_per_trial_sm,1)
sum_of_sq=0;
for iiROI=1:size(dFF_per_trial_sm,2)
sum_of_sq=sum_of_sq+(dFF_per_trial_sp(ii_sp,iiROI,ii_t)-dFF_per_trial_sm(ii_sm,iiROI,ii_t))^2;
end
jj=jj+1;
d(jj)=sqrt(sum_of_sq);
end
end
dist_euclid(ii_t)=mean(d);
end
handles_out.dist_euclid=dist_euclid;
time_span=[0:dt:dt*size(dFF_per_trial_sp,3)]-dt_span+dt;
time_span=time_span(1:end-1)+post_shift;
handles_out.time_span=time_span;
handles_out.dist_euclid_zero=mean(dist_euclid((time_span>-20)&(time_span<=0)));
%Kullback-Leibler divergence
KLdivergence=zeros(1,size(dFF_per_trial_sp,3));
for ii_t=1:size(dFF_per_trial_sp,3)
z_sp_mean=zeros(1,size(dFF_per_trial_sp,2));
these_z_sp=zeros(size(dFF_per_trial_sp,1),size(dFF_per_trial_sp,2));
these_z_sp(:,:)=dFF_per_trial_sp(:,:,ii_t);
z_sp_mean(1,:)=mean(these_z_sp,1);
z_sm_mean=zeros(1,size(dFF_per_trial_sm,2));
these_z_sm=zeros(size(dFF_per_trial_sm,1),size(dFF_per_trial_sm,2));
these_z_sm(:,:)=dFF_per_trial_sm(:,:,ii_t);
z_sm_mean(1,:)=mean(these_z_sm,1);
%Use pdist to find all distances for odor 1
ii_sp=0;
ii_sm=0;
distances_sp=zeros(1,size(dFF_per_trial_sp,1));
distances_sm=zeros(1,size(dFF_per_trial_sm,1));
for ii_sp=1:size(dFF_per_trial_sp,1)
odor_queary=zeros(1,size(dFF_per_trial_sp,2));
odor_queary(1,:)=these_z_sp(ii_sp,:);
all_points=[z_sp_mean; z_sm_mean; odor_queary];
all_distances=pdist(all_points);
d12=all_distances(1);
dq1=all_distances(2);
dq2=all_distances(3);
distances_sp(1,ii_sp)=(d12^2 + dq1^2 -dq2^2)/(2*d12);
end
for ii_sm=1:size(dFF_per_trial_sm,1)
odor_queary=zeros(1,size(dFF_per_trial_sm,2));
odor_queary(1,:)=these_z_sm(ii_sm,:);
all_points=[z_sp_mean; z_sm_mean; odor_queary];
all_distances=pdist(all_points);
d12=all_distances(1);
dq1=all_distances(2);
dq2=all_distances(3);
distances_sm(1,ii_sm)=(d12^2 + dq1^2 -dq2^2)/(2*d12);
end
%KL divergence
num_bins=50;
max_d=max([max(distances_sp) max(distances_sm)]);
min_d=min([min(distances_sp) min(distances_sm)]);
X=[min_d:(max_d-min_d)/(num_bins-1):max_d];
p1=zeros(1,length(X));
p2=zeros(1,length(X));
for ii=1:length(distances_sp)
[min_d min_ii]=min(abs(distances_sp(ii)-X));
p1(min_ii)=p1(min_ii)+1;
end
p1=p1/sum(p1);
p1(p1==0)=eps;
for ii=1:length(distances_sm)
[min_d min_ii]=min(abs(distances_sm(ii)-X));
p2(min_ii)=p2(min_ii)+1;
end
p2=p2/sum(p2);
p2(p2==0)=eps;
KLdivergence(ii_t) = kldiv(X,p1,p2);
end
handles_out.KLdivergence=KLdivergence;
meandFF_per_trial_sp=zeros(size(dFF_per_trial_sp,1),size(dFF_per_trial_sp,3));
meandFF_per_trial_sp(:,:)=mean(dFF_per_trial_sp,2);
meandFF_per_trial_sm=zeros(size(dFF_per_trial_sm,1),size(dFF_per_trial_sm,3));
meandFF_per_trial_sm(:,:)=mean(dFF_per_trial_sm,2);
%Plot the mean timecourse for S+ and S-
if show_figures==1
figNo=figNo+1;
try
close(figNo)
catch
end
hFig = figure(figNo);
set(hFig, 'units','normalized','position',[.05 .1 .3 .6])
subplot(3,1,1)
hold on
%S+ trials
try
CIsp = bootci(1000, @mean, meandFF_per_trial_sp);
meansp=mean(dFF_per_trial_sp,1);
CIsp(1,:)=meansp-CIsp(1,:);
CIsp(2,:)=CIsp(2,:)-meansp;
[hlsp, hpsp] = boundedline(time_span',mean(meandFF_per_trial_sp,1)', CIsp', 'cmap',[80/255 194/255 255/255]);
catch
end
%S-
try
CIsp = bootci(1000, @mean, meandFF_per_trial_sm);
meansp=mean(dFF_per_trial_sm,1);
CIsp(1,:)=meansp-CIsp(1,:);
CIsp(2,:)=CIsp(2,:)-meansp;
[hlsp, hpsp] = boundedline(time_span',mean(meandFF_per_trial_sm,1)', CIsp', 'cmap',[238/255 111/255 179/255]);
catch
end
plot(time_span',mean(meandFF_per_trial_sp,1)', 'Color',[80/255 194/255 255/255]);
plot(time_span',mean(meandFF_per_trial_sm,1)', 'Color',[238/255 111/255 179/255]);
text(30,0.75,'S-','Color',[80/255 194/255 255/255])
text(30,0.65,'S+','Color',[0 114/255 178/255])
ylim([0 1])
title(['Mean dFF'])
xlabel('Time(sec)')
ylabel('dFF')
subplot(3,1,2)
hold on
%Euclidean distance
plot(time_span',(dist_euclid-mean(dist_euclid((time_span>-20)&(time_span<=0))))', 'Color',[238/255 111/255 179/255]);
title(['Euclidean distance'])
xlabel('Time(sec)')
ylabel('Euclidean d')
subplot(3,1,3)
hold on
%KL divergence
plot(time_span',KLdivergence', 'Color',[238/255 111/255 179/255]);
title(['KL divergence'])
xlabel('Time(sec)')
ylabel('KL divergence')
end
handles_out.meandFFsp=mean(meandFF_per_trial_sp,1);
handles_out.meandFFsm=mean(meandFF_per_trial_sm,1);
training_decisions_post_sh=zeros(1,length(training_decisions_post));
training_decisions_post_sh(1,:)=training_decisions_post(randperm(length(training_decisions_post)));
for ii=1:no_shuffles
these_shuffled_decisions_per_trial=decisions_per_trial(randperm(length(decisions_per_trial)));
ww=0;
for jj=1:length(decisions_per_trial)
training_decisions_post_sh2(ii,ww+1:ww+no_points_post)=these_shuffled_decisions_per_trial(jj)*ones(1,no_points_post);
ww=ww+no_points_post;
end
end
% handles_out.Nall=Nall;
handles_out.dt=dt;
% handles_out.no_points_post=no_points_post;
% handles_out.no_points_post_shift=no_points_post_shift;
% handles_out.no_points_pre=no_points_pre;
% handles_out.measurements_post=measurements_post;
% handles_out.measurements_pre=measurements_pre;
% handles_out.training_decisions_post=training_decisions_post;
% handles_out.epochs_sp_post=epochs_sp_post;
% handles_out.epochs_sp_pre=epochs_sp_pre;
% handles_out.epochs_sm_post=epochs_sm_post;
% handles_out.epochs_sm_pre=epochs_sm_pre;
fprintf(1, ['Training post with %d ROIs (original no ROIs %d)...\n'],size(measurements_post,2),noROIs_before_trimming);
for MLalgo=MLalgo_to_use
this_cost=[0 ii_cost;ii_cost 0];
labels=[];
timepoint_processed=[];
correct_predict=[];
correct_predict_shuffled=[];
Nall_post=size(measurements_post,1);
switch processing_algorithm
case 1
%Train with all the post data
%Store the training data in a table.
tblTrn=[];
tblTrn = array2table(measurements_post);
%Store the decisions in Y
Y=training_decisions_post;
switch MLalgo
case 1
Mdl = fitcdiscr(tblTrn,Y,'Cost',this_cost);
case 2
Mdl = fitcsvm(tblTrn,Y,'Cost',this_cost);
case 3
Mdl = fitcnb(tblTrn,Y,'Cost',this_cost);
case 4
Mdl = fitcnet(tblTrn,Y);
case 5
Mdl = fitctree(tblTrn,Y,'Cost',this_cost);
end
%Predict labels for the test set. You trained Mdl using a table of data, but you can predict labels using a matrix.
%Note: for some reason tisdid not work for net when I did this:
% [label_traces,score] = predict(Mdl,traces');
% [label_post,score] = predict(Mdl,measurements_post);
%I had to resolrt to the for loop:
switch MLalgo
case 3,4
for ii=1:size(traces,2)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=traces(:,ii);
[label_traces(ii),score] = predict(Mdl,this_time_point);
end
for ii=1:size(measurements_post,1)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=measurements_post(ii,:);
[label_post(ii),score] = predict(Mdl,this_time_point);
end
otherwise
[label_traces,score] = predict(Mdl,traces');
[label_post,score] = predict(Mdl,measurements_post);
end
case 2
%k-fold cross validation evaluating performance with left-out data
%Store the training data in a table.
handles_not_out.MLalgo(MLalgo).models=[];
handles_not_out.MLalgo(MLalgo).processed_succesfully=1;
points_masked=floor(no_points_post/k_fold);
which_model=ones(1,size(measurements_post,1));
for kk=1:k_fold
training_mask=ones(size(measurements_post,1),1);
at_end=0;
ii=0;
while at_end==0
if ii+no_points_post<=size(measurements_post,1)
%This is what I originally used, these samples are adjacent
% training_mask(ii+(kk-1)*points_masked+1:ii+(kk-1)*points_masked+points_masked)=0;
% which_model(ii+(kk-1)*points_masked+1:ii+(kk-1)*points_masked+points_masked)=kk;
%
%I then changed it to this, spaced by delta_ii
delta_ii=floor(no_points_post/points_masked);
for ii_pm=1:points_masked
training_mask(ii+kk+(ii_pm-1)*delta_ii)=0;
which_model(ii+kk+(ii_pm-1)*delta_ii)=kk;
end
ii=ii+no_points_post;
else
at_end=1;
end
end
these_training_measurements=zeros(sum(training_mask),size(measurements_post,2));
these_training_decisions=zeros(1,sum(training_mask));
jj=0;
for ii=1:size(measurements_post,1)
if training_mask(ii)==1
jj=jj+1;
these_training_measurements(jj,:)=measurements_post(ii,:);
these_training_decisions(jj)=training_decisions_post(ii);
end
end
tblTrn=[];
tblTrn = array2table(these_training_measurements);
%Store the decisions in Y
Y=these_training_decisions;
switch MLalgo
case 1
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitcdiscr(tblTrn,Y,'Cost',this_cost);
case 2
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitcsvm(tblTrn,Y,'Cost',this_cost);
case 3
%The try catch was entered here because of this
%error
% Error using ClassificationNaiveBayes/findNoDataCombos (line 347)
% A normal distribution cannot be fit for the combination of class 1 and predictor
% these_training_measurements107. The data has zero variance.
try
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitcnb(tblTrn,Y,'Cost',this_cost);
catch
handles_not_out.MLalgo(MLalgo).processed_succesfully=0;
end
case 4
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitcnet(tblTrn,Y);
case 5
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitctree(tblTrn,Y,'Cost',this_cost);
case 6
handles_not_out.MLalgo(MLalgo).models(kk).Mdl = fitglm(these_training_measurements,Y','Distribution','binomial');
end
end
if handles_not_out.MLalgo(MLalgo).processed_succesfully==1
%Predict labels for the test set. You trained Mdl using a table of data, but you can predict labels using a matrix.
%Note: for some reason tisdid not work for net when I did this:
% [label_traces,score] = predict(Mdl,traces');
% [label_post,score] = predict(Mdl,measurements_post);
%I had to resolrt to the for loop:
which_model_for_traces=randi(k_fold,1,size(traces,2));
for ii=1:length(which_model)
which_model_for_traces(ii_pointer_to_td(ii))=which_model(ii);
end
if MLalgo==6
for ii=1:size(traces,2)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=traces(:,ii);
[label,score] = predict(handles_not_out.MLalgo(MLalgo).models(which_model_for_traces(ii)).Mdl,this_time_point);
if label>0.5
label_traces(ii)=1;
else
label_traces(ii)=0;
end
end
for ii=1:size(measurements_post,1)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=measurements_post(ii,:);
[label,score] = predict(handles_not_out.MLalgo(MLalgo).models(which_model(ii)).Mdl,this_time_point);
if label>0.5
label_post(ii)=1;
else
label_post(ii)=0;
end
end
else
for ii=1:size(traces,2)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=traces(:,ii);
[label_traces(ii),score] = predict(handles_not_out.MLalgo(MLalgo).models(which_model_for_traces(ii)).Mdl,this_time_point);
end
for ii=1:size(measurements_post,1)
this_time_point=zeros(1,size(traces,1));
this_time_point(1,:)=measurements_post(ii,:);
[label_post(ii),score] = predict(handles_not_out.MLalgo(MLalgo).models(which_model(ii)).Mdl,this_time_point);
end
end
handles_not_out.MLalgo(MLalgo).label_post=label_post;
handles_not_out.MLalgo(MLalgo).label_traces=label_traces;
%Now do prediction with shuffled training decisions
%k-fold cross validation evaluating performance with left-out data
%Store the training data in a table.
handles_not_out.MLalgo(MLalgo).sh_models=[];
points_masked=floor(no_points_post/k_fold);
which_model=ones(1,size(measurements_post,1));
for kk=1:k_fold
training_mask=ones(size(measurements_post,1),1);
at_end=0;
ii=0;
while at_end==0
if ii+no_points_post<=size(measurements_post,1)
%This is what I originally used, these samples are adjacent
% training_mask(ii+(kk-1)*points_masked+1:ii+(kk-1)*points_masked+points_masked)=0;
% which_model(ii+(kk-1)*points_masked+1:ii+(kk-1)*points_masked+points_masked)=kk;
%
%I then changed it to this, spaced by delta_ii
delta_ii=floor(no_points_post/points_masked);
for ii_pm=1:points_masked
training_mask(ii+kk+(ii_pm-1)*delta_ii)=0;
which_model(ii+kk+(ii_pm-1)*delta_ii)=kk;
end
ii=ii+no_points_post;
else
at_end=1;
end
end
these_training_measurements=zeros(sum(training_mask),size(measurements_post,2));
these_training_decisions=zeros(1,sum(training_mask));
jj=0;
for ii=1:size(measurements_post,1)