fixed Freq and NonStat Analysis for updated findMRCs, Igor outputs for preindent and attenuation scripts, fixed findMRCs output columns, simplified roundVel

Author: sammykatta

Analysis/SK/AnnotatedScripts/AntVsPost_VoltageAttenuation_180803.m

@@ -56,13 +56,17 @@
 % sweeps where the recording was lost partway through or some unexpected
 % source of noise was clearly at play.
 
-protList ={'WC_Probe';'NoPre'};
-matchType = 'first';
+protList ={'WC_Probe';'WC_ProbeSmall';'WC_ProbeLarge';'NoPrePulse'};
+matchType = 'full';
 
 ExcludeSweeps(ephysData, protList, anteriorDistCells, 'matchType', matchType);
 ExcludeSweeps(ephysData, protList, posteriorDistCells, 'matchType', matchType);
 
-%% Find MRCs
+%% Find MRCs 
+% antAllSteps.xlsx and postAllSteps.xlsx
+protList ={'WC_Probe';'NoPre'};
+matchType = 'first';
+
 sortSweeps = {'magnitude','magnitude','magnitude','magnitude'};
 
 anteriorMRCs = IdAnalysis(ephysData,protList,anteriorDistCells,'num','matchType',matchType, ...
@@ -87,7 +91,7 @@
 % the recording name against the voltage attenuation table to find the
 % attenuation factor for that recording.
 whichMRCs = anteriorMRCs;
-thisAtt = attenuationData(:,[2 10]);
+thisAtt = attenuationData(:,[2 8 10]);
 distVPeak = [];
 stepSize = 10;
 
@@ -98,8 +102,8 @@
         thisName = whichMRCs{iCell,1};
         hasAtt = strcmp(thisName,thisAtt(:,1));
 
-        if any(hasAtt)
-            distVPeak(iCell,:) = [thisCell(whichStep,[11 6]) thisAtt{hasAtt,2}];
+        if any(hasAtt) && thisAtt{hasAtt,2}
+            distVPeak(iCell,:) = [thisCell(whichStep,[11 6]) thisAtt{hasAtt,3}];
         else
             distVPeak(iCell,:) = [thisCell(whichStep,[11 6]) nan];
         end
@@ -166,12 +170,101 @@
 figure(); axh(1) = subplot(2,1,1);
 scatter(distVPeak_Ant(:,1),distVPeak_Ant(:,4));
 xlabel('Distance from cell body (um)');
-ylabel('Current @10um (pA)')
+ylabel(sprintf('Current @%dum (pA)',stepSize))
 text(100,200,'Anterior');
 axh(2) = subplot(2,1,2);
 scatter(distVPeak_Post(:,1),distVPeak_Post(:,2));
 xlabel('Distance from cell body (um)');
-ylabel('Current @10um (pA)')
+ylabel(sprintf('Current @%dum (pA)',stepSize))
 text(100,200,'Posterior');
 
-xlim(axh,[0 200]); ylim(axh,[0 200]);
+for i = 1:length(axh)
+    xlim(axh(i),[0 200]);
+    ylim(axh(i),[0 200]);
+end
+
+%% Plot on one plot
+
+figure();
+scatter(distVPeak_Ant(:,1),distVPeak_Ant(:,4));
+hold on;
+scatter(-distVPeak_Post(:,1),distVPeak_Post(:,2));
+xlabel('Distance from cell body (um)');
+ylabel(sprintf('Current @%dum (pA)',stepSize))
+xlim([-100 200]);ylim([0 200]);
+vline(0,'k:');
+
+%% Correct all sizes and export for Igor fitting of Boltzmann to each recording
+
+Vc = -0.06; %in V
+Ena = 0.094; % in V
+
+whichMRCs = anteriorMRCs;
+thisAtt = attenuationData(:,[2 8 10]);
+thisName = cell(0);
+ant_Out = cell(length(eachSize)+1,size(whichMRCs,1));
+
+for iCell = 1:size(whichMRCs,1)
+    thisCell = whichMRCs{iCell,3};
+    thisName{iCell,1} = whichMRCs{iCell,1}; %name
+    thisName{iCell,2} = mean(thisCell(:,11)); %distance
+    ant_Out{1,iCell} = thisName{iCell,1};
+    hasAtt = strcmp(thisName{iCell,1},thisAtt(:,1));
+    Iact = [];
+    
+    for iSize = 1:length(eachSize)
+        stepSize = eachSize(iSize);
+        whichStep = round(thisCell(:,1)*2)/2 == stepSize; %round to nearest 0.5
+        if any(whichStep)
+            
+            if any(hasAtt) && thisAtt{hasAtt,2} %if attenuation calc exists and not omitCell
+                Vm = Vc * thisAtt{hasAtt,3};
+                Im = thisCell(whichStep,6);
+                Iact(iSize,1) = (Im * (Vc-Ena)) ./ (Vm-Ena);
+            else
+                Iact(iSize,1) = nan;
+            end
+            
+        else
+            Iact(iSize,1) = nan;
+        end
+    end
+    ant_Out(2:length(eachSize)+1,iCell) = num2cell(Iact);
+
+end
+ant_Name = [{'Anterior', 'Ant_Dist'}; thisName];
+
+thisName = cell(0);
+whichMRCs = posteriorMRCs;
+post_Out = cell(length(eachSize)+1,size(whichMRCs,1));
+
+for iCell = 1:size(whichMRCs,1)
+    thisCell = whichMRCs{iCell,3};
+    thisName{iCell,1} = whichMRCs{iCell,1}; %name
+    thisName{iCell,2} = mean(thisCell(:,11)); %distance
+    post_Out{1,iCell} = thisName{iCell,1};
+    Iact = [];
+    
+    for iSize = 1:length(eachSize)
+        stepSize = eachSize(iSize);
+        whichStep = round(thisCell(:,1)*2)/2 == stepSize; %round to nearest 0.5
+        if any(whichStep)
+            Iact(iSize,1) = thisCell(whichStep,6);
+        else
+            Iact(iSize,1) = nan;
+        end
+    end
+    post_Out(2:length(eachSize)+1,iCell) = num2cell(Iact);
+
+end
+
+ant_Out = [[{'stepSize'};num2cell(eachSize)] ant_Out];
+post_Name = [{'Posterior', 'Post_Dist'}; thisName];
+
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',ant_Out,'ant');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',post_Out,'post');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',ant_Name,'antStats');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',post_Name,'postStats');
+
+
+clear iCell thisCell Iact whichMRCs whichStep hasAtt thisAtt Vc Ena thisName

Analysis/SK/ExcludeSweeps.m

@@ -210,6 +210,8 @@
     end
 end
 
+keepRows = cellfun(@(x) ~isempty(x), selectedSweeps(:,3));
+selectedSweeps = selectedSweeps(keepRows,:);
 
 % Ask where to save and write out the .xls file)
 [filename, pathname] = uiputfile(...

Analysis/SK/Scripts-in-progress/AttCorrectedID_Curve.m

@@ -0,0 +1,71 @@
+% The point of this function is to grab the data from Igor or from an Excel
+% sheet after fitting individual I-d curves to get max/delta/xhalf from the
+% Boltzmann fit, then normalize each I-d curve to its predicted max, and
+% export back into an Igor-readable format for plotting.
+%% Import fits from Igor table
+
+fitStats = ImportMetaData(); %AttCorrectedI-D_fitstats.xlsx
+fitHeaders = fitStats(1,:);
+fitStats = fitStats(2:end,:);
+
+%% Get names and match up stats
+antStats = fitStats(:,cell2mat(cellfun(@(x) ~isempty(regexp(x,'Ant')), fitHeaders,'un',0)));
+postStats = fitStats(:,cell2mat(cellfun(@(x) ~isempty(regexp(x,'Post')), fitHeaders,'un',0)));
+antStats = antStats(cellfun(@(x) ~isnumeric(x), antStats(:,1)),:);
+postStats = postStats(cellfun(@(x) ~isnumeric(x), postStats(:,1)),:);
+
+% this gives five columns: name, delta, distance, max, xhalf
+%MAKE SURE these columns are the same ones in the same order
+
+% straight up taking the fits and recoloring them here won't work because 
+% Igor fits are all 200 points long but the X wave
+% is calculated, and is different length for each (i.e., some curves have
+% X values from 0.5 to 11, some from 3 to 10, etc., and those ranges
+% determine what the x values are for each fit separately).
+
+% instead, I'm just going to do the normalization here and send the waves
+% back to Igor, where I can average them like the other I-dCellFits
+
+%%
+eachSize = fitStats(:,cell2mat(cellfun(@(x) ~isempty(regexp(x,'stepSize')), fitHeaders,'un',0)));
+eachSize = eachSize(cellfun(@(x) isnumeric(x) && ~isnan(x),eachSize));
+
+whichSide = antStats;
+thisCell = whichSide(:,1);
+thisDataNorm = cell(0);
+
+for i = 1:size(whichSide,1)
+   thisMax = whichSide{i,4};
+   whichData = cell2mat(cellfun(@(x) ~isempty(regexp(x,sprintf('^(?!fit).*%s',thisCell{i}))),fitHeaders,'un',0));
+   thisData = fitStats(1:length(eachSize),whichData);
+   thisData(cellfun(@isempty,thisData))={nan};
+   thisDataNorm(:,i) = cellfun(@(x) x./thisMax, thisData,'un',0);
+end
+
+antNorm = thisDataNorm;
+antHeaders = cellfun(@(x) sprintf('%s_Norm',x),thisCell,'un',0)';
+
+
+% POSTERIOR
+whichSide = postStats;
+thisCell = whichSide(:,1);
+thisDataNorm = cell(0);
+
+for i = 1:size(whichSide,1)
+   thisMax = whichSide{i,4};
+   whichData = cell2mat(cellfun(@(x) ~isempty(regexp(x,sprintf('^(?!fit).*%s',thisCell{i}))),fitHeaders,'un',0));
+   thisData = fitStats(1:length(eachSize),whichData);
+   thisData(cellfun(@isempty,thisData))={nan};
+   thisDataNorm(:,i) = cellfun(@(x) x./thisMax, thisData,'un',0);
+end
+
+postNorm = thisDataNorm;
+postHeaders = cellfun(@(x) sprintf('%s_Norm',x),thisCell,'un',0)';
+
+%%
+
+
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',antHeaders,'antNorm');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',postHeaders,'postNorm');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',antNorm,'antNorm','A2');
+xlswrite('PatchData/attCorrectedIdCurves(180819).xls',postNorm,'postNorm','A2');

Analysis/SK/Scripts-in-progress/FrequencyAnalysis.m

@@ -17,6 +17,7 @@
 p.addParameter('tauType','thalfmax', @(x) ischar(x) && ismember(x,{'fit' 'thalfmax'}));
 p.addParameter('integrateCurrent',1);
 p.addParameter('normToOn1',0);
+p.addParameter('matchPhase',0');
 
 p.parse(ephysData, ephysMetaData, protList, varargin{:});
 
@@ -25,6 +26,7 @@
 tauType = p.Results.tauType;
 integrateFlag = p.Results.integrateCurrent;
 normalizeFlag = p.Results.normToOn1;
+phaseFlag = p.Results.matchPhase;
 
 % Load and format Excel file with lists (col1 = cell name, col2 = series number,
 % col 3 = comma separated list of good traces for analysis)
@@ -180,7 +182,7 @@
         % between sweeps
 
         % Concatenate data for all series in this recording
-        allLeakSub{iSeries,1} = leakSubtractCell;
+        allLeakSub(iSeries,1) = leakSubtractCell;
         allSweeps{iSeries,1} = theseSines;
         allSweeps{iSeries,2} = theseSquares;
     end
@@ -209,8 +211,8 @@
 
     allSineSum = vertcat(allSweeps{:,1});
     allSquareSum = vertcat(allSweeps{:,2});
-    for iStim = 1:max(allSquareSum(:,7))
-        stimSquareSum(:,:,iStim) = allSquareSum(allSquareSum(:,7)==iStim,:);
+    for iStim = 1:max(allSquareSum(:,12))
+        stimSquareSum(:,:,iStim) = allSquareSum(allSquareSum(:,12)==iStim,:);
     end
 
     % Find how many unique timepoints/durations there are, within a given
@@ -290,12 +292,14 @@
 
     %     meansByStimProfile = NaN(nStimProfiles, length(sortedLeakSub));
     steadyMeans = [];
+    squareMeans = [];
     groupIdx = cell(0);
+    theseSweeps = cell(0);
 
     for iProfile = 1:nStimProfiles
         groupIdx{iProfile} = profileStartIdx(iProfile):profileEndIdx(iProfile);
 
-        %             theseSweeps = sortedLeakSub(groupIdx{iProfile},:);
+        theseSweeps{iProfile} = sortedLeakSub(groupIdx{iProfile},:);
 
         %             if length(groupIdx{iProfile})>1
         %                 meansByStimProfile(iProfile,:) = nanmean(theseSweeps,1);
@@ -322,7 +326,7 @@
         nSqStim = size(sortedSquares,3);
 
         for iSqStim = 1:nSqStim
-            squareMeans(iProfile,iSqStim) = mean(sortedSquares(groupIdx{iProfile},3,iSqStim));
+            squareMeans(iProfile,iSqStim) = mean(sortedSquares(groupIdx{iProfile},6,iSqStim));
         end             
 
         onRatio = sortedSquares(:,:,3)./sortedSquares(:,:,1);
@@ -337,7 +341,7 @@
     end
 
     sinePeaks{iCell,1} = cellName;
-    sinePeaks{iCell,2} = sortedLeakSub;
+    sinePeaks{iCell,2} = theseSweeps';
     sinePeaks{iCell,3} = [stimMetaData steadyMeans squareMeans];        
 
 end

Analysis/SK/Scripts-in-progress/NonStatNoiseAnalysis.m

@@ -185,6 +185,9 @@
         for iStim = 1:nStims
 
             stimLoc = uniquetol(allStim{iStim}(:,1:2),lengthTol,'ByRows',true);
+            stimSize = round(mean(allStim{iStim}(:,3)));
+            stimPos = round(mean(allStim{iStim}(:,4)));
+            stimVel = roundVel(mean(allStim{iStim}(:,5)));
             responseTime = [stimLoc(1)-preTime*sf:stimLoc(2)+postTime*sf];
             %NEXT: use seriesStimuli location/sweep number to set time
             %boundaries for where to look at the response
@@ -225,6 +228,9 @@
             % Save everything to output struct
             nonStatOutput.(cellName)(whichRow).protocol = protName;
             nonStatOutput.(cellName)(whichRow).stimNum = iStim;
+            nonStatOutput.(cellName)(whichRow).size = stimSize;
+            nonStatOutput.(cellName)(whichRow).position = stimPos;
+            nonStatOutput.(cellName)(whichRow).velocity = stimVel;
             nonStatOutput.(cellName)(whichRow).slidingMean = windowMeans;
             nonStatOutput.(cellName)(whichRow).slidingVar = windowVars;
             nonStatOutput.(cellName)(whichRow).sweepsPerWindow = averagingWindow;