Please use the online documentation website going forward: https://bahanonu.github.io/ciatah/

Example calciumImagingAnalysis pipeline via the command line.

Below is an example cacliumImagingAnalysis pipeline using the command line for those that do not want to use the class or want to create their own custom batch analyses. It assumes you have already run example_downloadTestData to download the example test data.

Can also access by typing edit ciapkg.demo.cmdLinePipeline into the command line.

% Running calciumImagingAnalysis command line

%% Load movie to analyze
inputMovie = loadMovieList('data\2014_04_01_p203_m19_check01\concat_recording_20140401_180333.h5');

%% Visualize slice of the movie
playMovie(inputMoevie(:,:,1:500));

%% Downsample input movie if need to
inputMovieD = downsampleMovie(inputMovie,'downsampleDimension','space','downsampleFactor',4);
playMovie(inputMovie,'extraMovie',inputMovieD);

%% Remove stripes from movie if needed
% Show full filter sequence for one frame
sopts.stripOrientation = 'both';
sopts.meanFilterSize = 1;
sopts.freqLowExclude = 10;
sopts.bandpassType = 'highpass';
removeStripsFromMovie(inputMovie(:,:,1),'options',sopts,'showImages',1);
% Run on the entire movie
removeStripsFromMovie(inputMovie,'options',sopts);

%% Get coordinates to crop
[cropCoords] = getCropCoords(squeeze(inputMovie(:,:,1)));
toptions.cropCoords = cropCoords;

%% Motion correction
% Or have turboreg run manual correction
toptions.cropCoords = 'manual';
toptions.turboregRotation = 0;
toptions.removeEdges = 1;
toptions.pxToCrop = 10;
% Pre-motion correction
	toptions.complementMatrix = 1;
	toptions.meanSubtract = 1;
	toptions.meanSubtractNormalize = 1;
	toptions.normalizeType = 'matlabDisk';
% Spatial filter
	toptions.normalizeBeforeRegister = 'divideByLowpass';
	toptions.freqLow = 0;
	toptions.freqHigh = 7;
[inputMovie2, ~] = turboregMovie(inputMovie,'options',toptions);

%% Compare raw and motion corrected movies
playMovie(inputMovie,'extraMovie',inputMovie2);

%% Run dF/F
inputMovie3 = dfofMovie(single(inputMovie2),'dfofType','dfof');

%% Run temporal downsampling
inputMovie3 = downsampleMovie(inputMovie3,'downsampleDimension','time','downsampleFactor',4);

%% Final check of movie before cell extraction
playMovie(inputMovie3);

%% Run PCA-ICA cell extraction
nPCs = 300; nICs = 225;
[PcaOutputSpatial, PcaOutputTemporal, PcaOutputSingularValues, PcaInfo] = run_pca(inputMovie3, nPCs, 'movie_dataset_name','/1');
[IcaFilters, IcaTraces, IcaInfo] = run_ica(PcaOutputSpatial, PcaOutputTemporal, PcaOutputSingularValues, size(inputMovie3,1), size(inputMovie3,2), nICs, 'output_units','fl','mu',0.1,'term_tol',5e-6,'max_iter',1e3);
IcaTraces = permute(IcaTraces,[2 1]);

%% Save outputs to NWB format
saveNeurodataWithoutBorders(IcaFilters,{IcaTraces},'pcaica','pcaica.nwb');

%% Run cell extraction using matrix
[outImages, outSignals, choices] = signalSorter(IcaFilters,IcaTraces,'inputMovie',inputMovie3);

%% Run signal sorting using NWB
[outImages, outSignals, choices] = signalSorter('pcaica.nwb',[],'inputMovie',inputMovie3);

%% Plot results of sorting
figure;
subplot(1,2,1);imagesc(max(IcaFilters,[],3));axis equal tight; title('Raw filters')
subplot(1,2,2);imagesc(max(outImages,[],3));axis equal tight; title('Sorted filters')

%% Create an overlay of extraction outputs on the movie and signal-based movie
[inputMovieO] = createImageOutlineOnMovie(inputMovie3,IcaFilters,'dilateOutlinesFactor',0);
[signalMovie] = createSignalBasedMovie(IcaTraces,IcaFilters,'signalType','peak');

%% Play all three movies
% Normalize all the movies
movieM = cellfun(@(x) normalizeVector(x,'normRange','zeroToOne'),{inputMovie3,inputMovieO,signalMovie},'UniformOutput',false);
playMovie(cat(2,movieM{:}));