/
run_SMCTFCS_analysisV1.m
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run_SMCTFCS_analysisV1.m
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%% run_SMCTFS_analysisV1
%This script analyses SMCT-FCS experimental data as in Marklund et al,
%Nature, 2020. The script starts with rawdata from the SMCT-FCS microscope,
%sort the datastreams, build trajectories, calculates auotcorelation
%functions for the individual trajectories.
%Then the script selects the sliding trajectores, pools the data for
%different expermental days and, plots the mean of the autocorrelation
%functions for individual trajectoreries. After calculating diffusion constanst for sliding,
%the script also fits a pitch dependent autocorerelation model
%to the data, which gives an estimate of the pitch for rotation coupled
%sliding
%
%Raw data is availible at elflab.icm.uu.se
%
%2020-04-05 Emil v1: First version of the streamlined pipeline for full
%analysis. The script asumes that you have created a folder 'saved_files'
%in the same folder as you have the git repository, as well that you have raw data in a
%folder 'raw_data_SMCT_FCS', also in the same folder as were you have the
%git repository. You should thus have three folders, 'saved_files', 'raw_data_SMCT_FCS'
%and 'SMCT_FCS' from github in the same folder, which is one level above
%this script in the folder structure.
%On a single core the full analysis will take some hours to run
%(less than 24 hours)
%% Add paths
addpath(genpath('Functions/FunctionsConfocal'))
addpath(genpath('Functions/Polarization modeling'));
%Path to where you have the raw data. Raw data is availible at
%elflab.icm.uu.se
rawDataPath='../raw_data_SMCT_FCS';
s=what(rawDataPath);
rawDataPathAbs=s.path;
%path to were you are saving files. This folder must exist to be able to
%run the script.
savePath='../saved_files';
s=what(savePath);
savePathAbs=s.path;
%% Define folder names with data
%The names of the scripts in the analyseRawDataScripts folder, that analyses raw data for
%individual experiment days, should be on the form ['analyseRawData_'
%rawDataSets{i}]
%LacI-MBP-R
rawDataSets{1}='170705_LacIdim2MBP';
rawDataSets{2}='170713_LacIdim2MBP';
rawDataSets{3}='170718_LacIdim2MBP';
rawDataSets{4}='181010_LacIdim2MBP';
rawDataSets{5}='181021_LacIdim2MBP';
rawDataSets{6}='181105_LacIdim2MBP';
%LacI-R
rawDataSets{7}='170607_LacIdim2';
rawDataSets{8}='170704_LacIdim2';
rawDataSets{9}='180704_LacIdim2';
rawDataSets{10}='181105_LacIdim2';
%% Analyse rawdata
%Sort datastreams, build trajectories and calculate autocorrelation functions
for thisind=1:numel(rawDataSets)
currDataPath=[rawDataPathAbs '/' rawDataSets{thisind}];
run(['analyseRawDataScripts/analyseRawData_' rawDataSets{thisind}]);
save([savePathAbs '/rawDataAnalysed_' rawDataSets{thisind}])
clear Trajectories;
clear Trajobject;
clear Scanobject;
clear Scanobjects;
end
close all;
%% %%
% Paths to analysed partially analysed data
pathAutoCorr=cell(1,numel(rawDataSets));
for thisind=1:numel(rawDataSets)
pathAutoCorr{thisind}=[savePathAbs '/rawDataAnalysed_' rawDataSets{thisind}];
end
%Vector with indeces pointing to what rawdata path to use. Sliding and stationary molecules
%are picked out from the same analysed rawdatapaths
setToExps=[1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10];
numexperiments=numel(setToExps);
numSets=numel(pathAutoCorr);
%'Pos' = pick out sliding molecules, 'Stuck' = pick out stationary
%molecules
control={'Pos','Pos','Pos','Pos','Pos','Pos','Pos','Pos','Pos','Pos','Stuck','Stuck','Stuck','Stuck','Stuck','Stuck','Stuck','Stuck','Stuck','Stuck'};
normNumber=2; %Number of timelags to use in the normalization of the ACF.
integrateStart=1;
thelag=5;
stdCutOff=0.6;%Throw away molecule ACFs with a std larger than this. Effectively removes noisy and uninformative ACFs
stdCountCutOff=inf;%
maxMeanCount=inf;
minMeanCount=45;%Minimum mean count of the traces to be used for aucorrelation analysis. Also removes noisy autocorrelation function that would bias the mean otherwise.
minTotCounts=0;
%Cell structuring determining how data is pooled
%[1 2 3 4 5 6] is LacI-MBP-R sliding
%[7 8 9 10] is LacI-R sliding
%[11 12 13 14 15 16] is LacI-MBP-R stationary
%[17 18 19 20] is LacI-R stationary
combineExperiments={[1 2 3 4 5 6],[7 8 9 10],[11 12 13 14 15 16],[17 18 19 20]};
numCombines=numel(combineExperiments);
endInd=22;
timeWind=7;%Number of time lags in the autocorrelation function
shiftperiod=500e-6;%Laser on time
shifttime=166e-6;%laser off time. Dark time of the laser has been removed from autocorrelation function (first happens after 500 us)
%% Remove trajectories that are bleaching in more than one step
%manually picked out for the example dataset. here you load the indexes for those trajectories. If you change classification of
%sliding below, you cannot load those indexes! In that case you again have to pick out
%bleachers first! You can also set classparams.manualBleachRemoval to 0 and
%skip this step
classparams.manualBleachRemoval=1;
notBleaching=cell(1,numel(setToExps));
numNotBleaching=zeros(1,numel(setToExps));
numTotB=zeros(1,numel(setToExps));
if classparams.manualBleachRemoval==1
load('notBleachingTrajs.mat','notBleaching');
end
%% Classification of sliding
clear diff;
load(pathAutoCorr{2},'Trajobject');
deltats=diff(Trajobject.TrajPosEstimation.FixWindowAv{1}(:,1));
deltat=deltats(1)*1000;%time step in seconds times 1000 to get in milliseconds
% Parameters for the sldiing classification. Find particles diffusing along
% the DNA axis
classparams.dE=deltat; %exposure time
classparams.nDeltaT=1;%steppeing size in deltat. if = 1 all time positions are used, if 2 everyother is skipped
classparams.deltat=classparams.nDeltaT*classparams.dE;
classparams.scale=1000; %nm/pixel, one pixel is 1 um
classparams.varwindow=10;%10
classparams.minPrincRelVar=0.75;%0.8
classparams.minPrincAbsVar=0.0015;%1.5*0.05*deltat/200;%2
classparams.maxPrincAbsVar=inf;
classparams.minNotStuck=0.02*deltat/200;%0.5 for PCAver9
classparams.minMinWindowVar=0.00008;
classparams.maxMaxWindowVar=0.2*(deltat/200)*(classparams.varwindow/5);%
classparams.maxMinWindowVar=0.00035;
classparams.maxMeanDiff=inf;
% error estimation ffrom gain
classparams.minMeanDiff=-inf;
classparams.maxAbsVar=7e-4;
classparams.angledomain=[-pi/4 pi/4+pi/2];
minTrajTime=0.55;
classparams.startTime=0.2;
classparams.minTrajLength=round(minTrajTime*1000/deltat); %
classparams.msdStartPoint=1;
classparams.msdEndPoint=2;
classparams.minAngle=0-0.35;
classparams.maxAngle=0+0.35;
classparams.maxDiffusionsPrinc1=0.07;
classparams.minDiffusionsPrinc1=-0.005;
classparams.newDeltat=16;
diffMaxVec=0.075*ones(1,numel(setToExps));
diffMinVec=-inf*ones(1,numel(setToExps));
%diffMaxVec(combineExperiments{1})=0.02;
%diffMinVec(combineExperiments{1})=-0.005;
%diffMaxVec(combineExperiments{2})=0.07;
%diffMinVec(combineExperiments{2})=0.02;
%slidingcontrol='Neg';%IF Neg, slidingInd is set to alal trajectoreis outside of minAngle of maxAngle, that is the negative control is analysed for aoutocorelation; If stuck, pick out stuck trajectories
DataSelectionRules.Trajectory.Mode='FixWindowAv';%Use: FixWindowAv, RollingAv or DirectEstimation3Points
%sliding=cell(1,numexperiments);
%sliding=cell(1,numSets);
parfor theSet=1:numel(setToExps)
classparamscurrent=classparams;
classparamscurrent.maxDiffusionsPrinc1=diffMaxVec(theSet);
classparamscurrent.minDiffusionsPrinc1=diffMinVec(theSet);
disp(['sliding classidication for set ' num2str(theSet)])
TrajobjectPar=load(pathAutoCorr{setToExps(theSet)},'Trajobject');
[slidingInd,notstuck,angles,rellat,trajPrinclatent,thescores,okLength,trajL,classifiedParams] = slidingClassifyPCACameraTemplatever12_11(TrajobjectPar.Trajobject,DataSelectionRules,classparamscurrent,1);
allInds=1:numel(notstuck);
classparams2=classparamscurrent;
classparams2.maxAngle=+inf;
classparams2.minAngle=-inf;
[slidingIndalldir,~,~,~,~,~] = slidingClassifyPCACameraTemplatever12_11(TrajobjectPar.Trajobject,DataSelectionRules,classparams2,1);
if strcmp(control{theSet},'Neg')
slidingInd=setdiff(slidingIndalldir,slidingInd);
end
if strcmp(control{theSet},'Stuck')
slidingInd=allInds(~notstuck);
% slidingInd=slidingInd(trajL(slidingInd)>classparams.minTrajLength);
end
disp(['For set ' num2str(theSet) ', Number of trajectories longer than treshhold: ' num2str(okLength)]);
disp(['For set ' num2str(theSet) ', Number of trajectories passing classification: ' num2str(numel(slidingInd))]);
% if ~strcmp(trajIndexVarName{theSet},'slidingInd')
% obj=MMload(pathTrajIndex{theSet},trajIndexVarName{theSet});
% slidingInd=obj.(trajIndexVarName{theSet});
%
% end
%% Remove trajectories that bleaches in in two steps or more, here done manually, remove this section if sliding classification params are changed!!
slidingIndPreBleachRem=slidingInd;
if classparams.manualBleachRemoval
if ~isempty(notBleaching{theSet})
numTotB(theSet)=numel(slidingInd);
slidingInd=slidingInd(notBleaching{theSet});
numNotBleaching=numel(slidingInd);
end
end
%%
slidingClassified=classifiedParams(slidingInd,:);
%pick out sliding trajectories to its own variable
slidingPixelScores=cell(1,numel(slidingInd));
slidingTrajs=cell(1,numel(slidingInd));
startInd=ceil(classparams.startTime/(deltat*0.001));
trajTime=zeros(1,numel(slidingInd));
for i=1:numel(slidingInd)
slidingPixelScores{i}=thescores{slidingInd(i)}(1:classparams.nDeltaT:end,:);
slidingTrajs{i}=TrajobjectPar.Trajobject.TrajPosEstimation.FixWindowAv{slidingInd(i)}(startInd:classparams.nDeltaT:end,2:3);
trajTime(i)=TrajobjectPar.Trajobject.Traj(slidingInd(i),1); %Total time in s
end
%scale=1000; %tthescore and slidingPixelsScors is in units of um, so 1 pixel is 1 um, scale is in of nm/pixel, so scale=1000
Dimension=2;
[avgTotDiff,compTotDiff]=getTotalDiffCVE(slidingPixelScores,classparams.deltat,classparams.scale,Dimension);
[avgDiff,compDiff]=getDiffCVECamera(slidingPixelScores,classparams.deltat,classparams.scale,Dimension);
minUseLength=round((minTrajTime-classparams.startTime)*1000/deltat)-1;
[msd,compDiffMSD]=getDiffMSDCamera_fixdiv2_ver2(slidingPixelScores,classparams.deltat,classparams.scale,Dimension,classparams.msdStartPoint,classparams.msdEndPoint);
Dxs=compDiff(1,:);
Dys=compDiff(2,:);
DxsMSD=compDiffMSD(1,:);
DysMSD=compDiffMSD(2,:);
varPrinc=trajPrinclatent(1,slidingInd);
%
disp(['For set ' num2str(theSet) ', Number of trajectories slidng in all directions: ' num2str(numel(slidingIndalldir))]);
angleTot=classparams.maxAngle-classparams.minAngle;
falsePos=((numel(slidingIndalldir)-numel(slidingIndPreBleachRem))/(pi-angleTot))/(numel(slidingIndPreBleachRem)/angleTot);
disp(['For set ' num2str(theSet) 'Estimated fraction false positive sliders :' num2str(falsePos)]);
[diffPrinc1,diffPrinc2,allDiffPrinc1,allDiffPric2,meanDiffPrinc1,meanDIffPrinc2]=calcTrajStepdiff(thescores,1,1,slidingInd);
sliding{theSet}.numTotTrajs=numel(notstuck);
sliding{theSet}.notsutck=notstuck;
sliding{theSet}.stuckInd=allInds(~notstuck);
sliding{theSet}.numTrajsOkLength=okLength;
sliding{theSet}.slidingInd=slidingInd;
sliding{theSet}.slidingIndalldir=slidingIndalldir;
sliding{theSet}.falsePos=falsePos;
sliding{theSet}.Dxs=Dxs;
sliding{theSet}.Dys=Dys;
sliding{theSet}.DxsMSD=DxsMSD;
sliding{theSet}.DysMSD=DysMSD;
sliding{theSet}.varPrinc=varPrinc;
sliding{theSet}.meanDiffPrinc1=meanDiffPrinc1(slidingInd);
sliding{theSet}.slidingTrajs=slidingTrajs;
sliding{theSet}.slidingPixelScores=slidingPixelScores;
sliding{theSet}.msd=msd;
sliding{theSet}.trajTime=trajTime; %total time in s of slidign trajectories, before cutoff first part of trajecotry
sliding{theSet}.slidingClassified=slidingClassified;
sliding{theSet}.slidingIndPreBleachRem=slidingIndPreBleachRem;
end
%% Calcualte diffusion constants
clear diff;
newDeltat=16;%classparams.newDeltat;
kWind=floor(newDeltat/deltat);
newStartPoint=1;
newEndPoint=10;
slidingAvg=cell(1,30);
%newmaxDiffusionsPrinc1=inf;
for theSet=1:numel(setToExps)
disp(['Calculating diffusion for set ' num2str(theSet)])
slidingPixelScores=sliding{theSet}.slidingPixelScores;
numtrajs=numel(slidingPixelScores);
averagePos=cell(1,numtrajs);
allDNADisplacements=[];
for i=1:numtrajs
if ~isempty(slidingPixelScores{i})
smoothAvg1=smooth(slidingPixelScores{i}(:,1),kWind);
smoothAvg2=smooth(slidingPixelScores{i}(:,2),kWind);
averagePos{i}=[];
averagePos{i}(:,1)=smoothAvg1(ceil(kWind/2):kWind:end);
averagePos{i}(:,2)=smoothAvg2(ceil(kWind/2):kWind:end);
%averagePos{i}(:,1)=smoothAvg1(1:kWind:end);
%averagePos{i}(:,2)=smoothAvg2(1:kWind:end);
displacements=diff(averagePos{i}(:,1));
allDNADisplacements=[allDNADisplacements;displacements];
end
end
Dimension=2;
[avgTotDiff,compTotDiff]=getTotalDiffCVE(averagePos,newDeltat,classparams.scale,Dimension);
[avgDiff,compDiff]=getDiffCVECamera(averagePos,newDeltat,classparams.scale,Dimension);
[msd,compDiffMSD]=getDiffMSDCamera_fixdiv2_ver2(averagePos,newDeltat,classparams.scale,Dimension,newStartPoint,newEndPoint);
Dxs=compDiff(1,:);
Dys=compDiff(2,:);
DxsMSD=compDiffMSD(1,:);
DysMSD=compDiffMSD(2,:);
%skew=skewness(allDNADisplacements);
n=numel(allDNADisplacements);
errorskew=sqrt(6*n*(n-1)/((n-2)*(n+1)*(n+3)));
%disp(['skewness : ' num2str(skew) ' +/- ' num2str(errorskew) ' um']);
slidingAvg{theSet}.Dxs=Dxs;
slidingAvg{theSet}.Dys=Dys;
slidingAvg{theSet}.DxsMSD=DxsMSD;
slidingAvg{theSet}.DxsMSD=DxsMSD;
slidingAvg{theSet}.slidingInd=sliding{theSet}.slidingInd;
slidingAvg{theSet}.compTotDiff=compTotDiff;
slidingAvg{theSet}.averagePos=averagePos;
slidingAvg{theSet}.allDNADisplacements = allDNADisplacements;
end
%% PLot MSD plots for sliders
%figure();hist(compTotDiffboot(1,:));
theStartP=1;
theEndP=3;
averagePoses1=[slidingAvg{1}.averagePos slidingAvg{2}.averagePos slidingAvg{3}.averagePos slidingAvg{4}.averagePos slidingAvg{5}.averagePos slidingAvg{6}.averagePos];
averagePoses2=[slidingAvg{7}.averagePos slidingAvg{8}.averagePos slidingAvg{9}.averagePos slidingAvg{10}.averagePos];
[msd2Tot,compDiffMSD2Tot,msdStd2Tot,msdN2Tot]=getTotDiffMSDCamera_fixdiv2_ver3(averagePoses2,newDeltat,classparams.scale,Dimension,theStartP,theEndP);
[msd1Tot,compDiffMSD1Tot,msdStd1Tot,msdN1Tot]=getTotDiffMSDCamera_fixdiv2_ver3(averagePoses1,newDeltat,classparams.scale,Dimension,theStartP,theEndP);
[msd2,compDiffMSD2]=getDiffMSDCamera_fixdiv2_ver2(averagePoses2,newDeltat,classparams.scale,Dimension,theStartP,theEndP);
[msd1,compDiffMSD1]=getDiffMSDCamera_fixdiv2_ver2(averagePoses1,newDeltat,classparams.scale,Dimension,theStartP,theEndP);
tvals=newDeltat:newDeltat:newDeltat*(theEndP-theStartP+1);
figure();
errorbar(tvals,msd1Tot{1},msdStd1Tot{1}./sqrt(msdN1Tot{1}),'bd')
hold on;
errorbar(tvals,msd2Tot{1},msdStd2Tot{1}./sqrt(msdN2Tot{1}),'rd')
[P1,S1]=polyfit(tvals,msd1Tot{1},1);
plot([tvals(1)*0.5 tvals tvals(end)*1.2],polyval(P1,[tvals(1)*0.5 tvals tvals(end)*1.2]),'b');
[P2,S2]=polyfit(tvals,msd2Tot{1},1);
plot([tvals(1)*0.5 tvals tvals(end)*1.2],polyval(P2,[tvals(1)*0.5 tvals tvals(end)*1.2]),'r');
xlabel('Time (ms)');
ylabel('MSD micronsquared');
%
figure();
errorbar(tvals,mean(msd1{1}),std(msd1{1})./sqrt(numel(msd1{1})),'bd')
hold on;
errorbar(tvals,mean(msd2{1}),std(msd2{1})./sqrt(numel(msd2{1})),'rd')
[P1,S1]=polyfit(tvals,mean(msd1{1}),1);
plot([tvals(1)*0.5 tvals tvals(end)*1.2],polyval(P1,[tvals(1)*0.5 tvals tvals(end)*1.2]),'b');
[P2,S2]=polyfit(tvals,mean(msd2{1}),1);
plot([tvals(1)*0.5 tvals tvals(end)*1.2],polyval(P2,[tvals(1)*0.5 tvals tvals(end)*1.2]),'r');
xlabel('Time (ms)');
ylabel('MSD micronsquared ');
%%
numTimeScales=endInd-timeWind+1;
scaleRes=cell(1,numTimeScales);
timeRegimes=zeros(1,numTimeScales);
combCorrTimesMean=zeros(numel(combineExperiments),numTimeScales);
resCorrTimesMean=zeros(numel(setToExps),numTimeScales);
combCorrTimesStd=zeros(numel(combineExperiments),numTimeScales);
resCorrTimesStd=zeros(numel(setToExps),numTimeScales);
combCorrTimesLogMean=zeros(numel(combineExperiments),numTimeScales);
resCorrTimesLogMean=zeros(numel(setToExps),numTimeScales);
combCorrTimesLogStd=zeros(numel(combineExperiments),numTimeScales);
resCorrTimesLogStd=zeros(numel(setToExps),numTimeScales);
resCorrTimesError=zeros(numel(setToExps),numTimeScales);
combCorrTimesMeanUpper=zeros(numel(combineExperiments),numTimeScales);
combCorrTimesMeanLower=zeros(numel(combineExperiments),numTimeScales);
combCorrTimesStdUpper=zeros(numel(combineExperiments),numTimeScales);
combCorrTimesStdLower=zeros(numel(combineExperiments),numTimeScales);
combCorrTimesError=zeros(numel(combineExperiments),numTimeScales);
corrTimesMatr=cell(1,numel(setToExps));
resCorrTimesAll=cell(1,numel(setToExps));
T=[];
%%
for theExp=cell2mat(combineExperiments)
disp(['Loading data for experiment ' num2str(theExp)]);
Trajobjects{theExp}=load(pathAutoCorr{setToExps(theExp)},'Trajobject');
end
%% Calculate and filter out individiuval trajectory auto correlationfunctions
%Also pool data into the combines structures, to compare all data for each protein
for timescaleInd=4:17%4:numTimeScales
minLagInd=timescaleInd;
%maxLagInd=endInd;
endNormInd=minLagInd+timeWind-1;
maxLagInd=endNormInd;
disp(['Time ' num2str(timescaleInd) ' out of ' num2str(numTimeScales)])
if ~isempty(T)
disp(['Start of tiem interval: ' num2str(T(timescaleInd))]);
end
%%
for theExp=cell2mat(combineExperiments)%[combineExperiments{1} combineExperiments{2} combineExperiments{3} combineExperiments{4} combineExperiments{5} combineExperiments{6} combineExperiments{7} combineExperiments{8} combineExperiments{9} combineExperiments{10} combineExperiments{11}]
disp(['Analyzing experiment ' num2str(theExp)])
Trajobject=Trajobjects{theExp}.Trajobject;
%data=load(pathTrajIndex{theExp},trajIndexVarName{theExp});
%nameIndexVar=fieldnames(data);
trajIndexOfInterest=sliding{theExp}.slidingInd;
numOfInterest=length(trajIndexOfInterest);
RChSum=Trajobject.TTTTRTrajs.Autocorrelation.RChSum;
RChCross=Trajobject.TTTTRTrajs.Autocorrelation.RChCross;
Timechift=Trajobject.TTTTRTrajs.Autocorrelation.Timechift;
T=mean(Timechift(trajIndexOfInterest,:),1);
% Fix times in autocorrelation to correct for the shift
T=T+floor(T/shiftperiod)*shifttime;
maxLagTime=T(maxLagInd);
plotT=0:1e-7:maxLagTime;
% minLagInd=find(T>minLagTime,1);
% maxLagInd=find(T>maxLagTime,1)-1;
% endNormInd=find(T>endNormLagTime,1)-1;
CorrSum=sum(RChSum(trajIndexOfInterest,:));
autoCorrValsAllRaw=RChSum(trajIndexOfInterest,minLagInd:maxLagInd);
autoCrossCorrValsAllRaw=RChCross(trajIndexOfInterest,:);
autoCorrValsAll=autoCorrValsAllRaw;
initialAmplitude=autoCorrValsAll(:,1);
autoCorrValsAll=autoCorrValsAll-autoCorrValsAll(:,maxLagInd-minLagInd+1)*ones(1,numel(minLagInd:maxLagInd));
autoCorrValsAll=autoCorrValsAll./abs(mean(autoCorrValsAll(:,1:normNumber),2)*ones(1,numel(minLagInd:maxLagInd)));
newNum=numel(autoCorrValsAllRaw(1,thelag+1:end));
endPoints=autoCorrValsAllRaw(:,end)*ones(1,newNum);
midPoints=autoCorrValsAllRaw(:,thelag+1:end)-endPoints;
startPoints=autoCorrValsAllRaw(:,1:end-thelag)-endPoints;
thediffs=midPoints-startPoints;
autoCorrValsAllQ=midPoints./startPoints;
autoCorrValsAllQ(midPoints<0)=0;
autoCorrValsAllQ(midPoints>startPoints)=1;
autoCorrValsAllQ(startPoints<0)=NaN;
autoCorrStd=std(autoCorrValsAll(:,:),0,2);
totCountsAll=sum(Trajobject.Traj(trajIndexOfInterest,6:7),2);
meanPolAll=Trajobject.Traj(trajIndexOfInterest,4);
countsAll=cell(1,numOfInterest);
meanCountsAll=zeros(numOfInterest,1);
stdCountsAll=zeros(numOfInterest,1);
k=1;
for theIndex=trajIndexOfInterest
countsAll{k}=sum(Trajobject.TrajPosEstimation.FixWindowAv{theIndex}(:,6:7),2);
stdCountsAll(k)=std(countsAll{k});
meanCountsAll(k)=mean(countsAll{k});
k=k+1;
end
okAutoCorrs=autoCorrStd<stdCutOff;
okAutoCorrs=(okAutoCorrs+((stdCountsAll./meanCountsAll)<stdCountCutOff))>1;
okAutoCorrs=(okAutoCorrs+(autoCorrValsAll(:,1)>0))>1;
okAutoCorrs=(okAutoCorrs+(totCountsAll>minTotCounts))>1;
okAutoCorrs=(okAutoCorrs+(meanCountsAll<maxMeanCount))>1;
okAutoCorrs=(okAutoCorrs+(meanCountsAll>minMeanCount))>1;
%okAutoCorrs=(okAutoCorrs+(autoCrossCorrValsAllRaw(:,1)==0))>1;
numCrossSingle=sum(autoCrossCorrValsAllRaw(:,1)==0);
% okAutoCorrs=results{theExp}.okAutoCorrs;
%okAutoCorrs=chooseInd.results{theExp}.okAutoCorrs;
slidingIndok=trajIndexOfInterest(okAutoCorrs);
numOkAutoCorrs=sum(okAutoCorrs);
autoCorrValsAllok=autoCorrValsAll(okAutoCorrs,:);
autoCorrValsAllQok=autoCorrValsAllQ(okAutoCorrs,:);
autoCorrValsAllRawok=autoCorrValsAllRaw(okAutoCorrs,:);
autoCorrVals=mean(autoCorrValsAllok,1);
lagTimes=T(minLagInd:maxLagInd);
newEndNormInd=endNormInd-minLagInd+1;
corrTimesAll=cumtrapz(lagTimes(integrateStart:newEndNormInd),autoCorrValsAll(:,integrateStart:newEndNormInd)');
corrTimesAllok=cumtrapz(lagTimes(integrateStart:newEndNormInd),autoCorrValsAllok(:,integrateStart:newEndNormInd)');
results{theExp}.initialAmplitude=initialAmplitude;
results{theExp}.lagTimes=lagTimes;
results{theExp}.autoCorrValsAll=autoCorrValsAll;
results{theExp}.okAutoCorrs=okAutoCorrs;
results{theExp}.autoCorrValsAllok = autoCorrValsAllok;
results{theExp}.corrTimesAllok=corrTimesAllok;
results{theExp}.corrTimesAll=corrTimesAll;
results{theExp}.totCountsAll=totCountsAll;
results{theExp}.meanPolAll=meanPolAll;
results{theExp}.slidingIndok=slidingIndok;
results{theExp}.autoCorrStd=autoCorrStd;
results{theExp}.autoCorrValsAllQ=autoCorrValsAllQ;
results{theExp}.autoCorrValsAllQok=autoCorrValsAllQok;
results{theExp}.autoCorrValsAllRaw= autoCorrValsAllRaw;
results{theExp}.countsAll=countsAll;
results{theExp}.stdCountsAll=stdCountsAll;
results{theExp}.meanCountsAll=meanCountsAll;
results{theExp}.numCrossSingle=numCrossSingle;
end
%% Combine/ pool data from same type of experiment together
%combines=cell(1,numCombines);
%cbootStrap=cell(1,numCombines);
for theComb=[1:numel(combineExperiments)]
disp(['Pooling data for combination number ' num2str(theComb)])
% cols=numel(results{combineExperiments{theComb}(1)}.autoCorrVals);
% autoCorrNormVals=zeros(1,cols);
amplitudes=ones(1,numel(combineExperiments{theComb}));
corrTimesAll=[];
corrTimesAllok=[];
varPrincsok=[];
totCountsAllok=[];
totCountsAll=[];
Dxs=[];
Dxsok=[];
DxsMSDok=[];
DxsMSD=[];
meanPolAllok=[];
meanPolAll=[];
autoCorrValsAllok=[];
meanDiffPrinc1=[];
autoCorrStd=[];
autoCorrValsAllQ=[];
autoCorrValsAllQok=[];
autoCorrValsAllRaw=[];
for theSet=1:numel(combineExperiments{theComb})
theExp=combineExperiments{theComb}(theSet);
%amplitudes(theSet)=results{theExp}.initialAmplitude*(results{theExp}.fitNorm-results{theExp}.endNorm);
corrTimesAllok=[corrTimesAllok results{theExp}.corrTimesAllok];
corrTimesAll=[corrTimesAll results{theExp}.corrTimesAll];
varPrincsok=[varPrincsok sliding{theExp}.varPrinc(results{theExp}.okAutoCorrs)];
totCountsAllok=[totCountsAllok;results{theExp}.totCountsAll(results{theExp}.okAutoCorrs)];
totCountsAll=[totCountsAll;results{theExp}.totCountsAll];
Dxs=[Dxs sliding{theExp}.DxsMSD];
Dxsok=[Dxsok sliding{theExp}.DxsMSD(results{theExp}.okAutoCorrs)];
DxsMSDok=[DxsMSDok sliding{theExp}.DxsMSD(results{theExp}.okAutoCorrs)];
DxsMSD=[DxsMSD sliding{theExp}.DxsMSD];
meanPolAllok=[meanPolAllok;results{theExp}.meanPolAll(results{theExp}.okAutoCorrs)];
meanPolAll=[meanPolAll;results{theExp}.meanPolAll];
autoCorrValsAllok=[autoCorrValsAllok;results{theExp}.autoCorrValsAllok];
meanDiffPrinc1=[meanDiffPrinc1 sliding{theExp}.meanDiffPrinc1];
autoCorrStd=[autoCorrStd;results{theExp}.autoCorrStd];
autoCorrValsAllQ=[autoCorrValsAllQ;results{theExp}.autoCorrValsAllQ];
autoCorrValsAllQok=[autoCorrValsAllQok;results{theExp}.autoCorrValsAllQok];
autoCorrValsAllRaw=[autoCorrValsAllRaw;results{theExp}.autoCorrValsAllRaw];
end
medianDxsMSDok=median(DxsMSDok);
combines{theComb}.corrTimesAll= corrTimesAll;
combines{theComb}.varPrincsok =varPrincsok;
combines{theComb}.Dxsok=Dxsok;
combines{theComb}.DxsMSDok=DxsMSDok;
combines{theComb}.DxsMSD=DxsMSD;
combines{theComb}.meanPolAllok=meanPolAllok;
combines{theComb}.meanPolAll=meanPolAll;
combines{theComb}.autoCorrValsAllok=autoCorrValsAllok;
combines{theComb}.medianDxsMSDok=medianDxsMSDok;
combines{theComb}.corrTimesAllok=corrTimesAllok;
combines{theComb}.meanDiffPrinc1=meanDiffPrinc1;
combines{theComb}.autoCorrStd=autoCorrStd;
combines{theComb}.totCountsAll=totCountsAll;
combines{theComb}.totCountsAllok = totCountsAllok;
combines{theComb}.autoCorrValsAllQ=autoCorrValsAllQ;
combines{theComb}.autoCorrValsAllQok=autoCorrValsAllQok;
combines{theComb}.autoCorrValsAllRaw = autoCorrValsAllRaw;
combines{theComb}.Dxs=Dxs;
end
numCombs=numel(combineExperiments);
timeRegimes(timescaleInd)=lagTimes(1);
scaleRes{timescaleInd}.lagTimes=lagTimes;
scaleRes{timescaleInd}.combcorrTimesAllok=cell(1,numCombs);
maxCorrTime(timescaleInd)=lagTimes(timeWind)-lagTimes(1);
for i=1:numCombs
scaleRes{timescaleInd}.combines{i}=combines{i};
combCorrTimesMean(i,timescaleInd)=nanmean(combines{i}.corrTimesAllok(end,:));
combCorrTimesStd(i,timescaleInd)=nanstd(combines{i}.corrTimesAllok(end,:));
combCorrTimesError(i,timescaleInd)= combCorrTimesStd(i,timescaleInd)/sqrt(numel(combines{i}.corrTimesAllok(end,:)));
inds=combines{i}.corrTimesAllok(end,:)>0;
combCorrTimesLogMean(i,timescaleInd)=nanmean(log10(combines{i}.corrTimesAllok(end,inds)));
combCorrTimesLogStd(i,timescaleInd)=nanstd(log10(combines{i}.corrTimesAllok(end,inds)));
numRes=numel(combineExperiments{i});
scaleRes{timescaleInd}.rescorrTimesAllok=cell(1,numRes);
for j=1:numRes
ind=combineExperiments{i}(j);
% corrTimesMatr{ind}(:,timescaleInd)=results{ind}.corrTimesAllok(end,:)';
scaleRes{timescaleInd}.results{ind}=results{ind};
% scaleRes{timescaleInd}.results{ind}.okAutoCorrs=results{ind}.okAutoCorrs;
% scaleRes{timescaleInd}.results{ind}.autoCorrValsAllok = results{ind}.autoCorrValsAllok;
[~,numtrajectories]=size(results{ind}.corrTimesAll);
if isempty(resCorrTimesAll{ind})
resCorrTimesAll{ind}=zeros(numtrajectories,numTimeScales);
end
for theTraj=1:numtrajectories
resCorrTimesAll{ind}(theTraj,timescaleInd)=results{ind}.corrTimesAll(end,theTraj);
end
resCorrTimesMean(ind,timescaleInd)=nanmean(results{ind}.corrTimesAllok(end,:));
resCorrTimesStd(ind,timescaleInd)=nanstd(results{ind}.corrTimesAllok(end,:));
resCorrTimesError(ind,timescaleInd)=resCorrTimesStd(ind,timescaleInd)./(sqrt(numel(results{ind}.corrTimesAllok(end,:))));
inds=results{ind}.corrTimesAllok(end,:)>0;
resCorrTimesLogMean(ind,timescaleInd)=nanmean(log10(results{ind}.corrTimesAllok(end,inds)));
resCorrTimesLogStd(ind,timescaleInd)=nanstd(log10(results{ind}.corrTimesAllok(end,inds)));
end
end
end
clear Trajobjects;
%% Estimate rate/frequency of sliders appearing
clear diff;
for theSet=1:numel(setToExps);
disp(['Event rate calculation for set' num2str(theSet)])
TrajobjectPar=load(pathAutoCorr{setToExps(theSet)},'Trajobject');
thediffs=diff(TrajobjectPar.Trajobject.Traj(:,8));
inds=1:numel(TrajobjectPar.Trajobject.Traj(:,8));
posStartInds=inds([1;thediffs]>0.5);
measTime=0;
for j=1:numel(posStartInds)-1
measTime=measTime+TrajobjectPar.Trajobject.TrajPosEstimation.FixWindowAv{posStartInds(j+1)-1}(end,1)-TrajobjectPar.Trajobject.TrajPosEstimation.FixWindowAv{posStartInds(j)}(1,1);
end
measTime=measTime+TrajobjectPar.Trajobject.TrajPosEstimation.FixWindowAv{end}(end,1)-TrajobjectPar.Trajobject.TrajPosEstimation.FixWindowAv{posStartInds(end)}(1,1);
sliding{theSet}.measTime=measTime;
end
slidingCombines=cell(1,numel(combineExperiments));
for theComb=[1:numel(combineExperiments)]
disp(['combination number ' num2str(theComb)])
measTimeTot=0;
numTrajsokLengthTot=0;
numTotTrajsTot=0;
numSlidingTot=0;
for theSet=1:numel(combineExperiments{theComb})
ind=combineExperiments{theComb}(theSet);
measTimeTot=measTimeTot+sliding{ind}.measTime;
numTrajsokLengthTot=numTrajsokLengthTot+sliding{ind}.numTrajsOkLength;
numTotTrajsTot=numTotTrajsTot+sliding{ind}.numTotTrajs;
numSlidingTot=numSlidingTot+numel(sliding{ind}.slidingInd);
end
slidingCombines{theComb}.measTime=measTimeTot;
slidingCombines{theComb}.numSliding=numSlidingTot;
slidingCombines{theComb}.numTrajsOkLength=numTrajsokLengthTot;
slidingCombines{theComb}.numTotTrajs=numTotTrajsTot;
end
%%
%% Plot autocorrealtion functions
times=12;%Time index of the first time lag of the autocorrelation you want to use
numplots=numel(times);
figure();
ind1=1;
ind2=2;
k=1;
perc=0;
numBoots=2000;
for timei=times
[numtrajs1,~]=size(scaleRes{timei}.combines{ind1}.autoCorrValsAllok);
[numtrajs2,~]=size(scaleRes{timei}.combines{ind2}.autoCorrValsAllok);
bootCorrs1=zeros(numBoots,timeWind);
bootCorrs2=zeros(numBoots,timeWind);
for boot=1:numBoots
bootInds1=ceil(rand(1,numtrajs1)*numtrajs1);
bootInds2=ceil(rand(1,numtrajs2)*numtrajs2);
bootCorrs1(boot,:)=trimmean(scaleRes{timei}.combines{ind1}.autoCorrValsAllok(bootInds1,:),perc);
bootCorrs2(boot,:)=trimmean(scaleRes{timei}.combines{ind2}.autoCorrValsAllok(bootInds2,:),perc);
end
%subplot(numplots,1,k);
hold on
errorbar(scaleRes{timei}.lagTimes,mean(bootCorrs1),std(bootCorrs1),'b');
hold on;
errorbar(scaleRes{timei}.lagTimes,mean(bootCorrs2),std(bootCorrs2),'r');
k=k+1;
%set(gca,'YScale','log');
set(gca,'XScale','log');
xlim([T(times(1)) T(times(end)+timeWind-1)])
ylim([0 1.2]);
end
title('Sliding molecules');
xlabel('Time lag [s]');
ylabel('Autocorrelation');
numplots=numel(times);
figure();
ind1=3;
ind2=4;
k=1;
numBoots=2000;
for timei=times
[numtrajs1,~]=size(scaleRes{timei}.combines{ind1}.autoCorrValsAllok);
[numtrajs2,~]=size(scaleRes{timei}.combines{ind2}.autoCorrValsAllok);
bootCorrs1=zeros(numBoots,timeWind);
bootCorrs2=zeros(numBoots,timeWind);
for boot=1:numBoots;
bootInds1=ceil(rand(1,numtrajs1)*numtrajs1);
bootInds2=ceil(rand(1,numtrajs2)*numtrajs2);
bootCorrs1(boot,:)=trimmean(scaleRes{timei}.combines{ind1}.autoCorrValsAllok(bootInds1,:),perc);
bootCorrs2(boot,:)=trimmean(scaleRes{timei}.combines{ind2}.autoCorrValsAllok(bootInds2,:),perc);
end
%subplot(numplots,1,k);
hold on
errorbar(scaleRes{timei}.lagTimes,mean(bootCorrs1),std(bootCorrs1),'b');
hold on;
errorbar(scaleRes{timei}.lagTimes,mean(bootCorrs2),std(bootCorrs2),'r');
k=k+1;
%set(gca,'YScale','log');
set(gca,'XScale','log');
xlim([T(times(1)) T(times(end)+timeWind-1)])
ylim([0 1.2]);
end
title('Stationary molecules')
xlabel('Time lag [s]');
ylabel('Autocorrelation');
%%
DxsMBP1=[slidingAvg{1}.Dxs slidingAvg{2}.Dxs slidingAvg{3}.Dxs];
DxsRhoda1=[slidingAvg{7}.Dxs slidingAvg{8}.Dxs];
DxsMBP2=[slidingAvg{4}.Dxs slidingAvg{5}.Dxs slidingAvg{6}.Dxs];
DxsRhoda2=[slidingAvg{9}.Dxs slidingAvg{10}.Dxs];
allDxs{1}=DxsMBP1;
allDxs{2}=DxsRhoda1;
allDxs{3}=DxsMBP2;
allDxs{4}=DxsRhoda2;
allDxs{1}=[DxsMBP1 DxsMBP2];
allDxs{2}=[DxsRhoda1 DxsRhoda2];
maxD=inf;
minD=-inf;
trims=0:80;
meansDxs=zeros(4,numel(trims));
errorsDxs=zeros(4,numel(trims));
meanBoots=cell(1,numel(trims));
numBoots=500;
meanBoot=zeros(numBoots,1);
k=1;
for i=trims
meanBoots{k}=zeros(numBoots,size(meansDxs,1));
disp(i);
for j=1:numel(allDxs)
currVals=allDxs{j}(allDxs{j}<maxD);
currVals=currVals(currVals>minD);
meansDxs(j,k)=trimmean(currVals,i);
meansBoot=zeros(1,numBoots);
numSamples=numel(currVals);
for bootInd=1:numBoots
sampleInds=ceil(rand(1,numSamples)*numSamples);
meanBoot(bootInd)=trimmean(currVals(sampleInds),i);
end
errorsDxs(j,k)=std(meanBoot);
meanBoots{k}(:,j)=meanBoot;
end
k=k+1;
end
confInterval=0.95;
sortedFactors=sort(meanBoots{1}(:,2)./meanBoots{1}(:,1),'ascend');
minInd=round(numBoots*(1-confInterval)/2);
lowerBoundkFactor=sortedFactors(minInd);
upperBoundkFactor=sortedFactors(end-minInd);
figure();errorbar(trims,meansDxs(1,:),errorsDxs(1,:),'b');
hold on;
errorbar(trims,meansDxs(2,:),errorsDxs(2,:),'r');
ylabel('Estimated diffusion constant [um^2/s]');
xlabel('Trim percent in trimmean');
legend('LacI-MBP-R','LacI-R')
%% Fit autocorrelation model to the difference in auto correlation of the two proteins
totNumParams=2;
kguessExpMax=8;
kguessExpMin=1;
aguessExpMax=-6;
aguessExpMin=-7;
numGuesses=200;%Number of itnial guesses to to pe each fit (on data and bootstrapped data)
numTerms=1;
%Index of timescales to use
%theScales=[5:26];
theScales=[timei];
numScales=numel(theScales);
%Datasets/combinations to compare
compare{1}=[1,2];
bgInds=[3 4];
%Background or amplitude of background isn't taken into account in this version of the fit
combUnNorm{bgInds(1)}.autoCorrScaled=zeros(1,timeWind);
combUnNorm{bgInds(2)}.autoCorrScaled=zeros(1,timeWind);
combUnNorm{bgInds(1)}.startScale=timei;
combUnNorm{bgInds(2)}.startScale=timei;
D2=meansDxs(2,21); %Diffusion constants are here the 20% trimmed means
D=meansDxs(1,21);
kFactor=D2/D;
%set lower and Upper bounds for the parameters of the fit. In this version,
%the exponential k of LacI-MBP-R is te only true free parameter, and the
%the exponential for the other prother can vary within what is predicted
%from the 95% confidence itnerval of the translational diffusion constants
%of the protein
lb=zeros(1,3);
ub=zeros(1,3);
lb(1:totNumParams/2)=lowerBoundkFactor;%kFactor, so that k*kFactor is the exponantial for the second protein
lb(totNumParams/2+1:totNumParams)=-inf;% %k, exponential of the first protein
lb(totNumParams+1:totNumParams+1)=0; %background parameter b
ub(1:totNumParams/2)=upperBoundkFactor;
ub(totNumParams/2+1:totNumParams)=0;
ub(totNumParams+1:totNumParams+1)=1e-8;
numBoots=500; %nubmer of bootstraps estiamtes to fit on
numPreBoots=500;%Number of bootstraps used when calculating the weights for the fit
perc=0;
tic;
[expFitCombDiff] = fitAutoCorrModelv9_bgguess(timeWind,normNumber,kguessExpMax,kguessExpMin,numGuesses,theScales,compare,lb,ub,numBoots,numPreBoots,perc,scaleRes,combUnNorm,bgInds,aguessExpMax,aguessExpMin);
%[expFitCombDiff] = fitAutoCorrModelv2(timeWind,normNumber,kguessExpMax,kguessExpMin,numGuesses,theScales,compare,lb,ub,numBoots,numPreBoots,perc,scaleRes);
toc
ind1=1;
kfacInds=1:totNumParams/2;
kInds=totNumParams/2+1:totNumParams;
bExtraInds=(totNumParams+1)*ones(1,numScales);
bootInd=8;
for i=1:numScales
figure('position',[0,0,250,500]);
hold on;
currAutoCorr=expFitCombDiff{ind1}.autoCorrsDiff(i,:);
currAutoCorrBoot=expFitCombDiff{ind1}.boot.autoCorrsDiffBoot(i,:,:);
errors=std(currAutoCorrBoot,[],3);
errors=reshape(errors,[1 timeWind]);
errorbar(expFitCombDiff{ind1}.lagTimes(i,:),currAutoCorr,errors,'kx','LineWidth',2);
bgAmpl=expFitCombDiff{ind1}.bgAmpl;
hold on;
currbestparams=expFitCombDiff{ind1}.bestparams;
fac=10;
timeDiff=expFitCombDiff{ind1}.lagTimes(i,2)-expFitCombDiff{ind1}.lagTimes(i,1);
plotTimes=expFitCombDiff{ind1}.lagTimes(i,1):timeDiff/fac:expFitCombDiff{ind1}.lagTimes(i,end);
normInds=zeros(1,normNumber);
for j=1:normNumber
[~,normInds(j)]=min(abs(plotTimes-expFitCombDiff{ind1}.lagTimes(i,j)));
end
autoCorrScaledBg1=combUnNorm{bgInds(1)}.autoCorrScaled;
autoCorrScaledBg2=combUnNorm{bgInds(2)}.autoCorrScaled;
bg1=[autoCorrScaledBg1(:,1)-autoCorrScaledBg1(:,end)];
bg2=[autoCorrScaledBg2(:,1)-autoCorrScaledBg2(:,end)];
corr1=corrFuncMultExpBgbExtra4_normIndscleaned(ones(1,numTerms),currbestparams(kInds),currbestparams(bExtraInds(i))*bg1(i),plotTimes,normInds);
corr2=corrFuncMultExpBgbExtra4_normIndscleaned(ones(1,numTerms),currbestparams(kInds).*currbestparams(kfacInds),currbestparams(bExtraInds(i))*bg2(i),plotTimes,normInds);
plot(plotTimes,corr1-corr2,'k','LineWidth',2);
ylim([-0.3 0.3])
xlim([0 expFitCombDiff{ind1}.lagTimes(i,end)]);
%xlim([10^-6 10^-3]);
xlabel('Time lag [s]');
ylabel('Autocorrelation diff.')
set(gca,'Xscale','log');
end
i=1; %index of timescale in theScales to do more plotting for