CNMF_demo_script.m

function output = CNMF_demo_script(filename, options)
% CNMF_DEMO_SCRIPT
%
%  OUTPUT = CNMF_DEMO_SCRIPT(FILENAME, [OPTIONS])
%
%  Performs a demo calcium source separation using the CNMF
%  algorithm.
%
%  FILENAME should be a multi-frame TIFF file with 2-photon calcium
%  imaging data. OPTIONS is an optional structure that modifies the
%  default parameters that are established in CNMFSetParms.
%
%  OUTPUT is a structure with all of the variables created while
%  processing the image. Note that this will include the entire
%  raw data.
%
%  See also: DEMO_SCRIPT, CNMFSetParms
%   


sframe=1;						% user input: first frame to read (optional, default 1)
num2read=2000;					% user input: how many frames to read   (optional, default until the end)
Y = bigread2(filename,sframe,num2read);

%Y = Y - min(Y(:)); 
if ~isa(Y,'double')
	 Y = double(Y);
end         % convert from single to double

[d1,d2,T] = size(Y);                                % dimensions of dataset, assume T is last
d = d1*d2;                                          % total number of pixels per T image

%% Set parameters

K = 40;                                           % number of components to be found
tau = 4;                                          % std of gaussian kernel (size of neuron) 
p = 2;                                            % order of autoregressive system
                                                  % (p = 0 no dynamics, p=1 just decay, p = 2, both rise and decay)
merge_thr = 0.8;                                  % merging threshold

defoptions = CNMFSetParms(...                      
    'd1',d1,'d2',d2,...                         % dimensions of datasets
    'search_method','dilate','dist',3,...       % search locations when updating spatial components
    'deconv_method','constrained_foopsi',...    % activity deconvolution method
    'temporal_iter',2,...                       % number of block-coordinate descent steps 
    'fudge_factor',0.98,...                     % bias correction for AR coefficients
    'merge_thr',merge_thr,...                    % merging threshold
    'gSig',tau...
    );

options = structmerge(defoptions,options,'ErrorIfNewField',1);

options,

%% Data pre-processing

[P,Y] = preprocess_data(Y,p);

%% fast initialization of spatial components using greedyROI and HALS

[Ain,Cin,bin,fin,center] = initialize_components(Y,K,tau,options,P);  % initialize

% display centers of found components
Cn =  correlation_image(Y);
figure;
imagesc(Cn);
axis equal; axis tight; hold all;
scatter(center(:,2),center(:,1),'mo');
title('Center of ROIs found from initialization algorithm');
drawnow;

%% manually refine components (optional)
refine_components = true;  % flag for manual refinement
if refine_components
    [Ain,Cin,center] = manually_refine_components(Y,Ain,Cin,center,Cn,tau,options);
end
    
%% update spatial components
Yr = reshape(Y,d,T);
[A,b,Cin] = update_spatial_components(Yr,Cin,fin,[Ain,bin],P,options);

%% update temporal components
P.p = 0;    % set AR temporarily to zero for speed
[C,f,P,S,YrA] = update_temporal_components(Yr,A,b,Cin,fin,P,options);

%% classify components
[ROIvars.rval_space,ROIvars.rval_time,ROIvars.max_pr,ROIvars.sizeA,keep] = classify_components(Y,A,C,b,f,YrA,options);

%% run GUI for modifying component selection (optional, close twice to save values)
run_GUI = true;
if run_GUI
    Coor = plot_contours(A,Cn,options,1); close;
    GUIout = ROI_GUI(A,options,Cn,Coor,keep,ROIvars);   
    options = GUIout{2};
    keep = GUIout{3};    
end
%% merge found components
[Am,Cm,K_m,merged_ROIs,Pm,Sm] = merge_components(Yr,A(:,keep),b,C(keep,:),f,P,S,options);

%%
display_merging = 1; % flag for displaying merging example
if and(display_merging, ~isempty(merged_ROIs))
    i = 1; %randi(length(merged_ROIs));
    ln = length(merged_ROIs{i});
    figure;
        set(gcf,'Position',[300,300,(ln+2)*300,300]);
        for j = 1:ln
            subplot(1,ln+2,j); imagesc(reshape(A(:,merged_ROIs{i}(j)),d1,d2)); 
                title(sprintf('Component %i',j),'fontsize',16,'fontweight','bold'); axis equal; axis tight;
        end
        subplot(1,ln+2,ln+1); imagesc(reshape(Am(:,K_m-length(merged_ROIs)+i),d1,d2));
                title('Merged Component','fontsize',16,'fontweight','bold');axis equal; axis tight; 
        subplot(1,ln+2,ln+2);
            plot(1:T,(diag(max(C(merged_ROIs{i},:),[],2))\C(merged_ROIs{i},:))'); 
            hold all; plot(1:T,Cm(K_m-length(merged_ROIs)+i,:)/max(Cm(K_m-length(merged_ROIs)+i,:)),'--k')
            title('Temporal Components','fontsize',16,'fontweight','bold')
        drawnow;
end

%% refine estimates excluding rejected components

Pm.p = p;    % restore AR value
[A2,b2,C2] = update_spatial_components(Yr,Cm,f,[Am,b],Pm,options);
[C2,f2,P2,S2,YrA2] = update_temporal_components(Yr,A2,b2,C2,f,Pm,options);


%% do some plotting

[A_or,C_or,S_or,P_or] = order_ROIs(A2,C2,S2,P2); % order components
K_m = size(C_or,1);
[C_df,~] = extract_DF_F(Yr,A_or,C_or,P_or,options); % extract DF/F values (optional)

figure;
[Coor,json_file] = plot_contours(A_or,Cn,options,1); % contour plot of spatial footprints
%savejson('jmesh',json_file,'filename');        % optional save json file with component coordinates (requires matlab json library)

%% display components

plot_components_GUI(Yr,A_or,C_or,b2,f2,Cn,options)

%% make movie

make_patch_video(A_or,C_or,b2,f2,Yr,Coor,options)

output = workspace2struct;