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@@ -0,0 +1,719 @@
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+ccc
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+%% Define search path.
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+parentDir = fileparts(fileparts(mfilename('fullpath')));
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+saveFolder = [parentDir filesep 'data'];
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+load([saveFolder filesep 'Fig6-T5Tm9-processedTable'], 'ProcessedTable');
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+
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+%%
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+figDir = [parentDir filesep 'figures' filesep 'Fig6' filesep];
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+if ~exist(figDir, 'dir')
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+ mkdir(figDir);
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+end
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+
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+%%
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+stimSetCellStr = getStimSetCellStr(4);
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+
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+StimTable = getStimSubsetTable(ProcessedTable, stimSetCellStr, 7);
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+
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+tuningMethods = StimTable.nonParamTuning{1}(1).tuningMethods;
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+
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+fieldNamesToUse = {'spacing', 'temporalFrequency'};
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+[ stimSize, sortedFieldNameInds, fieldNames, stimParamsFlat ] = ...
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+ StimulusDomain_ND( StimTable.stimParams{1}{1}, fieldNamesToUse );
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+
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+%%
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+spacing = [StimTable.stimParams{1}{1}(2:end).spacing];
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+stimtrans = [StimTable.stimParams{1}{1}(2:end).stimtrans];
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+temporalFreq = unique([stimtrans.mean] ./ spacing);
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+spatialFreq = 1 ./ unique(spacing);
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+
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+%%
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+selectedTuningMethod = '1F_amplitude';
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+
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+tcRoiCell = cellfun(@(x) ...
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+ getTuningCurvePerROI(x(1).tc, tuningMethods, '1F_amplitude'), ...
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+ StimTable.nonParamTuning, 'uni', 0);
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+tm9TCs = [tcRoiCell{:}];
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+%%
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+tcRoiCell = cellfun(@(x) ...
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+ getTuningCurvePerROI(x(2).tc, tuningMethods, '1F_amplitude'), ...
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+ StimTable.nonParamTuning, 'uni', 0);
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+t5TCs = [tcRoiCell{:}];
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+%%
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+
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+[xGrid, yGrid] = meshgrid(spatialFreq, temporalFreq);
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+
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+%% These are the maps which average is shown in figure 6g
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+respIndex = cell2mat(StimTable.responseIndex')';
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+
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+validInds = all(respIndex > 0.5, 2);
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+figure
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+hAx = createSquarishSubplotGrid(sum(validInds));
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+iAx = 1;
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+for iRoi = find(validInds)'
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+ imagesc(hAx(iAx), t5TCs{iRoi})
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+ iAx = iAx + 1;
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+end
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+figure
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+hAx = createSquarishSubplotGrid(sum(validInds));
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+iAx = 1;
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+for iRoi = find(validInds)'
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+ imagesc(hAx(iAx), tm9TCs{iRoi})
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+ iAx = iAx + 1;
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+end
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+%% Export maps for figure 6g
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+% Columns are increasing temporal frequency, rows are increasing spatial
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+% frequency. But transpose the array so it matches the spatial and temporal frequency
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+% axes. Then columns are increasing spatial frequency and rows increasing
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+% temporal frequency.
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+[spatialFreqGrid, temporalFreqGrid] = meshgrid(spatialFreq, temporalFreq); % xGrid-space, yGrid-time
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+% Create table to append columns with ROI tunint to it.
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+DataTable = table(spatialFreqGrid(:), temporalFreqGrid(:), 'VariableNames', {'spatial_frequency_cycles_per_deg', 'temporal_frequency_Hz'});
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+
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+for iRoi = find(validInds)'
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+ tm9Map = tm9TCs{iRoi}';
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+ t5Map = t5TCs{iRoi}';
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+ DataTable.(sprintf(['ROI%d_Tm9_F1_amplitude'], iRoi)) = tm9Map(:);
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+ DataTable.(sprintf(['ROI%d_T5_F1_amplitude'], iRoi)) = t5Map(:);
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+end
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+
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+dataFileName = [figDir filesep 'Fig6.xls'];
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+
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+sheetName = 'Fig6g';
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+writetable(DataTable, dataFileName, 'Sheet', sheetName, 'Range', 'A1');
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+
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+%% Figure 6h
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+hFig = createPrintFig(10*[1/3 1]);
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+support = [-1-eps 1+eps];
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+plotOrientation = 'vertical'; % Can change to plotHorizontalBool.
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+halveBean = false;
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+useAlpha = true;
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+bwOption = true;
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+mapCorrs = cellfun(@(x,y) corr2(x, y), t5TCs(validInds), tm9TCs(validInds));
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+beanPlot({mapCorrs},{'Tm9-T5'},'k', 1, gca, support, plotOrientation, halveBean);
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+% beanPlot({mapCorrs},{'Tm4-T5'},'k', 1, gca, support, plotOrientation, halveBean);
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+print(gcf, [figDir 'tuningMap-corr-bean' '.pdf'], '-dpdf', '-r300');
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+%% Export pairwise correlation values ffrom figure 6h
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+
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+labelStrCell = sprintfc(['ROI%d_T5vsTm9_F1_Amplitude_CorrelationCoefficient'], find(validInds));
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+for iRow = 1: numel(labelStrCell)
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+ xlswrite(dataFileName, labelStrCell(iRow), 'Fig6h', ['A' num2str(iRow)])
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+ xlswrite(dataFileName, {mapCorrs(iRow)}, 'Fig6h', ['B' num2str(iRow)])
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+end
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+%% Shuffle control for correlation of maps for confidence interval in Fig 6h
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+validT5TCs = t5TCs(validInds);
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+validTm9TCs = tm9TCs(validInds);
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+mapCorrs = cellfun(@(x,y) corr2(x, y), validT5TCs, validTm9TCs);
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+nShuffles = 1000000;
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+% exactShuffles = nchoosek(2 * sum(validInds), sum(validInds)); %even 14
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+% gives 40116600 shuffles, so 1e6 is ok approximation
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+shuffleMapCorrs = zeros(nShuffles, 1);
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+
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+for iShuffle = 1: nShuffles
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+ shuffleInds = randperm(numel(validTm9TCs));
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+ shuffleMapCorrs(iShuffle) = mean(cellfun(@(x,y) corr2(x, y), ...
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+ validT5TCs, ...
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+ validTm9TCs(shuffleInds)));
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+end
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+pValueCorrMaps = sum(shuffleMapCorrs > mean(mapCorrs)) / nShuffles;
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+corrConfInt = arrayfun(@(x) quantile(shuffleMapCorrs, x), [0.05, 0.95]);
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+meanShuffleCorr = mean(shuffleMapCorrs);
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+
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+%%
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+% Plot spatial tuning by collapsing over temporal frequencies.
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+spatialTuning{1} = cell2mat(cellfun(@(x) mean(x, 2)', t5TCs, 'uni', 0)');
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+spatialTuning{2} = cell2mat(cellfun(@(x) mean(x, 2)', tm9TCs, 'uni', 0)');
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+% Plot temporal tuning by collapsing over spatial frequencies.
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+temporalTuning{1} = cell2mat(cellfun(@(x) mean(x, 1), t5TCs, 'uni', 0)');
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+temporalTuning{2} = cell2mat(cellfun(@(x) mean(x, 1), tm9TCs, 'uni', 0)');
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+%%
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+spatialTuning = cellfun(@(x) x./max(x, [], 2), spatialTuning, 'uni', 0);
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+temporalTuning = cellfun(@(x) x./max(x, [], 2), temporalTuning, 'uni', 0);
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+validInds = all(respIndex > 0.5, 2); % Response quality threshold
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+
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+%% From here we get mean maps in figure 6g
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+selectedTuningMethod = '1F_amplitude';
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+createPrintFig(10*[1 1]);
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+plotROISpatioTemporalTuning(spatialFreq, temporalFreq, mean(cat(3, t5TCs{validInds}), 3))
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+print(gcf, [figDir 'tuningMap-roiMean-0p5respInd' selectedTuningMethod 'T5' '.pdf'], '-dpdf', '-r300');
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+
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+createPrintFig(10*[1 1]);
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+plotROISpatioTemporalTuning(spatialFreq, temporalFreq, mean(cat(3, tm9TCs{validInds}), 3))
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+print(gcf, [figDir 'tuningMap-roiMean-0p5respInd' selectedTuningMethod 'Tm9' '.pdf'], '-dpdf', '-r300');
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+
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+%%
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+ProcessedTable = ProcessedTable(ProcessedTable.typeOfProblem == 0, :);
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+
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+for iFly = 1: numel(unique(ProcessedTable.flyInd))
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+ flyIndsCell = ProcessedTable.flyInd == iFly;
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+ flyStims = ProcessedTable(flyIndsCell, :).stimParamFileName;
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+ % Find if stim is within the chosen subset.
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+ flyStimInds{iFly} = (cellfun(@(x) find(strcmp(x, stimSetCellStr)), flyStims, 'uni', 0));
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+ % If it contains stimuli, check that it includes the 5 chosen.
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+ if ~isempty(flyStimInds{iFly})
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+ setDiff{iFly} = setdiff(1: 7, [flyStimInds{iFly}{:}]);
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+ else
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+ setDiff{iFly} = -1;
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+ end
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+ % For flies with all chosen stimuli order the table accordingly.
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+ if isempty(setDiff{iFly})
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+ sortFlyIndsTemp = find(flyIndsCell);
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+ [~, sortInd] = sort([flyStimInds{iFly}{:}]);
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+ sortFlyInds{iFly} = sortFlyIndsTemp(sortInd);
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+ end
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+end
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+
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+StimOrderedTable = ProcessedTable(cat(1, sortFlyInds{:}), :);
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+StimOrderedTable = StimOrderedTable(StimOrderedTable.flyInd~=7,:);
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+%% Split table into cell types.
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+tokens = arrayfun(@(x) regexp(x, '.*Tm(\d).*', 'tokens'), StimOrderedTable.timeSeriesPath);
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+cellTypeNumArray = cellfun(@(x) str2num(x{1}{1}), tokens);
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+cellTypeNum = unique(cellTypeNumArray);
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+for iNeuron = 1: numel(cellTypeNum)
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+ StimTableNeuronCell{iNeuron} = StimOrderedTable(cellTypeNumArray == cellTypeNum(iNeuron), :);
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+end
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+%%
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+stimInd = 1;
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+nStims = numel(stimSetCellStr);
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+for iNeuron = 1: numel(cellTypeNum)
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+ for jStim = 1: nStims
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+ StimTableNeuronStim{iNeuron, jStim} = getStimSubsetTable(StimTableNeuronCell{iNeuron}, stimSetCellStr, jStim);
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+ end
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+end
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+
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+%% Extract the parameters for the analysis of spatial RFs.
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+clear amp pos width rsquare responseIndex
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+jBar = 1;
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+for iStim = 2:5
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+ dummyTable = StimTableNeuronStim{1, iStim};
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+ for jChannel = 1: 2
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+ responseIndex{jChannel}(jBar, :) = cell2mat(cellfun(@(x, y) x(jChannel, 1: end - 1*0), dummyTable.responseIndex, 'uni', 0)');
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+ paramsCell = cellfun(@(x) x(jChannel, 1: end - 1*0), dummyTable.paramTuning, 'uni', 0);
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+ amp{jChannel}(jBar, :) = cell2mat(cellfun(@(x) arrayfun(@(y) y.fit.a1, x, 'uni', 1), paramsCell, 'uni', 0)');
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+ pos{jChannel}(jBar, :) = cell2mat(cellfun(@(x) arrayfun(@(y) y.fit.b1, x, 'uni', 1), paramsCell, 'uni', 0)');
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+ width{jChannel}(jBar, :) = cell2mat(cellfun(@(x) arrayfun(@(y) y.fit.c1, x, 'uni', 1), paramsCell, 'uni', 0)');
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+ rsquare{jChannel}(jBar, :) = cell2mat(cellfun(@(x) arrayfun(@(y) y.gof.rsquare, x, 'uni', 1), paramsCell, 'uni', 0)');
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+ end
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+ jBar = jBar + 1;
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+end
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+pos = cellfun(@(x) 2 * (x - 1), pos, 'uni', 0);
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+width = cellfun(@(x) 2 * (2 * sqrt(log(2))) * x, width, 'uni', 0);
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+%%
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+flyInds = repelem(1:size(StimTableNeuronStim{1}), ...
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+ cellfun(@(x) size(x, 2), StimTableNeuronStim{1}.responseIndex));
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+responseIndexOff = cat(1, responseIndex{1}(1:2, :), responseIndex{2}(1:2, :));
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+rSquareOff = cat(1, rsquare{1}(1:2, :), rsquare{2}(1:2, :));
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+
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+minOffRSquare = cell2mat(cellfun(@(x) min(x(1:2, :)), rsquare, 'uni', 0)');
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+minOnRSquare = cell2mat(cellfun(@(x) min(x(3:4, :)), rsquare, 'uni', 0)');
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+thresholdRSquare = 0.25;
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+minRSquare = min(minOffRSquare);
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+minRespInd = min(responseIndexOff);
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+isAboveThreshold = minRSquare > thresholdRSquare & minRespInd > 0.3;
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+
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+%% Figure 6f and supplementary figure 5a
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+responseIndexOff = cat(1, responseIndex{1}(1:2, :), responseIndex{2}(1:2, :));
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+responseIndexOn = cat(1, responseIndex{1}(3:4, :), responseIndex{2}(3:4, :));
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+
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+rSquareOff = cat(1, rsquare{1}(1:2, :), rsquare{2}(1:2, :));
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+rSquareOn = cat(1, rsquare{1}(3:4, :), rsquare{2}(3:4, :));
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+
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+minOffRSquare = cell2mat(cellfun(@(x) min(x(1:2, :)), rsquare, 'uni', 0)');
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+minOnRSquare = cell2mat(cellfun(@(x) min(x(3:4, :)), rsquare, 'uni', 0)');
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+
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+plotOFF = true;
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+if plotOFF
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+ minRespInd = min(responseIndexOff);
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+ thresholdRSquare = 0.25;
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+ minRSquare = min(minOffRSquare);
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+ isAboveThreshold = minRSquare > thresholdRSquare & minRespInd > 0.3;
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+else
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+ if 0
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+ minRespInd = max(responseIndexOn);
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+ thresholdRSquare = 0;
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+ minRSquare = max(minOnRSquare);
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+ isAboveThreshold = minRSquare > thresholdRSquare & minRespInd > 0.3;
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+ else
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+ minRespInd = min(responseIndexOff);
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+ thresholdRSquare = 0.25;
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+ minRSquare = min(minOffRSquare);
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+ isAboveThreshold = minRSquare > thresholdRSquare & minRespInd > 0.3;
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+ end
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+end
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+
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+clear hSubAx
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+hFig = createPrintFig(20 * [1 2/3]);
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+set(hFig,'color','w');
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+
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+colorLims = [-1 1] + 0*thresholdRSquare;
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+fitParamsCell = {width , pos};
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+for jOrientation = 1: 2
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+ for iParam = 1: numel(fitParamsCell)
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+ iAx = numel(fitParamsCell) * (jOrientation - 1) + iParam;
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+ hSubAx(iAx) = subplot(2, numel(fitParamsCell), iAx);
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+ if plotOFF
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+ x = fitParamsCell{iParam}{1}(jOrientation, isAboveThreshold);
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+ y = fitParamsCell{iParam}{2}(jOrientation, isAboveThreshold);
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+ else
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+ x = fitParamsCell{iParam}{1}(2 + jOrientation, isAboveThreshold);
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+ y = fitParamsCell{iParam}{2}(2 + jOrientation, isAboveThreshold);
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+ end
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+ hScatter = scatter(x, y, [], [1 1 1]*0.25, 'filled', 'MarkerFaceAlpha', 1);
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+
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+ minVal = min([hSubAx(iAx).XLim(1) hSubAx(iAx).YLim(1)]);
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+ maxVal = min([hSubAx(iAx).XLim(2) hSubAx(iAx).YLim(2)]);
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+ lsline
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+ line([minVal maxVal], [minVal maxVal], 'Color', 0.5 * [1 1 1]);
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+ hChildren = get(hSubAx(iAx), 'Children');
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+ set(gca, 'Children', [hChildren(end); hChildren(1: end - 1)]);
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+ axis square;
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+ [corrCoef, p] = corr(x(:), y(:));
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+ legStr = ['n = ' num2str(numel(x)) ', corr = ' num2str(corrCoef, 2) ', p = ' num2str(p, 2)];
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+ hLeg = legend(hScatter, legStr, 'Location', 'northoutside');
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+ hLeg.Box = 'off';
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+ end
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+end
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+linkaxes(hSubAx([1,3]), 'xy')
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+linkaxes(hSubAx([2,4]), 'xy')
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+hYLabel(1) = ylabel(hSubAx(1), 'T5 OFF RF FWHM Horizontal Bar (deg)');
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+hXLabel(1) = xlabel(hSubAx(1), 'Tm9 OFF RF FWHM Horizontal Bar (deg)');
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+hYLabel(2) = ylabel(hSubAx(2), 'T5 OFF RF Center Horizontal Bar (deg)');
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+hXLabel(2) = xlabel(hSubAx(2), 'Tm9 OFF RF Center Horizontal Bar (deg)');
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+hYLabel(3) = ylabel(hSubAx(3), 'T5 OFF RF FWHM Vertical Bar (deg)');
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+hXLabel(3) = xlabel(hSubAx(3), 'Tm9 OFF RF FWHM Vertical Bar (deg)');
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+hYLabel(4) = ylabel(hSubAx(4), 'T5 OFF RF Center Vertical Bar (deg)');
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+hXLabel(4) = xlabel(hSubAx(4), 'Tm9 OFF RF Center Vertical Bar (deg)');
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+
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+[hXLabel.FontSize] = deal(10);
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+[hYLabel.FontSize] = deal(10);
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+hLeg.FontSize = 8;
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+[hSubAx.FontSize] = deal(8);
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+[hSubAx.Color] = deal('none');
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+
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+arrayfun(@prettifyAxes, [hSubAx]);
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+if plotOFF
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+ print(gcf, [figDir 'rfFitParamsScatter-Off-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
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+else
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+ print(gcf, [figDir 'rfFitParamsScatter-On-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
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+end
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+%% Export data for figure 6f ans supplementary figure 6a
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+tm9XFWHM = width{1}(1, isAboveThreshold);
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+t5XFWHM = width{2}(1, isAboveThreshold);
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+tm9YFWHM = width{1}(2, isAboveThreshold);
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+t5YFWHM = width{2}(2, isAboveThreshold);
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+
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+tm9XPos = pos{1}(1, isAboveThreshold);
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+t5XPos = pos{2}(1, isAboveThreshold);
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+tm9YPos = pos{1}(2, isAboveThreshold);
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+t5YPos = pos{2}(2, isAboveThreshold);
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+
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+
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+DataTable = table(tm9XFWHM(:), t5XFWHM(:), tm9YFWHM(:), t5YFWHM(:), ...
|
|
|
+ tm9XPos(:), t5XPos(:), tm9YPos(:), t5YPos(:), ...
|
|
|
+ 'VariableNames', ...
|
|
|
+ {'Tm9_OFF_RF_FWHM_Horizontal_Bar_deg', ...
|
|
|
+ 'T5_OFF_RF_FWHM_Horizontal_Bar_deg', ...
|
|
|
+ 'Tm9_OFF_RF_FWHM_Vertical_Bar_deg', ...
|
|
|
+ 'T5_OFF_RF_FWHM_Vertical_Bar_deg', ...
|
|
|
+ 'Tm9_OFF_RF_Position_Horizontal_Bar_deg', ...
|
|
|
+ 'T5_OFF_RF_Position_Horizontal_Bar_deg', ...
|
|
|
+ 'Tm9_OFF_RF_Position_Vertical_Bar_deg', ...
|
|
|
+ 'T5_OFF_RF_Position_Vertical_Bar_deg', ...
|
|
|
+ });
|
|
|
+
|
|
|
+
|
|
|
+dataFileName = [figDir filesep 'Fig6.xls'];
|
|
|
+
|
|
|
+sheetName = 'Fig6f';
|
|
|
+writetable(DataTable(:, 1: 4), dataFileName, 'Sheet', sheetName, 'Range', 'A1');
|
|
|
+
|
|
|
+sheetName = 'SuppFig6ab';
|
|
|
+writetable(DataTable(:, 5: 8), dataFileName, 'Sheet', sheetName, 'Range', 'A1');
|
|
|
+
|
|
|
+%% Reply to reviwer#1 comment 5
|
|
|
+doBootstrap = 0;
|
|
|
+
|
|
|
+xH = vec(width{1}(1, isAboveThreshold));
|
|
|
+yH = vec(width{2}(1, isAboveThreshold));
|
|
|
+xV = vec(width{1}(2, isAboveThreshold));
|
|
|
+yV = vec(width{2}(2, isAboveThreshold));
|
|
|
+tmpInds = flyInds(isAboveThreshold);
|
|
|
+nValidFlies = numel(unique(tmpInds));
|
|
|
+iAx = 1;
|
|
|
+
|
|
|
+
|
|
|
+clear hSubAx
|
|
|
+mSize = 15;
|
|
|
+hFig = createPrintFig(25 * [1 1/3]);
|
|
|
+for iFly = unique(tmpInds)
|
|
|
+ hSubAx(iAx, 1) = subplot(2, nValidFlies, iAx);
|
|
|
+ x = xH(iFly == tmpInds);
|
|
|
+ y = yH(iFly == tmpInds);
|
|
|
+ scatter(xH, yH, mSize, 'filled', 'MarkerFaceAlpha', 0.1, 'MarkerFaceColor', 'k');
|
|
|
+ hold on;
|
|
|
+ hScatter = scatter(x, y, mSize);
|
|
|
+ hLines = lsline;
|
|
|
+ delete(hLines(2));
|
|
|
+% refline(1,0)
|
|
|
+ if doBootstrap && numel(x) > 1
|
|
|
+ [bootstrapCorrCI, bootstrapCorrs] = bootci(10000, @corr, x(:), y(:));
|
|
|
+ legStr = sprintf('n = %d\ncorr = %.2f\n0.95-CI = [%.2f, %.2f]', ...
|
|
|
+ numel(x), mean(bootstrapCorrs), bootstrapCorrCI);
|
|
|
+ else
|
|
|
+ [corrCoef, p] = corr(x(:), y(:));
|
|
|
+ legStr = ['n = ' num2str(numel(x)) ...
|
|
|
+ ', \n corr = ' num2str(corrCoef, 2) ', \n p = ' num2str(p, 2)];
|
|
|
+ % hLeg = legend(hScatter, legStr, 'Location', 'northoutside');
|
|
|
+ end
|
|
|
+% hLeg.Box = 'off';
|
|
|
+ title(sprintf(legStr));
|
|
|
+ corrArray(iAx, 1) = corrCoef;
|
|
|
+
|
|
|
+ hSubAx(iAx, 2) = subplot(2, nValidFlies, nValidFlies + iAx);
|
|
|
+ x = xV(iFly == tmpInds);
|
|
|
+ y = yV(iFly == tmpInds);
|
|
|
+ scatter(xV, yV, mSize, 'filled', 'MarkerFaceAlpha', 0.1, 'MarkerFaceColor', 'k');
|
|
|
+ hold on;
|
|
|
+ hScatter = scatter(x, y, mSize);
|
|
|
+ hLines = lsline;
|
|
|
+ delete(hLines(2));
|
|
|
+ if doBootstrap && numel(x) > 1
|
|
|
+ [bootstrapCorrCI, bootstrapCorrs] = bootci(10000, @corr, x(:), y(:));
|
|
|
+ legStr = sprintf('n = %d\ncorr = %.2f\n0.95-CI = [%.2f, %.2f]', ...
|
|
|
+ numel(x), mean(bootstrapCorrs), bootstrapCorrCI);
|
|
|
+ else
|
|
|
+ [corrCoef, p] = corr(x(:), y(:));
|
|
|
+ legStr = ['n = ' num2str(numel(x)) ...
|
|
|
+ '\n corr = ' num2str(corrCoef, 2) '\n p = ' num2str(p, 2)];
|
|
|
+ % hLeg = legend(hScatter, legStr, 'Location', 'northoutside');
|
|
|
+ end
|
|
|
+ title(sprintf(legStr))
|
|
|
+ corrArray(iAx, 2) = corrCoef;
|
|
|
+ iAx = iAx + 1;
|
|
|
+end
|
|
|
+
|
|
|
+axis(hSubAx, 'square');
|
|
|
+linkaxes(hSubAx, 'xy')
|
|
|
+clear hXLabel hYLabel
|
|
|
+hXLabel(1) = xlabel(hSubAx(1, 1), 'Tm9 fwhm hor. (deg)');
|
|
|
+hYLabel(1) = ylabel(hSubAx(1, 1), 'T5 fwhm hor. (deg)');
|
|
|
+hXLabel(2) = xlabel(hSubAx(1, 2), 'Tm9 fwhm vert. (deg)');
|
|
|
+hYLabel(2) = ylabel(hSubAx(1, 2), 'T5 fwhm vert. (deg)');
|
|
|
+
|
|
|
+[hXLabel(1).FontSize] = deal(10);
|
|
|
+[hYLabel(1).FontSize] = deal(10);
|
|
|
+% hLeg.FontSize = 8;
|
|
|
+[hSubAx.FontSize] = deal(8);
|
|
|
+[hSubAx.Color] = deal('none');
|
|
|
+arrayfun(@prettifyAxes, [hSubAx]);
|
|
|
+
|
|
|
+print(gcf, [figDir 'rfFwhmScatterPerFly-Off-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
|
|
|
+
|
|
|
+%% average values within fly and plot
|
|
|
+% fly means
|
|
|
+xHFlyMeans = accumarray(tmpInds', xH, [], @mean);
|
|
|
+yHFlyMeans = accumarray(tmpInds', yH, [], @mean);
|
|
|
+xHFlyStds = accumarray(tmpInds', xH, [], @std);
|
|
|
+yHFlyStds = accumarray(tmpInds', yH, [], @std);
|
|
|
+xHFlyMins = accumarray(tmpInds', xH, [], @min);
|
|
|
+yHFlyMins = accumarray(tmpInds', yH, [], @min);
|
|
|
+xHFlyMaxs = accumarray(tmpInds', xH, [], @max);
|
|
|
+yHFlyMaxs = accumarray(tmpInds', yH, [], @max);
|
|
|
+
|
|
|
+[x, y] = deal(xHFlyMeans(xHFlyMeans~=0), yHFlyMeans(xHFlyMeans~=0));
|
|
|
+[xStd, yStd] = deal(xHFlyStds(xHFlyMeans~=0), yHFlyStds(xHFlyMeans~=0));
|
|
|
+[xMin, yMin] = deal(xHFlyMins(xHFlyMeans~=0), yHFlyMins(xHFlyMeans~=0));
|
|
|
+[xMax, yMax] = deal(xHFlyMaxs(xHFlyMeans~=0), yHFlyMaxs(xHFlyMeans~=0));
|
|
|
+
|
|
|
+hFig = createPrintFig(7 * [1 1]);
|
|
|
+hScatter = errorbar(x, y, xMin - x, xMax - x, yMin - y, yMax - y, ...
|
|
|
+ 'LineStyle', 'none', 'CapSize', 0, 'LineWidth', 1, ...
|
|
|
+ 'Color', [1 1 1] * 0.4);
|
|
|
+hold on
|
|
|
+hScatter = scatter(x, y, 25, 'filled', 'MarkerFaceColor', [1 1 1] * 0.20);
|
|
|
+lsline
|
|
|
+if doBootstrap && numel(x) > 1
|
|
|
+ [bootstrapCorrCI, bootstrapCorrs] = bootci(10000, @corr, x(:), y(:));
|
|
|
+ legStr = sprintf('n = %d, corr = %.2f\n0.95-CI = [%.2f, %.2f]', ...
|
|
|
+ numel(x), mean(bootstrapCorrs), bootstrapCorrCI);
|
|
|
+else
|
|
|
+ [corrCoef, p] = corr(x(:), y(:));
|
|
|
+ legStr = ['n = ' num2str(numel(x)) ...
|
|
|
+ ', \n corr = ' num2str(corrCoef, 2) ', \n p = ' num2str(p, 2)];
|
|
|
+end
|
|
|
+title(sprintf(legStr));
|
|
|
+xlabel('Tm9 fwhm hor. (deg)');
|
|
|
+ylabel('T5 fwhm hor. (deg)');
|
|
|
+% hLeg = legend(hScatter, legStr, 'Location', 'northoutside');
|
|
|
+% hLeg.Box = 'off';
|
|
|
+prettifyAxes(gca);
|
|
|
+
|
|
|
+print(gcf, [figDir 'rfFwhmScatterFlyMeans-XOff-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
|
|
|
+%% Same fo vertical
|
|
|
+xHFlyMeans = accumarray(tmpInds', xV, [], @mean);
|
|
|
+yHFlyMeans = accumarray(tmpInds', yV, [], @mean);
|
|
|
+xHFlyStds = accumarray(tmpInds', xV, [], @std);
|
|
|
+yHFlyStds = accumarray(tmpInds', yV, [], @std);
|
|
|
+xHFlyMins = accumarray(tmpInds', xV, [], @min);
|
|
|
+yHFlyMins = accumarray(tmpInds', yV, [], @min);
|
|
|
+xHFlyMaxs = accumarray(tmpInds', xV, [], @max);
|
|
|
+yHFlyMaxs = accumarray(tmpInds', yV, [], @max);
|
|
|
+
|
|
|
+[x, y] = deal(xHFlyMeans(xHFlyMeans~=0), yHFlyMeans(xHFlyMeans~=0));
|
|
|
+[xStd, yStd] = deal(xHFlyStds(xHFlyMeans~=0), yHFlyStds(xHFlyMeans~=0));
|
|
|
+[xMin, yMin] = deal(xHFlyMins(xHFlyMeans~=0), yHFlyMins(xHFlyMeans~=0));
|
|
|
+[xMax, yMax] = deal(xHFlyMaxs(xHFlyMeans~=0), yHFlyMaxs(xHFlyMeans~=0));
|
|
|
+
|
|
|
+hFig = createPrintFig(7 * [1 1]);
|
|
|
+hScatter = errorbar(x, y, xMin - x, xMax - x, yMin - y, yMax - y, ...
|
|
|
+ 'LineStyle', 'none', 'CapSize', 0, 'LineWidth', 1, ...
|
|
|
+ 'Color', [1 1 1] * 0.4);
|
|
|
+% hScatter = errorbar(x, y, -xStd, xStd, -yStd, yStd, ...
|
|
|
+% 'LineStyle', 'none', 'CapSize', 0, 'LineWidth', 1, ...
|
|
|
+% 'Color', [1 1 1] * 0.2);
|
|
|
+hold on
|
|
|
+hScatter = scatter(x, y, 25, 'filled', 'MarkerFaceColor', [1 1 1] * 0.20);
|
|
|
+lsline
|
|
|
+if doBootstrap && numel(x) > 1
|
|
|
+ [bootstrapCorrCI, bootstrapCorrs] = bootci(10000, @corr, x(:), y(:));
|
|
|
+ legStr = sprintf('n = %d, corr = %.2f\n0.95-CI = [%.2f, %.2f]', ...
|
|
|
+ numel(x), mean(bootstrapCorrs), bootstrapCorrCI);
|
|
|
+else
|
|
|
+ [corrCoef, p] = corr(x(:), y(:));
|
|
|
+ legStr = ['n = ' num2str(numel(x)) ...
|
|
|
+ ', \n corr = ' num2str(corrCoef, 2) ', \n p = ' num2str(p, 2)];
|
|
|
+end
|
|
|
+title(sprintf(legStr));
|
|
|
+xlabel('Tm9 fwhm vert. (deg)');
|
|
|
+ylabel('T5 fwhm vert. (deg)');
|
|
|
+% hLeg = legend(hScatter, legStr, 'Location', 'northoutside');
|
|
|
+% hLeg.Box = 'off';
|
|
|
+prettifyAxes(gca);
|
|
|
+
|
|
|
+print(gcf, [figDir 'rfFwhmScatterFlyMeans-YOff-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
|
|
|
+
|
|
|
+%% Check RF position distribution
|
|
|
+
|
|
|
+xH = vec(pos{1}(1, isAboveThreshold));
|
|
|
+yH = vec(pos{2}(1, isAboveThreshold));
|
|
|
+xV = vec(pos{1}(2, isAboveThreshold));
|
|
|
+yV = vec(pos{2}(2, isAboveThreshold));
|
|
|
+tmpInds = flyInds(isAboveThreshold);
|
|
|
+nValidFlies = numel(unique(tmpInds));
|
|
|
+iAx = 1;
|
|
|
+
|
|
|
+hFig = createPrintFig(7 * [ 1 2/3]);
|
|
|
+
|
|
|
+hHAx(1) = subplot(1, 2, 1);
|
|
|
+hHist(1) = histogram(xH - yH);
|
|
|
+hHAx(2) = subplot(1, 2, 2);
|
|
|
+hHist(2) = histogram(xV - yV);
|
|
|
+
|
|
|
+[hHist.EdgeColor] = deal('w');
|
|
|
+[hHist.FaceColor] = deal([1 1 1] * 0.0);
|
|
|
+
|
|
|
+title(hHAx(1), {'Hor. RF Pos.', '(Tm9 - T5)'});
|
|
|
+title(hHAx(2), {'Hor. RF Pos.', '(Tm9 - T5)'});
|
|
|
+
|
|
|
+xlabel(hHAx(1), 'RF Pos. Diff. (deg)');
|
|
|
+
|
|
|
+arrayfun(@prettifyAxes, hHAx);
|
|
|
+setFontForThesis(hHAx, hFig);
|
|
|
+
|
|
|
+print(gcf, [figDir 'rfPosDiffScatter-Off-rSquare-' num2str(thresholdRSquare) '.pdf'], '-dpdf', '-r300');
|
|
|
+
|
|
|
+%% Extract the parameters for the analysis of spatial RFs.
|
|
|
+% clear amp pos width rsquare responseIndex
|
|
|
+tuningMethod = 'median';
|
|
|
+StimTableTmp = StimTableNeuronStim{2};
|
|
|
+tuningInd = strcmp(tuningMethod, StimTableTmp.nonParamTuning{1}(1).tuningMethods);
|
|
|
+jBar = 1;
|
|
|
+for iStim = 2:5
|
|
|
+ dummyTable = StimTableNeuronStim{1, iStim};
|
|
|
+ for jChannel = 1: 2
|
|
|
+ responseIndex{jChannel}(jBar, :) = cell2mat(cellfun(@(x, y) x(jChannel, 1: end - 1*0), dummyTable.responseIndex, 'uni', 0)');
|
|
|
+ % Hard coded getting rid of background ROI, but this should also be
|
|
|
+ % imporved to handle the roiMeta field to discard invalid ROIs
|
|
|
+ % (rois with NaNs)
|
|
|
+ tuningCurves{jChannel}(jBar, :) = cellfun(@(x) squeeze(x(jChannel).tc{tuningInd}(:, 1: end - 1, :)), ...
|
|
|
+ dummyTable.nonParamTuning, 'uni', 0);
|
|
|
+ end
|
|
|
+ jBar = jBar + 1;
|
|
|
+end
|
|
|
+%% Plot tuning curves.
|
|
|
+for iStim = 1: 4
|
|
|
+ tm9AllTCs{iStim} = cat(1, tuningCurves{1}{iStim, :});
|
|
|
+ t5AllTCs{iStim} = cat(1, tuningCurves{2}{iStim, :});
|
|
|
+end
|
|
|
+% Align to fit
|
|
|
+% pos is already original parameter b1 as 2*(b1 - 1)
|
|
|
+
|
|
|
+lagsCell = cellfun(@(x, y) round(x/2+1 - size(y, 2) / 2), ...
|
|
|
+ mat2cell(pos{1}, [1 1 1 1])', tm9AllTCs, 'uni', 0);
|
|
|
+Tm9BarRFs = cellfun(@(x, y) alignRFsToAbsMax(x, 0, y'), tm9AllTCs, lagsCell, 'uni', 0);
|
|
|
+
|
|
|
+lagsCell = cellfun(@(x, y) round(x/2+1 - size(y, 2) / 2), ...
|
|
|
+ mat2cell(pos{2}, [1 1 1 1])', t5AllTCs, 'uni', 0);
|
|
|
+T5BarRFs = cellfun(@(x, y) alignRFsToAbsMax(x, 0, y'), t5AllTCs, lagsCell, 'uni', 0);
|
|
|
+
|
|
|
+
|
|
|
+%% Blank epoch was already deleted, now delete vertical bars on edges of screen.
|
|
|
+Tm9BarRFs{2}(:, end-1: end) = [];
|
|
|
+Tm9BarRFs{4}(:, end-1: end) = [];
|
|
|
+Tm9BarRFs{2}(:, 1: 2) = [];
|
|
|
+Tm9BarRFs{4}(:, 1: 2) = [];
|
|
|
+
|
|
|
+T5BarRFs{2}(:, end-1: end) = [];
|
|
|
+T5BarRFs{4}(:, end-1: end) = [];
|
|
|
+T5BarRFs{2}(:, 1: 2) = [];
|
|
|
+T5BarRFs{4}(:, 1: 2) = [];
|
|
|
+%% Plot only analyzed OFF RFs.
|
|
|
+minRespInd = min(responseIndexOff);
|
|
|
+thresholdRSquare = 0.25;
|
|
|
+minRSquare = min(minOffRSquare);
|
|
|
+isAboveThreshold = minRSquare > thresholdRSquare & minRespInd > 0.3;
|
|
|
+Tm9BarRFs = cellfun(@(x) x(isAboveThreshold, :), Tm9BarRFs, 'uni', 0);
|
|
|
+T5BarRFs = cellfun(@(x) x(isAboveThreshold, :), T5BarRFs, 'uni', 0);
|
|
|
+%%
|
|
|
+% Plot lasagna plots, separate figures for off and on, and reapeated panel
|
|
|
+% is to have a colorbar that does not distort existing axis of interest.
|
|
|
+hFig = createPrintFig(15 * [ 1 1/4]);
|
|
|
+hSubAx = plotLasagnaTracesFromCell(gcf, [Tm9BarRFs, Tm9BarRFs(1)], brewermap(6, 'Paired'));
|
|
|
+colorbar
|
|
|
+arrayfun(@(x) set(get(x, 'YAxis'), 'Visible', 'on'), hSubAx([1, 3], 1));
|
|
|
+arrayfun(@(x) set(get(x, 'YAxis'), 'Visible', 'off'), hSubAx(2: end, 2));
|
|
|
+arrayfun(@prettifyAxes, hSubAx)
|
|
|
+arrayfun(@offsetAxes, hSubAx)
|
|
|
+arrayfun(@(x) set(x, 'Color', 'none'), hSubAx);
|
|
|
+
|
|
|
+% set(hSubAx, 'Visible', 'off')
|
|
|
+linkaxes(hSubAx(1: 2, 1), 'y')
|
|
|
+linkaxes(hSubAx(3: 4, 1), 'y')
|
|
|
+hFig.Color = 'none';
|
|
|
+figFileName = ['Tm9-RFcurves-OFF-XY-lasagna'];
|
|
|
+print(hFig, [figDir figFileName '.pdf'], '-dpdf')
|
|
|
+%%
|
|
|
+hFig = createPrintFig(15 * [ 1 1/4]);
|
|
|
+hSubAx = plotLasagnaTracesFromCell(gcf, [T5BarRFs, T5BarRFs(1)], brewermap(6, 'Paired'));
|
|
|
+colorbar
|
|
|
+arrayfun(@(x) set(get(x, 'YAxis'), 'Visible', 'on'), hSubAx([1, 3], 1));
|
|
|
+arrayfun(@(x) set(get(x, 'YAxis'), 'Visible', 'off'), hSubAx(2: end, 2));
|
|
|
+arrayfun(@prettifyAxes, hSubAx)
|
|
|
+arrayfun(@offsetAxes, hSubAx)
|
|
|
+arrayfun(@(x) set(x, 'Color', 'none'), hSubAx);
|
|
|
+
|
|
|
+% set(hSubAx, 'Visible', 'off')
|
|
|
+linkaxes(hSubAx(1: 2, 1), 'y')
|
|
|
+linkaxes(hSubAx(3: 4, 1), 'y')
|
|
|
+hFig.Color = 'none';
|
|
|
+figFileName = ['T5-RFcurves-OFF-XY-lasagna'];
|
|
|
+print(hFig, [figDir figFileName '.pdf'], '-dpdf')
|
|
|
+
|
|
|
+%% Export data for figure 6b-e
|
|
|
+
|
|
|
+receptiveFields = [Tm9BarRFs; T5BarRFs];
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|
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+dataFileName = [figDir filesep 'Fig6.xls'];
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+cellTypeStr = {'Tm9', 'T5'};
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+sheetNamesCell = {'bc', 'de'};
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+
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+stimNameStrCell = {'_OFF_horizontal_position_deg', '_OFF_vertical_position_deg', ...
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+ '_ON_horizontal_position_deg', '_ON_vertical_position_deg'};
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+
|
|
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+for iCellType = 1: numel(cellTypeStr)
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+ startRow = 1;
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+ sheetName = ['Fig6' sheetNamesCell{iCellType}];
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+ for iStim = 1: 4
|
|
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+ parameterLabel = [cellTypeStr{iCellType} stimNameStrCell{iStim}];
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+ DataTable = createRFsTable(receptiveFields{iCellType, iStim}, parameterLabel);
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+ writetable(DataTable, dataFileName, 'Sheet', sheetName, 'Range', ['A' num2str(startRow)]);
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+ startRow = startRow + size(DataTable, 1) + 2;
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|
+ end
|
|
|
+end
|
|
|
+%% Tests if T5 ON response is 0 for reviewer #2
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|
|
+
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+xH = vec(amp{1}(3, isAboveThreshold));
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|
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+yH = vec(amp{2}(3, isAboveThreshold));
|
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+xV = vec(amp{1}(4, isAboveThreshold));
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+yV = vec(amp{2}(4, isAboveThreshold));
|
|
|
+% use minimum for negative ON response.
|
|
|
+minPeaks = cellfun(@(x) min(x, [], 2), T5BarRFs(3: 4), 'uni', 0);
|
|
|
+tcStd = cellfun(@(x) std(x, [], 2), T5BarRFs(3: 4), 'uni', 0);
|
|
|
+peakStdRatio = cellfun(@(x, y) x ./ y, minPeaks, tcStd, 'uni', 0);
|
|
|
+
|
|
|
+% figure
|
|
|
+% for iAx = 1: 2
|
|
|
+% subplot(1, 2, iAx);
|
|
|
+% histogram(peakStdRatio{iAx})
|
|
|
+% end
|
|
|
+ % check if the fitted amplitude is higher than the tuning curve values
|
|
|
+ % (noise), signrank uses x - y, so tc - amp shuould be positive for a
|
|
|
+ % smaller amplitude (larger negative peak), so we need right tail test.
|
|
|
+[pValCell, isSignificant] = cellfun(@(x, y) arrayfun(@(z) signrank(x(z, :), y(z), 'tail', 'right'), 1: numel(y)), ...
|
|
|
+ T5BarRFs(3: 4), {yH, yV}, 'uni', 0);
|
|
|
+
|
|
|
+proportionSignificant = cellfun(@(x) sum(x) / numel(x), isSignificant);
|
|
|
+
|
|
|
+clear hAx
|
|
|
+hFig = createPrintFig(15 * [3/4 1]);
|
|
|
+hAx(1) = subplot(3, 2, 1);
|
|
|
+histogram(yH);
|
|
|
+hAx(2) = subplot(3, 2, 2);
|
|
|
+histogram(yV);
|
|
|
+
|
|
|
+
|
|
|
+for iAx = 1: 2
|
|
|
+ hAx(2 + iAx) = subplot(3, 2, 2 * iAx + 1);
|
|
|
+ histogram(pValCell{iAx}, 'NumBins', 100);
|
|
|
+end
|
|
|
+hAx(5) = subplot(3, 2, [4, 6]);
|
|
|
+hBar = bar([proportionSignificant; 1 - proportionSignificant]', 'stacked');
|
|
|
+hLeg = legend(hAx(5), {'p < 0.05', 'p >= 0.05'}, 'Location', 'best');
|
|
|
+hAx(5).XTickLabels = {'Hor.', 'Vert.'};
|
|
|
+ylim([0 1])
|
|
|
+
|
|
|
+suptitle('T5 Responses to ON bars')
|
|
|
+
|
|
|
+arrayfun(@prettifyAxes, hAx);
|
|
|
+setFontForThesis(hAx, hFig);
|
|
|
+
|
|
|
+xLabelCell = {'Amplitude Hor. (\Delta F / F_0)', ...
|
|
|
+ 'Amplitude Vert. (\Delta F / F_0)', ...
|
|
|
+ '', 'p-Value', 'Bar orientation'};
|
|
|
+yLabelCell = {'No. ROIs', '', 'No. ROIs', 'No. ROIs', 'Proportion of ROIs'};
|
|
|
+
|
|
|
+arrayfun(@(x, y) xlabel(x, y), hAx, xLabelCell);
|
|
|
+arrayfun(@(x, y) ylabel(x, y), hAx, yLabelCell);
|
|
|
+
|
|
|
+figFileName = ['T5-ON-Amp-XY-stats'];
|
|
|
+print(hFig, [figDir figFileName '.pdf'], '-dpdf')
|
|
|
+
|
|
|
+%%
|
|
|
+
|
|
|
+function tcRoiCell = getTuningCurvePerROI(tc, tuningMethods, selectedTuningMethod)
|
|
|
+%% Expose single Roi tuning maps.
|
|
|
+% The epochs are varying first in the spacing then in the temporal
|
|
|
+% frequency, so tc consists of 1 x nTFreqs and each element is of shape
|
|
|
+% 1 x nRois x nSFreqs.
|
|
|
+% Here we first extract per Roi the nSFreqs tuning, as column vector, and
|
|
|
+% concatenate the nTFreqs column vectors arising this way to get a tuning
|
|
|
+% matrix of nSFreqs x nTFreqs.
|
|
|
+% tc1F = tc(:, strcmp(tuningMethods, '1F_amplitude'));
|
|
|
+tc = tc(:, strcmp(tuningMethods, selectedTuningMethod));
|
|
|
+tcRoiCell = cell(1, size(tc{1}, 2));
|
|
|
+
|
|
|
+for iRoi = 1: size(tc{1}, 2)
|
|
|
+ tcRoiCell{iRoi} = cell2mat(cellfun(@(x) squeeze(x(:, iRoi, :)), tc, 'uni', 0)');
|
|
|
+end
|
|
|
+
|
|
|
+if 0
|
|
|
+% Try to reshape the tc into one tc array for each roi. is equivalent but
|
|
|
+% maybe less clear. First concatenate along stim dimension to get an array
|
|
|
+% of dims 1 x nRois x (nSFreqs x nTFreqs), squeeze it to get an array of
|
|
|
+% dims nRois x (nSFreqs x nTFreqs), and reshape it into an array of dims
|
|
|
+% nRois x nSFreqs x nTFreqs. Now we slice it along the first dimension into
|
|
|
+% a cell array of nRois x 1, each cell being nSFreqs x nTFreqs.
|
|
|
+tcRoiCell = mat2cell(reshape(squeeze(cat(3, tc{:})), sizeTC(2), sizeTC(3), []), ...
|
|
|
+ ones(1, sizeTC(2)), stimSizeTC(1), stimSizeTC(2));
|
|
|
+end
|
|
|
+
|
|
|
+end
|
