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d190708_testing_glm_results.m
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baseDir = 'D:\TPM\JK\suite2p\';
mice = [25,27,30,36,37,38,39,41,52,53,54,56];
sessions = {[4,19],[3,10],[3,21],[1,17],[7],[2],[1,22],[3],[3,21],[3],[3],[3]};
% sessions = {[19],[3,10],[3,21],[1,17],[7],[2],[22],[3],[3,21],[3],[3],[3]};
for mi = 1
for si = 1
mouse = mice(mi);
session = sessions{mi}(si);
posShiftTouch = 2;
posShiftSound = 3;
posShiftReward = 3;
posShiftWhisking = 4;
posShiftLicking = 1;
posShift = 4; % maximum posShift
negShift = 2;
testPortion = 0.3; % 30 % test set
dn = sprintf('%s%03d',baseDir,mouse);
ufn = sprintf('UberJK%03dS%02d.mat', mouse, session);
cd(dn)
% if exist(ufn,'file')
load(ufn)
% else
% u = Uber.buildUberArray(mouse, session);
% end
frameRate = u.frameRate;
angles = unique(cellfun(@(x) x.angle, u.trials));
distances = unique(cellfun(@(x) x.distance, u.trials));
touchGroup = cell(2,1);
choiceGroup = cell(3,1);
angleGroup = cell(length(angles),1);
distanceGroup = cell(length(distances),1);
timeGroup = cell(3,1); % dividing whole session into 5 different time points
ptouchGroup{1} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) length(x.protractionTouchChunks), u.trials))));
ptouchGroup{2} = setdiff(u.trialNums, ptouchGroup{1});
rtouchGroup{1} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) length(x.retractionTouchChunks), u.trials))));
rtouchGroup{2} = setdiff(u.trialNums, rtouchGroup{1});
choiceGroup{1} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) x.response == 1, u.trials))));
choiceGroup{2} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) x.response == 0, u.trials))));
choiceGroup{3} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) x.response == -1, u.trials))));
for i = 1 : length(angles)
angleGroup{i} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) x.angle == angles(i), u.trials))));
end
for i = 1 : length(distances)
distanceGroup{i} = cellfun(@(x) x.trialNum, u.trials(find(cellfun(@(x) x.distance == distances(i), u.trials))));
end
%% Design matrices
% standardized using all the trials
allPredictors = cell(8,1);
allPredictorsMean = cell(8,1);
allPredictorsStd = cell(8,1);
nani = cell(8,1);
% trainingPredictorInd = cell(8,1);
% testPredictorInd = cell(8,1);
% trainingInputMat = cell(8,1);
% testInputMat = cell(8,1);
for cgi = 1:2 % cell group index
tindcell = find(cellfun(@(x) ismember(1001+(cgi-1)*4000, x.neuindSession), u.trials));
tind = tindcell;
for plane = 1 : 4
% trainingPredictorInd{(cgi-1)*4 + plane} = cell2mat(cellfun(@(x) (ones(1,length(x.tpmTime{plane})+posShift*2)) * ismember(x.trialNum, trainingTn), u.trials(tind)','uniformoutput',false));
% testPredictorInd{(cgi-1)*4 + plane} = cell2mat(cellfun(@(x) (ones(1,length(x.tpmTime{plane})+posShift*2)) * ismember(x.trialNum, testTn), u.trials(tind)','uniformoutput',false));
pTouchCount = cell2mat(cellfun(@(x) [nan(1,posShift), histcounts(cellfun(@(y) x.whiskerTime(y(1)), x.protractionTouchChunks), [0, x.tpmTime{plane}]), nan(1,posShift)], u.trials(tind)','uniformoutput',false));
pTouchFrame = pTouchCount;
pTouchFrame(pTouchFrame > 0) = 1;
pTouchFrameAngles = cell(length(angles)+1,1);
for ai = 1 : length(angles)
tempAngleBinary = cell2mat(cellfun(@(x) ones(length(x.tpmTime{plane}) + 2 * posShift, 1) * (x.angle == angles(ai)), u.trials(tind), 'uniformoutput', false));
pTouchFrameAngles{ai} = pTouchFrame .* tempAngleBinary';
end
pTouchFrameAngles{end} = pTouchFrame;
scPoleup = cell2mat(cellfun(@(x) [nan(1,posShift), histcounts(x.poleUpOnsetTime, [0, x.tpmTime{plane}]), nan(1,posShift)], u.trials(tind)','uniformoutput',false));
drink = cell2mat(cellfun(@(x) [nan(1,posShift), histcounts(x.drinkingOnsetTime:0.03:x.drinkingOnsetTime+1, [0, x.tpmTime{plane}]), nan(1,posShift)], u.trials(tind)','uniformoutput',false));
drink(drink>0) = 1;
drinkAngles = cell(length(angles)+1,1);
for ai = 1 : length(angles)
tempAngleBinary = cell2mat(cellfun(@(x) ones(length(x.tpmTime{plane}) + 2 * posShift, 1) * (x.angle == angles(ai)), u.trials(tind), 'uniformoutput', false));
drinkAngles{ai} = drink .* tempAngleBinary';
end
drinkAngles{end} = drink;
lLick = cell2mat(cellfun(@(x) [nan(1,posShift), histcounts(x.leftLickTime, [0, x.tpmTime{plane}]), nan(1,posShift)], u.trials(tind)','uniformoutput',false));
rLick = cell2mat(cellfun(@(x) [nan(1,posShift), histcounts(x.rightLickTime, [0, x.tpmTime{plane}]), nan(1,posShift)], u.trials(tind)','uniformoutput',false));
%%
whiskingOnsetCell = cell(1,length(tind));
whiskingAmplitudeCell = cell(1,length(tind));
whiskingMidpointCell = cell(1,length(tind));
for ti = 1 : length(tind)
currTrial = u.trials{tind(ti)};
time = [0, currTrial.tpmTime{plane}];
wtimes = currTrial.whiskerTime;
if iscell(wtimes)
wtimes = wtimes{1};
end
[onsetFrame, amplitude, midpoint] = jkWhiskerOnsetNAmplitude(currTrial.theta);
whiskerVideoFrameDuration = u.trials{tind(1)}.frameDuration; % in s
onsetTimes = onsetFrame*whiskerVideoFrameDuration; % back to s
tempOnset = histcounts(onsetTimes, time);
whiskingOnsetCell{ti} = [nan(1,posShift), tempOnset, nan(1,posShift)];
tempMid = zeros(1,length(time)-1);
tempAmp = zeros(1,length(time)-1);
for i = 1 : length(tempMid)
startInd = find(wtimes >= time(i), 1, 'first');
endInd = find(wtimes < time(i+1), 1, 'last');
tempMid(i) = mean(midpoint(startInd:endInd));
tempAmp(i) = max(amplitude(startInd:endInd));
end
tempMid(isnan(tempMid)) = deal(mode(tempMid(isfinite(tempMid))));
tempAmp(isnan(tempAmp)) = deal(mode(tempAmp(isfinite(tempAmp))));
whiskingMidpointCell{ti} = [nan(1,posShift), tempMid, nan(1,posShift)];
whiskingAmplitudeCell{ti} = [nan(1,posShift), tempAmp, nan(1,posShift)];
end
whiskingOnset = cell2mat(whiskingOnsetCell);
whiskingMidpoint = cell2mat(whiskingMidpointCell);
whiskingAmplitude = cell2mat(whiskingAmplitudeCell);
%%
pTouchFrameMat = zeros(length(pTouchFrame), (posShiftTouch + 1) * (length(angles)+1));
scPoleUpMat = zeros(length(scPoleup), posShiftSound + 1);
drinkAnglesMat = zeros(length(drink), (posShiftReward + 1) * (length(angles)+1));
for i = 1 : posShiftTouch + 1
for ai = 1 : length(angles) + 1
pTouchFrameMat(:,(i-1)*(length(angles)+1) + ai) = circshift(pTouchFrameAngles{ai}, [0 i-1])';
end
end
for i = 1 : posShiftSound + 1
scPoleUpMat(:,i) = circshift(scPoleup, [0 i-1])';
end
for i = 1 : posShiftReward + 1
for ai = 1 : length(angles) + 1
drinkAnglesMat(:,(i-1)*(length(angles)+1) + ai) = circshift(drinkAngles{ai}, [0 i-1])';
end
end
whiskingOnsetMat = zeros(length(whiskingOnset), negShift + posShiftWhisking + 1);
whiskingAmplitudeMat = zeros(length(whiskingAmplitude), negShift + posShiftWhisking + 1);
whiskingMidpointMat = zeros(length(whiskingMidpoint), negShift + posShiftWhisking + 1);
lLickMat = zeros(length(lLick), negShift + posShiftLicking + 1);
rLickMat = zeros(length(rLick), negShift + posShiftLicking + 1);
for i = 1 : negShift + posShiftWhisking + 1
whiskingOnsetMat(:,i) = circshift(whiskingOnset, [0 -negShift + i - 1])';
whiskingMidpointMat(:,i) = circshift(whiskingMidpoint, [0 -negShift + i - 1])';
whiskingAmplitudeMat(:,i) = circshift(whiskingAmplitude, [0 -negShift + i - 1])';
end
for i = 1 : negShift + posShiftLicking + 1
lLickMat(:,i) = circshift(lLick, [0 -negShift + i - 1])';
rLickMat(:,i) = circshift(rLick, [0 -negShift + i - 1])';
end
touchMat = [pTouchFrameMat];
soundMat = [scPoleUpMat];
drinkMat = drinkAnglesMat;
whiskingMat = [whiskingOnsetMat, whiskingAmplitudeMat, whiskingMidpointMat];
lickingMat = [lLickMat, rLickMat];
allPredictors{(cgi-1)*4 + plane} = [touchMat, soundMat, drinkMat, whiskingMat, lickingMat];
nani{(cgi-1)*4 + plane} = find(nanstd(allPredictors{(cgi-1)*4 + plane})==0);
allPredictorsMean{(cgi-1)*4 + plane} = nanmean(allPredictors{(cgi-1)*4 + plane});
allPredictorsStd{(cgi-1)*4 + plane} = nanstd(allPredictors{(cgi-1)*4 + plane});
% normalization of all predictors
allPredictors{(cgi-1)*4 + plane} = (allPredictors{(cgi-1)*4 + plane} - nanmean(allPredictors{(cgi-1)*4 + plane})) ./ nanstd(allPredictors{(cgi-1)*4 + plane});
allPredictors{(cgi-1)*4 + plane}(:,nani{(cgi-1)*4 + plane}) = deal(0);
% trainingInputMat{(cgi-1)*4 + plane} = allPredictors{(cgi-1)*4 + plane}(find(trainingPredictorInd{(cgi-1)*4 + plane}),:);
% testInputMat{(cgi-1)*4 + plane} = allPredictors{(cgi-1)*4 + plane}(find(testPredictorInd{(cgi-1)*4 + plane}),:);
end
end
end
end
%%
apTest = allPredictors;
load('glmResponseType_JK025S04_m44_R10','allPredictors')
%%
compare(apTest{1}(find(~isnan(apTest{1}))), allPredictors{1}(find(~isnan(allPredictors{1}))))
%%
col = 82;
compare(apTest{1}(:,col), allPredictors{1}(:,col))
%%
load('glmResponseType_JK025S04_m44_R04','allPredictors')
ap1 = allPredictors;
load('glmResponseType_JK025S04_m44_R03','allPredictors')
ap2 = allPredictors;
compare(ap1{1}, ap2{1})
%% The difference between allPredictors came from whisking parameters.
% BUT, if the spikes are the same, then the result should be the same.
%% Test GLM results with new uber arrays.
baseDir = 'D:\TPM\JK\suite2p\';
mice = [25,27,30,36,37,38,39,41,52,53,54,56];
sessions = {[4,19],[3,10],[3,21],[1,17],[7],[2],[1,22],[3],[3,21],[3],[3],[3]};
mi = 2;
si = 1;
mouse = mice(mi);
session = sessions{mi}(si);
ufn = sprintf('UberJK%03dS%02d.mat', mouse, session);
load(ufn)
% cIDAll = u.cellNums;
%%
numCell = length(cIDAll);
tindcellAll = cell(numCell,1);
cindAll = zeros(numCell,1);
planeIndAll = zeros(numCell,1);
for i = 1 : numCell
tindcellAll{i} = find(cellfun(@(x) ismember(cIDAll(i), x.neuindSession), u.trials));
cindAll(i) = find(u.trials{tindcellAll{i}(1)}.neuindSession == cIDAll(i));
planeIndAll(i) = floor(cIDAll(i)/1000);
end
spikeAll = cellfun(@(x) x.spk, u.trials, 'uniformoutput', false);
%%
cellnum = 1;
cind = cindAll(cellnum);
tindCell = tindcellAll{cellnum};
testInd = find(ismember(u.trialNums,testTn{cellnum}));
iTest = intersect(tindCell, testInd);
planeInd = planeIndAll(cellnum);
plane = mod(planeInd,4);
if plane==0
plane = 4;
end
testPredictorInd = cell2mat(cellfun(@(x) (ones(1,length(x.tpmTime{plane})+posShift*2)) * ismember(x.trialNum, testTn{cellnum}), u.trials(tindCell)','uniformoutput',false));
testInput = allPredictors{planeInd}(find(testPredictorInd),:);
spkTemp = cell2mat(cellfun(@(x) [nan(1,posShift), x(cind,:), nan(1,posShift)], spikeAll(iTest)','uniformoutput',false));
spkTemp = spkTemp';
finiteIndTest = intersect(find(isfinite(spkTemp)), find(isfinite(sum(testInput,2))));
spkTest = spkTemp(finiteIndTest)';
% spkTemp2 =
model = exp([ones(length(finiteIndTest),1),testInput(finiteIndTest,:)]*fitCoeffs{cellnum});
mu = mean(spkTest); % null poisson parameter
nullLogLikelihood = sum(log(poisspdf(spkTest,mu)));
fullLogLikelihood = sum(log(poisspdf(spkTest',model)));
saturatedLogLikelihood = sum(log(poisspdf(spkTest,spkTest)));
devianceFullNull = 2*(fullLogLikelihood - nullLogLikelihood);
% fitDeviance(cellnum) - devianceFullNull
fitDevExplained(cellnum) - 1 + (saturatedLogLikelihood - fullLogLikelihood)/(saturatedLogLikelihood - nullLogLikelihood)