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metadata_variance_exp_variables.m
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metadata_variance_exp_variables.m
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% responseVars = [rmp log10(ir) log10(tau) amp hw thresh];
% dataUseInds = log10(Age) > .67 & ~strcmp('cell culture ', PrepType);
% % predictorVarsCat = [NeuronType Species Strain ElectrodeType PrepType];
% predictorVarsCat = [NeuronType Species Strain ElectrodeType PrepType JxnPotential Grandfather];
% % predictorVarsCont = [Age Temp Weight];
% predictorVarsCont = [log10(Age) Temp PubYear];
%
% predictorVars = [];
% for i = 1:size(predictorVarsCat, 2)
% predictorVars(:,end+1) = toCategorical(predictorVarsCat(dataUseInds,i));
% end
%
% for i = 1:size(predictorVarsCont, 2)
% predictorVars(:,end+1) = predictorVarsCont(dataUseInds, i);
% end
% % predictorVarsNames = {'NeuronType'; 'Species'; 'Strain'; 'ElectrodeType'; 'PrepType'; 'Age'; 'Temp'; 'Weight'};
% predictorVarsNames = {'NeuronType'; 'Species'; 'Strain'; 'ElectrodeType'; 'PrepType'; 'JxnPotential'; 'Grandfather'; 'Age'; 'Temp'; 'PubYear'};
% responseVarsNames = {'rmp'; 'ir'; 'tau'; 'amp'; 'hw'; 'thresh'};
%
% % catVarInds = [1 1 1 1 1 0 0 0];
% catVarInds = [1 1 1 1 1 1 1 0 0 0];
numResponseVars = size(responseVars, 2);
[neuronCategorical, neuronNames] = grp2idx(NeuronType(dataUseInds));
numUniqueNeurons = max(neuronCategorical);
minNeuronMentions = 1;
minIterImprovement = .01;
for i = [1 2 3 4 5 6]
ephysInd = i;
currRespVar = responseVars(dataUseInds,ephysInd);
currUsePredVarInds = 1:length(predictorVarsNames);
% only use data from neuron types which have at least 3 reports
predRespMat = [predictorVars(:,currUsePredVarInds) currRespVar];
predRespVec = sum(predRespMat, 2);
articleInds = [];
numUsedNeurons = 0;
for m = 1:numUniqueNeurons
% numUniqueArts = sum(neuronCategorical == m & ~isnan(currRespVar));
numUniqueArts = sum(neuronCategorical == m );
if numUniqueArts >= minNeuronMentions
% if randn > 0
% currArticleInds = find(neuronCategorical == m & ~isnan(currRespVar));
currArticleInds = find(neuronCategorical == m);
articleInds = [articleInds; currArticleInds];
numUsedNeurons = numUsedNeurons + 1;
% end
end
end
for j = 1:length(predictorVarsNames)
usePredictors = unique([1 j], 'stable');
varNames = predictorVarsNames(usePredictors);
varNames{end+1} = responseVarsNames{ephysInd};
mdl = LinearModel.fit(predictorVars(articleInds,usePredictors),currRespVar(articleInds),...
'linear', 'CategoricalVars', logical(catVarInds(usePredictors)), 'VarNames', varNames);
mdl.Formula;
modelStructAll{i,j}.model{1} = mdl;
end
end
rsqMat = zeros(size(modelStructAll));
pValMat = zeros(size(modelStructAll));
for i = 1:size(modelStructAll, 1)
for j = 1:size(modelStructAll, 2)
rsqMat(i,j) = modelStructAll{i,j}.model{1}.Rsquared.Adjusted;
tbl = anova(modelStructAll{i,j}.model{1}, 'component');
pValMat(i,j) = tbl.pValue(end-1);
end
end
figure; imagesc(rsqMat);
set(gca, 'XTick', [1:length(predictorVarsNames)], 'XTickLabel', predictorVarsNames);
colormap Hot
xticklabel_rotate;
colorbar;
% set(gca, 'YTick', [1:length(responseVarsNames)], 'YTickLabel', responseVarsNamesPlot);
format_ticks(gca,[], responseVarsNamesPlot)
caxis([0 max(max(rsqMat))]);
hold on;
pValueThresh1 = .05;
pValueThresh2 = .1;
for i = 1:size(modelStructAll, 1)
for j = 1:size(modelStructAll, 2)
if (pValMat(i,j) <= pValueThresh1)
plot(j,i, 'go');
elseif (pValMat(i,j) <= pValueThresh2)
plot(j,i, 'g.');
end
end
end
rsqDiffMat = rsqMat(:,2:end) - repmat(rsqMat(:,1), 1, size(rsqMat, 2)-1);
figure; imagesc(rsqDiffMat);
set(gca, 'XTick', [1:length(predictorVarsNames)-1], 'XTickLabel', predictorVarsNames(2:end));
colormap Hot
xticklabel_rotate;
h = colorbar;
ylabel(h, ' Adj. R^2 (relative to neuron-type only model) ')
set(gca, 'YTick', [1:length(responseVarsNames)], 'YTickLabel', responseVarsNames);
format_ticks(gca,[], responseVarsNamesPlot)
caxis([0 max(max(rsqDiffMat))]);
hold on;
for i = 1:size(modelStructAll, 1)
for j = 2:size(modelStructAll, 2)
if (pValMat(i,j) <= pValueThresh1)
plot(j-1,i, 'go');
% elseif (pValMat(i,j) <= pValueThresh2)
% plot(j-1,i, 'g.');
end
end
end
[neuronCategorical, neuronNames] = grp2idx(NeuronType(dataUseInds));
numUniqueNeurons = max(neuronCategorical);
neuronUniqueArtMat = zeros(numUniqueNeurons, numResponseVars);
for i = 1:numResponseVars
currRespVar = responseVars(dataUseInds,i);
for j = 1:numUniqueNeurons
numUniqueArts = sum(neuronCategorical == j & ~isnan(currRespVar));
neuronUniqueArtMat(j,i) = numUniqueArts;
end
end
%% plot specific examples of metadata predictors and ephys props for supp figure
% plot some data that'll go into the main figure
responseVarPlotInds = [2 3 6];
predictorVarPlotInds = [4 7 6];
figure;
for j = 1:length(responseVarPlotInds)
subplot(1, 3,j);
responseVarInd = responseVarPlotInds(j);
predictorVarInd = predictorVarPlotInds(j);
tempPredictorVars = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,1:2));
currPredictorVars = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,1:2));
if catVarInds(predictorVarInd) % check if variable is categorical
tempPredictorVars(:,2) = mode(tempPredictorVars(:,2));
else
tempPredictorVars(:,2) = nanmean(tempPredictorVars(:,2));
end
predVals = predict(modelStructAll{responseVarInd,predictorVarInd}.model{1}, tempPredictorVars);
yvals = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,3)) - predVals + nanmean(responseVars(dataUseInds,responseVarInd));
if responseVarInd == 2 || responseVarInd == 5 || responseVarInd == 3
yvals = 10.^yvals;
end
if catVarInds(predictorVarInd) % check if variable is categorical
metadataVals = predictorVarsCat(dataUseInds, predictorVarInd);
numMetadataVals = length(unique(metadataVals));
metadataXVals = unique(metadataVals, 'stable');
uniquePredictorVars = unique(currPredictorVars(:, 2), 'stable');
numUniquePredictorVars = numel(uniquePredictorVars);
predMeans = [];
predStds = [];
for i = 1:numUniquePredictorVars
currMetadataInds = currPredictorVars(:, 2) == i;
predMeans(i) = nanmean(yvals(currMetadataInds));
predStds(i) = nanstd(yvals(currMetadataInds));
end
jitterInds = randn(length(yvals), 1)*.05;
if responseVarInd == 2 || responseVarInd == 3
semilogy(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
hold on;
set(gca, 'XTick', uniquePredictorVars, 'XTickLabel', metadataXVals);
errorbar(uniquePredictorVars+.25, predMeans, predStds,'k.');
semilogy(uniquePredictorVars+.25, predMeans, 'k*');
else
plot(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
hold on;
set(gca, 'XTick', uniquePredictorVars, 'XTickLabel', metadataXVals);
errorbar(uniquePredictorVars+.25, predMeans, predStds,'k.');
plot(uniquePredictorVars+.25, predMeans, 'k*');
end
box off;
ylabel([ephysVarsNames{responseVarInd} ' (' ephysVarsUnits{responseVarInd} ') ']);
else
% subplot(2,2,j);
jitterInds = randn(length(yvals), 1)*.05;
if predictorVarInd == 7
if responseVarInd == 2
loglog(10.^(currPredictorVars(:, 2)+jitterInds), yvals, 'k.')
else
semilogx(10.^(currPredictorVars(:, 2)+jitterInds), yvals, 'k.')
end
else
plot(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
end
hold on;
box off;
ylabel([ephysVarsNames{responseVarInd} ' (' ephysVarsUnits{responseVarInd} ') ']);
if predictorVarInd == 8
xlabel(' Recording temperature (°C) ');
axis([20 38 0 max(yvals)*1.1]);
elseif predictorVarInd == 7
xlabel(' Animal age (days) ');
set(gca, 'XTick', [10 100])
axis([5 500 0 max(yvals)*1.1]);
end
end
end
% plot some supp figures
responseVarPlotInds = [2 6 4 5 3 6];
predictorVarPlotInds = [4 4 5 8 8 6];
figure;
for j = 1:length(responseVarPlotInds)
subplot(3,2,j);
responseVarInd = responseVarPlotInds(j);
predictorVarInd = predictorVarPlotInds(j);
tempPredictorVars = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,1:2));
currPredictorVars = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,1:2));
if catVarInds(predictorVarInd) % check if variable is categorical
tempPredictorVars(:,2) = mode(tempPredictorVars(:,2));
else
tempPredictorVars(:,2) = nanmean(tempPredictorVars(:,2));
end
predVals = predict(modelStructAll{responseVarInd,predictorVarInd}.model{1}, tempPredictorVars);
yvals = double(modelStructAll{responseVarInd,predictorVarInd}.model{1}.Variables(:,3)) - predVals + nanmean(responseVars(dataUseInds,responseVarInd));
if responseVarInd == 2 || responseVarInd == 5 || responseVarInd == 3
yvals = 10.^yvals;
end
if catVarInds(predictorVarInd) % check if variable is categorical
metadataVals = predictorVarsCat(dataUseInds, predictorVarInd);
numMetadataVals = length(unique(metadataVals));
metadataXVals = unique(metadataVals, 'stable');
uniquePredictorVars = unique(currPredictorVars(:, 2), 'stable');
numUniquePredictorVars = numel(uniquePredictorVars);
predMeans = [];
predStds = [];
for i = 1:numUniquePredictorVars
currMetadataInds = currPredictorVars(:, 2) == i;
predMeans(i) = nanmean(yvals(currMetadataInds));
predStds(i) = nanstd(yvals(currMetadataInds));
end
jitterInds = randn(length(yvals), 1)*.05;
if responseVarInd == 2
semilogy(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
hold on;
set(gca, 'XTick', uniquePredictorVars, 'XTickLabel', metadataXVals);
errorbar(uniquePredictorVars+.25, predMeans, predStds,'k.');
semilogy(uniquePredictorVars+.25, predMeans, 'k*');
else
plot(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
hold on;
set(gca, 'XTick', uniquePredictorVars, 'XTickLabel', metadataXVals);
errorbar(uniquePredictorVars+.25, predMeans, predStds,'k.');
plot(uniquePredictorVars+.25, predMeans, 'k*');
end
box off;
ylabel([ephysVarsNames{responseVarInd} ' (' ephysVarsUnits{responseVarInd} ') ']);
else
% subplot(2,2,j);
jitterInds = randn(length(yvals), 1)*.05;
if predictorVarInd == 7
semilogx(10.^(currPredictorVars(:, 2)+jitterInds), yvals, 'k.')
else
plot(currPredictorVars(:, 2)+jitterInds, yvals, 'k.')
end
hold on;
box off;
ylabel([ephysVarsNames{responseVarInd} ' (' ephysVarsUnits{responseVarInd} ') ']);
if predictorVarInd == 8
xlabel(' Recording temperature (°C) ');
axis([20 38 0 max(yvals)*1.1]);
elseif predictorVarInd == 7
xlabel(' Animal age (days) ');
set(gca, 'XTick', [10 100])
axis([5 500 0 max(yvals)*1.1]);
end
end
end