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angle_tuning_predecision.m
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% Only within cells responding to touch (from glmFunctionRidgeDE010.mat)
% 1) z-score of calcium response. pole up frames / before pole up frames (calcium)
% 2) # of spikes during touch frames + [0:2] / before pole up (spkPole)
% Only from trials with touch
% run ANOVA
% permutation tests from tuned cells
% final decision of tuned cells
%
% From tuned cells calculate:
% 1) tuned angle (max abs)
% 2) tuning direction (excited, inhibited, bipolar)
% 3) unimodal-single, unimodal-broad, multimodal, leave-one-out, categorical, ramp
% 4) modultaion: max-min. sharpness: response of the max - mean response of the rest
% From not-tuned cells calculate:
% 1) response direction (excited, inhibited)
% 2) response amplitude
% 2019/04/08 JK
% Updates:
% 2019/06/25 - Only pre-decision periods. addition of frames were just 1,
% instead of 2. Just compensating for a possible spike detection frame
% error (at most single is assumed). Consider touch chunks "by whisking".
% settings
clear
% baseDir = 'Y:\Whiskernas\JK\suite2p\';
baseDir = 'D:\TPM\JK\suite2p\';
mice = [25,27,30,36,37,38,39,41,52,53,54,56];
% sessions = {[4,19],[3,16],[3,21],[1,17],[7],[2],[1,23],[3],[3,21],[3],[3],[3],[6],[4],[4],[4]};
sessions = {[4,19],[3,10],[3,21],[1,17],[7],[2],[1,23],[3],[3,21],[3],[3],[3]};
naiveMi = 1:12;
expertMi = [1,2,3,4,7,9];
angles = 45:15:135;
thresholdAnovaP = 0.05;
thresholdTtestNeighbors = 0.05;
thresholdTtestResponse = 0.05;
thresholdCategory = 0.05;
anovactype = 'hsd';
numResampling = 10000; % permutation test
% Load ridge results file
% It should be at the base directory
cd(baseDir)
load('cellFunctionLasso_NC.mat')
for mi = 1 : length(mice)
% for mi = 2
mouse = mice(mi);
cd(sprintf('%s%03d',baseDir,mouse))
for si = 1 : length(sessions{mi})
% for si = 2
session = sessions{mi}(si);
% load uber
ufn = sprintf('UberJK%03dS%02d_NC',mouse, session);
load(ufn)
% still some settings
savefn = [u.mouseName,u.sessionName,'angle_tuning_lasso_predecision_NC_permTestCorrected.mat']; %
% making templates
% find predecision touch trials
decisionTime = cell(length(u.trials),1);
for di = 1 : length(decisionTime)
if isempty(u.trials{di}.answerLickTime)
decisionTime{di} = u.trials{di}.poleDownOnsetTime;
else
decisionTime{di} = u.trials{di}.answerLickTime;
end
end
tempTouchTrialInd = find(cellfun(@(x) ~isempty(x.protractionTouchChunks), u.trials));
pdTouchInd = find(cellfun(@(x,y) x.whiskerTime(x.protractionTouchChunks{1}(1)) < y, u.trials(tempTouchTrialInd), decisionTime(tempTouchTrialInd)));
touchTrialInd = tempTouchTrialInd(pdTouchInd);
numPlane = length(u.mimg);
planeTrialsInd = cell(numPlane,1);
planeTrialsNum = cell(numPlane,1);
poleUpFrames = cell(numPlane,1);
beforePoleUpFrames = cell(numPlane,1);
touchFrames = cell(numPlane,1);
nonTouchFrames = cell(numPlane,1); % within pole up frames
angleTrialInds = cell(numPlane,1);
for pi = 1 : numPlane
planeTrialsInd{pi} = intersect(find(cellfun(@(x) ismember(pi, x.planes), u.trials)), touchTrialInd);
tempInd = find(u.trials{planeTrialsInd{pi}(1)}.planes == pi);
planeTrialsNum{pi} = cellfun(@(x) x.trialNum, u.trials(planeTrialsInd{pi}));
poleUpFrames{pi} = cellfun(@(x) find(x.tpmTime{tempInd} >= x.poleUpTime(1) & x.tpmTime{tempInd} <= x.poleUpTime(end)), u.trials(planeTrialsInd{pi}), 'uniformoutput', false);
beforePoleUpFrames{pi} = cellfun(@(x) find(x.tpmTime{tempInd} < x.poleUpOnsetTime), u.trials(planeTrialsInd{pi}), 'uniformoutput', false);
touchFrames{pi} = cell(length(planeTrialsInd{pi}),1);
nonTouchFrames{pi} = cell(length(planeTrialsInd{pi}),1);
for ti = 1 : length(planeTrialsInd{pi})
tempTrial = u.trials{planeTrialsInd{pi}(ti)};
if isempty(tempTrial.answerLickTime)
tempDecisionTime = tempTrial.poleDownOnsetTime;
else
tempDecisionTime = tempTrial.answerLickTime;
end
preDecisionInd = find(cellfun(@(x) tempTrial.whiskerTime(x(1)) < tempDecisionTime, tempTrial.protractionTouchChunks));
tempFrames = cell(1, length(preDecisionInd));
for ptci = 1 : length(tempFrames)
tempFrames{ptci} = [0:1] + find(tempTrial.tpmTime{tempInd} >= tempTrial.whiskerTime(tempTrial.protractionTouchChunks{ptci}(1)), 1, 'first');
end
touchFrames{pi}{ti} = unique(cell2mat(tempFrames));
% tempTouchFrames = unique(cell2mat(tempFrames));
% touchFrames{pi}{ti} = tempTouchFrames(tempTouchFrames <= length(tempTrial.tpmTime{tempInd})); % sometimes touch can happen after tpm imaging is done with that trial
nonTouchFrames{pi}{ti} = setdiff(poleUpFrames{pi}{ti}, touchFrames{pi}{ti});
end
angleTrialInds{pi} = cell(length(angles),1); % index of planeTrialsInd{pi}
for ai = 1 : length(angles)
angleTrialInds{pi}{ai} = find(cellfun(@(x) x.angle == angles(ai), u.trials(planeTrialsInd{pi})));
end
end
% find glm results
if si == 2
glmi = find(expertMi == mi);
glm = expert(glmi);
else
glm = naive(mi);
end
touchID = glm.touchID;
if size(touchID,1) < size(touchID,2)
touchID = touchID';
end
ca.touchID = touchID;
catuned = zeros(length(touchID),1);
caAnovaPAll = zeros(length(touchID),1);
catunedAngle = zeros(length(touchID),1);
catuneDirection = zeros(length(touchID),1);
caunimodalSingle = zeros(length(touchID),1);
caunimodalBroad = zeros(length(touchID),1);
camultimodal = zeros(length(touchID),1);
caleaveOneOut = zeros(length(touchID),1);
cacategorical = zeros(length(touchID),1);
caramp = zeros(length(touchID),1);
camodulation = zeros(length(touchID),1);
casharpness = zeros(length(touchID),1);
caNTamplitude = zeros(length(touchID),1); % for not-tuned cells
caNTdirection = zeros(length(touchID),1); % for not-tuned cells
caValAll = cell(length(touchID),1);
spk.touchID = touchID;
spktuned = zeros(length(touchID),1);
spkAnovaPAll = zeros(length(touchID),1);
spktunedAngle = zeros(length(touchID),1);
spktuneDirection = zeros(length(touchID),1);
spkunimodalSingle = zeros(length(touchID),1);
spkunimodalBroad = zeros(length(touchID),1);
spkmultimodal = zeros(length(touchID),1);
spkleaveOneOut = zeros(length(touchID),1);
spkcategorical = zeros(length(touchID),1);
spkramp = zeros(length(touchID),1);
spkmodulation = zeros(length(touchID),1);
spksharpness = zeros(length(touchID),1);
spkNTamplitude = zeros(length(touchID),1); % for not-tuned cells
spkNTdirection = zeros(length(touchID),1); % for not-tuned cells
spkValAll = cell(length(touchID),1);
parfor ci = 1:length(touchID)
% for ci = 195
fprintf('Processing JK%03d S%02d touch cell %d / %d\n', mouse, session, ci, length(touchID))
cellNum = touchID(ci);
% if mouse == 27 && (session == 9 || session == 10)
% plane = floor(cellNum/1000) - 4;
% else
plane = floor(cellNum/1000);
% end
trialInds = planeTrialsInd{plane};
calciumPoleUpFrames = poleUpFrames{plane};
spkTouchFrames = touchFrames{plane};
baselineFrames = beforePoleUpFrames{plane};
angleInds = angleTrialInds{plane}; % index of trialInds
% z-score calcium signal in each cell
cind = find(u.trials{trialInds(1)}.neuindSession == cellNum);
tempF = cellfun(@(x) x.dF(cind,:), u.trials(trialInds), 'uniformoutput', false);
meanF = mean(cell2mat(tempF'));
stdF = std(cell2mat(tempF'));
caZscore = cellfun(@(x) (x-meanF)/stdF, tempF, 'uniformoutput', false);
% all spikes
tempSpk = cellfun(@(x) x.spk(cind,:), u.trials(trialInds), 'uniformoutput', false);
caValAll{ci} = cell(length(angles),1);
spkValAll{ci} = cell(length(angles),1);
for ai = 1 : length(angles)
trialAngleInd = angleInds{ai};
caValAll{ci}{ai} = zeros(length(trialAngleInd),1);
spkValAll{ci}{ai} = zeros(length(trialAngleInd),1);
for ti = 1 : length(trialAngleInd)
tempInd = trialAngleInd(ti);
caValAll{ci}{ai}(ti) = mean(tempF{tempInd}(calciumPoleUpFrames{tempInd})) - mean(tempF{tempInd}(baselineFrames{tempInd}));
spkValAll{ci}{ai}(ti) = mean(tempSpk{tempInd}(spkTouchFrames{tempInd})) - mean(tempSpk{tempInd}(baselineFrames{tempInd}));
end
end
caVal = caValAll{ci};
spkVal = spkValAll{ci};
%% ANOVA
caAnovaVal = cell2mat(caVal);
spkAnovaVal = cell2mat(spkVal);
if length(caAnovaVal) ~= length(spkAnovaVal)
error('calcium and spike has different lengths')
end
if ~isempty(find(isnan(caAnovaVal))) || ~isempty(find(isnan(spkAnovaVal)))
error('nan values')
end
groupAnova = zeros(size(caAnovaVal));
angleLengths = [0;cumsum(cellfun(@length, caVal))];
for ai = 1 : length(angles)
groupAnova(angleLengths(ai)+1:angleLengths(ai+1)) = deal(ai);
end
[caAnovaP, ~, caAnovaStat] = anova1(caAnovaVal, groupAnova, 'off');
caAnovaPAll(ci) = caAnovaP;
[spkAnovaP, ~, spkAnovaStat] = anova1(spkAnovaVal, groupAnova, 'off');
spkAnovaPAll(ci) = spkAnovaP;
caPairComp = multcompare(caAnovaStat, 'Ctype', anovactype, 'Display', 'off');
spkPairComp = multcompare(spkAnovaStat, 'Ctype', anovactype, 'Display', 'off');
caMeans = caAnovaStat.means;
spkMeans = spkAnovaStat.means;
%% Start with calcium first
tempH = cellfun(@(x) ttest(x), caVal);
tempH(isnan(tempH)) = deal(0);
sigInd = find(tempH); % significant indices
if caAnovaP < thresholdAnovaP && ~isempty(sigInd)
% permutation test
maxmod = max(caMeans) - min(caMeans);
permAnovaP = zeros(numResampling,1);
permmaxmod = zeros(numResampling,1);
% parfor ri = 1 : numResampling
for ri = 1 : numResampling
tempG = groupAnova(randperm(length(groupAnova),length(groupAnova)));
[permAnovaP(ri), ~, permStats] = anova1(caAnovaVal, tempG, 'off');
permmaxmod(ri) = max(permStats.means) - min(permStats.means);
end
if length(intersect(find(permAnovaP < thresholdAnovaP), find(permmaxmod >= maxmod))) >= 0.05 * numResampling % failed to pass permutation test
% NT: not tuned
if mean(caAnovaVal) > 0
caNTdirection(ci) = 1;
else
caNTdirection(ci) = 2;
end
caNTamplitude(ci) = mean(caAnovaVal);
else % passed permutation test. Tuned.
catuned(ci) = 1;
[~, maxind] = max(abs(caMeans(sigInd)));
tunedAngleInd = sigInd(maxind);
catunedAngle(ci) = angles(tunedAngleInd);
maxVal = max(caMeans(sigInd));
minVal = min(caMeans(sigInd));
if minVal > 0
catuneDirection(ci) = 1;
elseif maxVal < 0
catuneDirection(ci) = 2;
elseif maxVal > 0 && minVal < 0
catuneDirection(ci) = 3;
else
catuneDirection(ci) = -1; % error
end
camodulation(ci) = max(caMeans) - min(caMeans);
casharpness(ci) = caMeans(tunedAngleInd) - mean(caMeans(setdiff(1:length(angles), tunedAngleInd)));
% Categorization
ind__1 = find(caPairComp(:,1) == tunedAngleInd);
ind__2 = find(caPairComp(:,2) == tunedAngleInd);
testInd = union(ind__1, ind__2);
insigDiffInd = find(caPairComp(testInd,6) >= thresholdCategory);
sigDiffInd = find(caPairComp(testInd,6) < thresholdCategory);
temp = caPairComp(testInd(insigDiffInd),1:2);
insigDiffIndGroup = unique(temp(:)); % sorted. Include tunedAngleInd, except when there's nothing
if isempty(insigDiffIndGroup)
caunimodalSingle(ci) = 1;
else
broadInd = intersect(sigInd,insigDiffIndGroup);
if length(broadInd) < 2
caunimodalSingle(ci) = 1;
else
broadNum = 1;
for tunei = tunedAngleInd-1:-1:1
if ismember(tunei, broadInd)
broadNum = broadNum + 1;
else
break
end
end
for tunei = tunedAngleInd+1:length(angles)
if ismember(tunei, broadInd)
broadNum = broadNum + 1;
end
end
if broadNum == length(broadInd)
caunimodalBroad(ci) = 1;
% if broad, then it can be a categorical
center = (length(angles)+1) / 2;
compInd = union(find(caPairComp(:,1) == tunedAngleInd), find(caPairComp(:,2) == tunedAngleInd));
indMat = caPairComp(compInd,1:2);
if tunedAngleInd < center
withinInd = unique(mod( setdiff( find(indMat < center), find(indMat == tunedAngleInd) ) , size(indMat,1)));
withinInd(withinInd==0) = size(indMat,1);
betweenInd = unique(mod( find(indMat > center) , size(indMat,1) ));
betweenInd(betweenInd==0) = size(indMat,1);
else
withinInd = unique(mod( setdiff( find(indMat > center), find(indMat == tunedAngleInd) ) , size(indMat,1)));
withinInd(withinInd==0) = size(indMat,1);
betweenInd = unique(mod( find(indMat < center) , size(indMat,1) ));
betweenInd(betweenInd==0) = size(indMat,1);
end
if isempty(find(caPairComp(compInd(withinInd),6) < thresholdCategory, 1)) && ... % nothing within the same half is different from the max ind
isempty(find(caPairComp(compInd(betweenInd),6) >= thresholdCategory, 1)) % nothing between different half is same with the max ind
cacategorical(ci) = 1; % categorical (>= 90 or <= 90)
end
else
camultimodal(ci) = 1;
end
end
temp = caPairComp(testInd(sigDiffInd),1:2);
sigIndGroup = setdiff(temp(:), tunedAngleInd); % exclude tunedAngleInd. Any index that is significantly different from the tuned angle index.
if ~isempty(find(diff(insigDiffIndGroup)>1,1))
if sum(tempH(sigIndGroup))
camultimodal(ci) = 1; % multimodal. Including bipolar.
end
if length(sigIndGroup) == 1 && ... % only one bin is significantly different from the tuned bin. (can't be larger in response because of the way tuned bin is defined)
all(tempH(insigDiffIndGroup)) % and all insignicant indices are different from 0
caleaveOneOut(ci) = 1 ; % leave-one-out. Part of multimodal in definition.
end
end
if isempty(find(diff(sign(diff(caMeans))),1)) % everything is going up or down
caramp(ci) = 1; % ramping up or down
end
end
end
else % NT: not tuned
if mean(caAnovaVal) > 0
caNTdirection(ci) = 1;
else
caNTdirection(ci) = 2;
end
caNTamplitude(ci) = mean(caAnovaVal);
end
%% Then with spikes
tempH = cellfun(@(x) ttest(x), spkVal);
tempH(isnan(tempH)) = deal(0);
sigInd = find(tempH); % significant indices
if spkAnovaP < thresholdAnovaP && ~isempty(sigInd)
% permutation test
maxmod = max(spkMeans) - min(spkMeans);
permAnovaP = zeros(numResampling,1);
permmaxmod = zeros(numResampling,1);
% parfor ri = 1 : numResampling
for ri = 1 : numResampling
tempG = groupAnova(randperm(length(groupAnova),length(groupAnova)));
[permAnovaP(ri), ~, permStats] = anova1(spkAnovaVal, tempG, 'off');
permmaxmod(ri) = max(permStats.means) - min(permStats.means);
end
if length(find(permAnovaP < spkAnovaP)) >= 0.05 * numResampling % failed to pass permutation test
% NT: not tuned
if mean(spkAnovaVal) > 0
spkNTdirection(ci) = 1;
else
spkNTdirection(ci) = 2;
end
spkNTamplitude(ci) = mean(spkAnovaVal);
else % passed permutation test. Tuned.
spktuned(ci) = 1;
[~, maxind] = max(abs(spkMeans(sigInd)));
tunedAngleInd = sigInd(maxind);
spktunedAngle(ci) = angles(tunedAngleInd);
maxVal = max(spkMeans(sigInd));
minVal = min(spkMeans(sigInd));
if minVal > 0
spktuneDirection(ci) = 1;
elseif maxVal < 0
spktuneDirection(ci) = 2;
elseif maxVal > 0 && minVal < 0
spktuneDirection(ci) = 3;
else
spktuneDirection(ci) = -1; % error
end
spkmodulation(ci) = max(spkMeans) - min(spkMeans);
spksharpness(ci) = spkMeans(tunedAngleInd) - mean(spkMeans(setdiff(1:length(angles), tunedAngleInd)));
% Categorization
ind__1 = find(spkPairComp(:,1) == tunedAngleInd);
ind__2 = find(spkPairComp(:,2) == tunedAngleInd);
testInd = union(ind__1, ind__2);
insigDiffInd = find(spkPairComp(testInd,6) >= thresholdCategory);
sigDiffInd = find(spkPairComp(testInd,6) < thresholdCategory);
temp = spkPairComp(testInd(insigDiffInd),1:2);
insigDiffIndGroup = unique(temp(:)); % sorted. Include tunedAngleInd, except when there's nothing
if isempty(insigDiffIndGroup)
spkunimodalSingle(ci) = 1;
else
broadInd = intersect(sigInd,insigDiffIndGroup);
if length(broadInd) < 2
spkunimodalSingle(ci) = 1;
else
broadNum = 1;
for tunei = tunedAngleInd-1:-1:1
if ismember(tunei, broadInd)
broadNum = broadNum + 1;
else
break
end
end
for tunei = tunedAngleInd+1:length(angles)
if ismember(tunei, broadInd)
broadNum = broadNum + 1;
else
break
end
end
if broadNum == length(broadInd)
spkunimodalBroad(ci) = 1;
% if broad, then it can be a categorical
center = (length(angles)+1) / 2;
compInd = union(find(spkPairComp(:,1) == tunedAngleInd), find(spkPairComp(:,2) == tunedAngleInd));
indMat = spkPairComp(compInd,1:2);
if tunedAngleInd < center
withinInd = unique(mod( setdiff( find(indMat < center), find(indMat == tunedAngleInd) ) , size(indMat,1)));
withinInd(withinInd==0) = size(indMat,1);
betweenInd = unique(mod( find(indMat > center) , size(indMat,1) ));
betweenInd(betweenInd==0) = size(indMat,1);
else
withinInd = unique(mod( setdiff( find(indMat > center), find(indMat == tunedAngleInd) ) , size(indMat,1)));
withinInd(withinInd==0) = size(indMat,1);
betweenInd = unique(mod( find(indMat < center) , size(indMat,1) ));
betweenInd(betweenInd==0) = size(indMat,1);
end
if isempty(find(spkPairComp(compInd(withinInd),6) < thresholdCategory, 1)) && ... % nothing within the same half is different from the max ind
isempty(find(spkPairComp(compInd(betweenInd),6) >= thresholdCategory, 1)) % nothing between different half is same with the max ind
spkcategorical(ci) = 1; % categorical (>= 90 or <= 90)
end
else
spkmultimodal(ci) = 1;
end
end
temp = spkPairComp(testInd(sigDiffInd),1:2);
sigIndGroup = setdiff(temp(:), tunedAngleInd); % exclude tunedAngleInd. Any index that is significantly different from the tuned angle index.
if ~isempty(find(diff(insigDiffIndGroup)>1,1))
if sum(tempH(insigDiffIndGroup))>1 % to exclude tuned angle
spkmultimodal(ci) = 1; % multimodal. Including bipolar.
end
if length(sigIndGroup) == 1 && ... % only one bin is significantly different from the tuned bin. (can't be larger in response because of the way tuned bin is defined)
all(tempH(insigDiffIndGroup)) % and all insignicant indices are different from 0
spkleaveOneOut(ci) = 1 ; % leave-one-out. Part of multimodal in definition.
end
end
if isempty(find(diff(sign(diff(spkMeans))),1)) % everything is going up or down
spkramp(ci) = 1; % ramping up or down
end
end
end
else % NT: not tuned
if mean(spkAnovaVal) > 0
spkNTdirection(ci) = 1;
else
spkNTdirection(ci) = 2;
end
spkNTamplitude(ci) = mean(spkAnovaVal);
end
end
ca.tuned = catuned;
ca.anovaP = caAnovaPAll;
ca.tunedAngle = catunedAngle;
ca.tuneDirection = catuneDirection;
ca.unimodalSingle = caunimodalSingle;
ca.unimodalBroad = caunimodalBroad;
ca.multimodal = camultimodal;
ca.leaveOneOut = caleaveOneOut;
ca.categorical = cacategorical;
ca.ramp = caramp;
ca.modulation = camodulation;
ca.sharpness = casharpness;
ca.NTamplitude = caNTamplitude;
ca.NTdirection = caNTdirection;
ca.val = caValAll;
spk.tuned = spktuned;
spk.anovaP = spkAnovaPAll;
spk.tunedAngle = spktunedAngle;
spk.tuneDirection = spktuneDirection;
spk.unimodalSingle = spkunimodalSingle;
spk.unimodalBroad = spkunimodalBroad;
spk.multimodal = spkmultimodal;
spk.leaveOneOut = spkleaveOneOut;
spk.categorical = spkcategorical;
spk.ramp = spkramp;
spk.modulation = spkmodulation;
spk.sharpness = spksharpness;
spk.NTamplitude = spkNTamplitude;
spk.NTdirection = spkNTdirection;
spk.val = spkValAll;
info.cellID = u.cellNums;
info.celly = u.celly;
info.cellx = u.cellx;
info.c2ypoints = u.c2ypoints;
info.c2xpoints = u.c2xpoints;
info.fovsize = u.fovsize;
info.fovxrange = u.fovxrange;
info.fovyrange = u.fovyrange;
info.fovdepth = u.fovdepth;
save(savefn, 'ca','spk','info')
end
end