Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse.

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[1] § Methods › Cell registration across sessions ↔ GUI/CellReg.m, lines 1–91 · score 0.61 · CellReg, cellular activity, scored, events, MATLAB, neurons

Paper

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The authors' code

MATLAB · 2,236 lines · 108 KB · GPL-2.0 · 1 match

function varargout = CellReg(varargin)
% This GUI is an implementation of a probabilistic approach for the
% identification of the same neurons (cell registration) across multiple sessions
% in Ca2+ imaging data, developed by Sheintuch et al., 2017.
% Input: The inputs for the cell registration method are the spatial footprints of
% cellular activity of the cells that were detected in the different
% sessions. Each spatial footprint is a matrix the size of the frame and
% each pixel's value represents its contribution to the
% cell's fluorescence.
% Output: The main output for the cell registration method is the obtained mapping of
% cell identity across all registered sessions. It is a matrix the size of
% the final number of registered cells by the number of registered
% sessions. Each entry holds the index for the cell in a given session.
% Other outputs include:
% 1) register scores - providing with the registration quality of each cell register
% 2) log file - with all the relevant information regarding the data, registration
% configurations, and a summary of the registration results and quality.
% 3) figures - important figures that are saved automatically.
% The GUI includes the following stages:
% 1) Loading the spatial footprints of cellular activity from the different sessions.
% 2) Transforming all the sessions to a reference coordinate system using
% rigid-body transformation.
% 3) Computing a probabilistic model of the spatial footprints similarities
% of neighboring cell-pairs from different sessions using the centroid
% distances and spatial correlations.
% 4) Obtaining an initial cell registration according to an optimized registration threshold.
% 5) Obtaining the final cell registration based on a correlation clustering algorithm.
% CellReg MATLAB code for CellReg.fig
% CellReg, by itself, creates a new CellReg or raises the existing
% singleton*.
%
% H = CellReg returns the handle to a new CellReg or the handle to
% the existing singleton*.
%
% CellReg('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in CellReg.M with the given input arguments.
%
% CellReg('Property','Value',...) creates a new CellReg or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before CellReg_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to CellReg_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help CellReg
% Last Modified by GUIDE v2.5 19-Mar-2018 16:44:19
% reset figure properties to default:
if verLessThan('matlab','8.4')
% MATLAB R2014a and earlier
% if there are problems with GUI and figures change properties back to default
else
% MATLAB R2014b and later
reset(0);
end
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @CellReg_OpeningFcn, ...
'gui_OutputFcn', @CellReg_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before CellReg is made visible.
function CellReg_OpeningFcn(hObject,~, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% varargin command line arguments to CellReg (see VARARGIN)
% Choose default command line output for CellReg
handles.output = hObject;
% Update handles struct
guidata(hObject, handles);
% UIWAIT makes CellReg wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% defining data struct:
data_struct=struct;
data_struct.sessions_list=[];
% Reseting figures and GUI parameters:
cla(handles.axes1,'reset')
axes(handles.axes1);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes2,'reset')
axes(handles.axes2);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes3,'reset')
axes(handles.axes3);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes4,'reset')
axes(handles.axes4);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes5,'reset')
axes(handles.axes5);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes6,'reset')
axes(handles.axes6);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes7,'reset')
axes(handles.axes7);
logo=imread('CellReg_Logo.png');
imagesc(logo);
set(gca,'xtick',[])
set(gca,'ytick',[])
data_struct.sessions_list=[];
set(handles.list_of_sessions,'value',1)
set(handles.list_of_sessions,'string',[]);
set(handles.green_session,'string','2')
set(handles.blue_session,'string','3')
set(handles.decision_thresh,'string','0.5')
set(handles.initial_p_same_slider,'value',0.5);
set(handles.initial_p_same_threshold,'string','0.5');
set(handles.final_p_same_slider,'value',0.5);
set(handles.model_maximal_distance,'string','14')
set(handles.distance_threshold,'string','5')
set(handles.correlation_threshold,'string','0.65')
set(handles.simple_distance_threshold,'string','5')
set(handles.simple_correlation_threshold,'string','0.65')
set(handles.figures_visibility_on,'Value',1);
set(handles.write2file_on, 'Value', 0);
set(handles.translations_rotations,'Value',1);
set(handles.spatial_correlations_2,'Value',1);
set(handles.spatial_correlations,'Value',1);
set(handles.use_model,'Value',1);
set(handles.microns_per_pixel,'string',[])
set(handles.microns_per_pixel,'value',0)
set(handles.microns_per_pixel,'backgroundColor',[1 1 1]);
set(handles.reference_session_index,'string','1')
set(handles.maximal_rotation','string','30')
set(handles.maximal_rotation','enable','on')
set(handles.transformation_smoothness,'string','2')
set(handles.transformation_smoothness,'enable','off')
set(handles.distance_threshold,'enable','off')
set(handles.correlation_threshold,'enable','on')
set(handles.decision_thresh,'enable','on')
set(handles.simple_distance_threshold,'enable','off')
set(handles.simple_correlation_threshold,'enable','off')
set(handles.comments,'string',[])
handles.data_struct=data_struct;
guidata(hObject, handles);
% --- Outputs from this function are returned to the command line.
function varargout = CellReg_OutputFcn(~, ~, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Get default command line output from handles struct
varargout{1} = handles.output;
% --------------------------------------------------------------------
function load_new_data_Callback(hObject,~, handles)
% hObject handle to load_new_data (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Stage 1: Loading the spatial footprints of cellular activity from the different
% sessions.
% This callback loads a new data set which includes several sessions with
% their spatial footprints, centroid locations (optional), and events (optional). A
% single folder should be selected with all the mat files with number:
% example: "finalFiltersMat_1", "finalFiltersMat_2", "finalEventsMat_1", "finalEventsMat_2"
data_struct=handles.data_struct;
% choosing the files to load:
msgbox_timed('Please choose the files containing the spatial footprints from all the sessions: ',3)
[file_names,files_path]=uigetfile('*.mat','MultiSelect','on',...
'Choose spatial footprints from all the sessions: ' );
% in case only one file is selected uigetfile will return a char and not a
% cell, and thus make later code bug.
if ischar(file_names)
warndlg_timed('To process only one session you should use Add Session button',3)
return
end
number_of_sessions=size(file_names,2);
sessions_list=cell(1,number_of_sessions);
temp_file_names=cell(1,number_of_sessions);
for n=1:number_of_sessions
sessions_list{1,n}=['Session ' num2str(n) ' - ' files_path file_names{1,n}];
temp_file_names{1,n}=[files_path file_names{1,n}];
end
file_names=temp_file_names;
% defining the microns per pixel ratio:
microns_per_pixel=str2num(get(handles.microns_per_pixel,'string'));
if isempty(microns_per_pixel)
msgbox_timed('Please insert the pixel size in microns and press enter ',3);
set(handles.microns_per_pixel,'backgroundColor',[1 0.5 0.5]);
waitfor(handles.microns_per_pixel,'value',1);
microns_per_pixel=str2num(get(handles.microns_per_pixel,'string'));
end
set(handles.microns_per_pixel,'string',num2str(round(100*microns_per_pixel)/100));
set(handles.microns_per_pixel,'backgroundColor',[1 1 1]);
% defining the results directory:
msgbox_timed('Please select the folder in which the results will be saved',3)
results_directory=uigetdir(files_path); % the directory which the final results will be saved
figures_directory=fullfile(results_directory,'Figures');
if exist(figures_directory,'dir')~=7
mkdir(figures_directory);
end
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
% loading the spatial footprints:
disp('Stage 1 - Loading sessions')
if get(handles.write2file_on,'Value')
spatial_footprints = file_names;
number_of_sessions = length(file_names);
else
[spatial_footprints,number_of_sessions]=load_multiple_sessions(file_names);
end
[footprints_projections]=compute_footprints_projections(spatial_footprints);
plot_all_sessions_projections(footprints_projections,figures_directory,figures_visibility)
% saving the loaded data into the data struct for the GUI
if get(handles.write2file_on,'Value')
data_struct.temp_dir = [figures_directory, filesep, 'temp'];
if ~exist(data_struct.temp_dir)
mkdir(data_struct.temp_dir);
end
else
data_struct.temp_dir = [];
end
data_struct.results_directory=results_directory;
data_struct.figures_directory=figures_directory;
data_struct.microns_per_pixel=microns_per_pixel;
data_struct.spatial_footprints=spatial_footprints;
data_struct.footprints_projections=footprints_projections;
data_struct.number_of_sessions=number_of_sessions;
data_struct.sessions_list=sessions_list;
data_struct.file_names=file_names;
set(handles.list_of_sessions,'string',data_struct.sessions_list);
handles.data_struct=data_struct;
guidata(hObject, handles)
disp('Done')
msgbox_timed('Finished loading sessions',3)
% --- Executes on button press in add_session.
function add_session_Callback(hObject,~, handles)
% hObject handle to add_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% This callback adds another session to the list of sessions to be
% registered. The folder containg the filters, centroid_locations, and events
% (optional) should be selected
data_struct=handles.data_struct;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
if isfield(data_struct,'spatial_footprints') % some sessions were already loaded
spatial_footprints=data_struct.spatial_footprints;
number_of_sessions=data_struct.number_of_sessions;
file_names=data_struct.file_names;
sessions_list=data_struct.sessions_list;
results_directory=data_struct.results_directory;
% loading the session:
msgbox_timed('Please choose the file with the spatial footprints for this session: ',1)
[file_name,file_path]=uigetfile(strcat(results_directory,filesep,'*.mat'),...
'Choose the file with the spatial footprints for this session: ','MultiSelect','off');
number_of_sessions=number_of_sessions+1;
file_names{number_of_sessions}=[file_path file_name];
sessions_list{number_of_sessions}=['Session ' num2str(number_of_sessions) ' - ' file_path file_name];
disp('Stage 1 - Loading sessions')
if get(handles.write2file_on,'Value')
added_spatial_footprints = file_names{number_of_sessions};
else
[added_spatial_footprints]=load_single_session(file_names{number_of_sessions});
end
spatial_footprints{number_of_sessions}=added_spatial_footprints;
[added_footprints_projection]=compute_footprints_projections({added_spatial_footprints});
footprints_projections{number_of_sessions}=added_footprints_projection;
plot_single_session_projections(added_footprints_projection,num2str(number_of_sessions),figures_visibility)
else % first loaded session
data_struct=handles.data_struct;
% chosing the file to load:
msgbox_timed('Please choose the file with the spatial footprints for this session: ',1)
[file_name,file_path]=uigetfile('*.mat',...
'Choose the file with the spatial footprints for this session: ','MultiSelect','off');
number_of_sessions=1;
sessions_list={['Session 1 - ' file_path file_name]};
file_names={[file_path file_name]};
% defining the microns per pixel ratio:
microns_per_pixel=str2num(get(handles.microns_per_pixel,'string'));
if isempty(microns_per_pixel)
msgbox_timed('Please insert the pixel size in microns and press enter ',3);
set(handles.microns_per_pixel,'backgroundColor',[1 0.5 0.5]);
waitfor(handles.microns_per_pixel,'value',1);
microns_per_pixel=str2num(get(handles.microns_per_pixel,'string'));
end
set(handles.microns_per_pixel,'string',num2str(round(100*microns_per_pixel)/100));
set(handles.microns_per_pixel,'backgroundColor',[1 1 1]);
% defining the results directory:
msgbox_timed('Please select the folder in which the results will be saved',3)
results_directory=uigetdir(file_path); % the directory which the final results will be saved
figures_directory=fullfile(results_directory,'Figures');
if exist(figures_directory,'dir')~=7
mkdir(figures_directory);
end
% loading the spatial footprints:
disp('Stage 1 - Loading sessions')
if get(handles.write2file_on,'Value');
spatial_footprints = {file_names{1}};
else
[spatial_footprints]={load_single_session(file_names{1})};
end
[footprints_projections]=compute_footprints_projections(spatial_footprints);
plot_single_session_projections(footprints_projections,1,figures_visibility)
% saving the loaded data into the data struct for the GUI
data_struct.results_directory=results_directory;
data_struct.figures_directory=figures_directory;
data_struct.microns_per_pixel=microns_per_pixel;
end
% saving the loaded data into the data struct for the GUI
if get(handles.write2file_on,'Value')
data_struct.temp_dir = [figures_directory, filesep, 'temp'];
if ~exist(data_struct.temp_dir)
mkdir(data_struct.temp_dir);
end
else
data_struct.temp_dir = [];
end
data_struct.spatial_footprints=spatial_footprints;
data_struct.footprints_projections=footprints_projections;
data_struct.number_of_sessions=number_of_sessions;
data_struct.sessions_list=sessions_list;
data_struct.file_names=file_names;
set(handles.list_of_sessions,'string',data_struct.sessions_list);
handles.data_struct=data_struct;
guidata(hObject, handles)
disp('Done')
msgbox_timed('Finished loading session',1)
% --- Executes on button press in remove_session.
function remove_session_Callback(hObject,~, handles)
% hObject handle to remove_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% This callback removes the selected session from the list of sessions to be registered
data_struct=handles.data_struct;
if isfield(data_struct,'spatial_footprints')
% selecting the session to remove:
number_of_sessions=data_struct.number_of_sessions;
chosen_session=get(handles.list_of_sessions,'value');
sessions_to_keep=setdiff(1:number_of_sessions,chosen_session);
set(handles.list_of_sessions,'value',1)
number_of_sessions=number_of_sessions-1;
% removing the session from the data:
if isfield(data_struct,'spatial_footprints_corrected') % if data was aligned
centroid_locations=data_struct.centroid_locations(sessions_to_keep);
adjusted_footprints_projections=data_struct.adjusted_footprints_projections(sessions_to_keep);
spatial_footprints_corrected=data_struct.spatial_footprints_corrected(sessions_to_keep);
centroid_locations_corrected=data_struct.centroid_locations_corrected(sessions_to_keep);
footprints_projections_corrected=data_struct.footprints_projections_corrected(sessions_to_keep);
% for variables that are compared to a reference session:
reference_session_index=data_struct.reference_session_index;
if reference_session_index==chosen_session
errordlg('This session was used as a reference for alignment and therefore cannot be removed')
error('This session was used as a reference for alignment and therefore cannot be removed')
else
sessions_without_reference=setdiff(1:number_of_sessions+1,reference_session_index);
chosen_session_compared_to_reference_index=find(sessions_without_reference==chosen_session);
sessions_to_keep_compared_to_reference=setdiff(1:number_of_sessions,chosen_session_compared_to_reference_index);
maximal_cross_correlation=data_struct.maximal_cross_correlation(sessions_to_keep_compared_to_reference);
alignment_translations=data_struct.alignment_translations(:,sessions_to_keep_compared_to_reference);
end
if reference_session_index>chosen_session % index of reference should change
reference_session_index=reference_session_index-1;
end
data_struct.centroid_locations=centroid_locations;
data_struct.adjusted_footprints_projections=adjusted_footprints_projections;
data_struct.spatial_footprints_corrected=spatial_footprints_corrected;
data_struct.centroid_locations_corrected=centroid_locations_corrected;
data_struct.footprints_projections_corrected=footprints_projections_corrected;
data_struct.maximal_cross_correlation=maximal_cross_correlation;
data_struct.alignment_translations=alignment_translations;
data_struct.reference_session_index=reference_session_index;
end
spatial_footprints=data_struct.spatial_footprints;
footprints_projections=data_struct.footprints_projections;
spatial_footprints=spatial_footprints(sessions_to_keep);
footprints_projections=footprints_projections(sessions_to_keep);
file_names=data_struct.file_names(sessions_to_keep);
sessions_list=cell(1,number_of_sessions);
for n=1:number_of_sessions
sessions_list{1,n}=['Session ' num2str(n) ' - ' file_names{1,n}];
end
data_struct.number_of_sessions=number_of_sessions;
data_struct.spatial_footprints=spatial_footprints;
data_struct.footprints_projections=footprints_projections;
data_struct.sessions_list=sessions_list;
data_struct.file_names=file_names;
data_struct.sessions_list=sessions_list;
set(handles.list_of_sessions,'string',data_struct.sessions_list);
handles.data_struct=data_struct;
guidata(hObject, handles)
msgbox_timed('Finished removing session',1)
end
% --------------------------------------------------------------------
function load_transformed_data_Callback(hObject,~, handles)
% hObject handle to load_transformed_data (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% This callback loads sessions that were already aligned into a reference coordinate system.
% For such data the compute model should be the next step.
msgbox_timed('Please choose the file containing the aligned data structure: ',3)
[file_name,file_path]=uigetfile('*.mat',...
'Choose the file containing the aligned data: ','MultiSelect','off');
disp('Loading aligned data')
aligned_data_struct=load(fullfile(file_path,file_name));
if ~isstruct(aligned_data_struct)
errordlg('This file does not contain data with the required format')
error('This file does not contain data with the required format')
elseif ~isfield(aligned_data_struct,'aligned_data_struct')
errordlg('This file does not contain data with the required format')
error('This file does not contain data with the required format')
else
% loading the aligned data:
msgbox_timed('Please select the folder in which the results will be saved',3)
results_directory=uigetdir(file_path); % the directory which the final results will be saved
data_struct=aligned_data_struct.aligned_data_struct;
data_struct.results_directory=results_directory;
figures_directory=fullfile(results_directory,'Figures');
data_struct.figures_directory=figures_directory;
if exist(figures_directory,'dir')~=7
mkdir(figures_directory);
end
% plotting the aligned data:
footprints_projections_corrected=data_struct.footprints_projections_corrected;
overlapping_FOV=data_struct.overlapping_FOV;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
plot_all_sessions_projections(footprints_projections_corrected,figures_directory,figures_visibility)
number_of_sessions=length(footprints_projections_corrected);
if number_of_sessions>2
RGB_indexes=[1 2 3];
else
RGB_indexes=[1 2];
end
axes(handles.axes1);
plot_RGB_overlay(footprints_projections_corrected,RGB_indexes,overlapping_FOV)
figure('units','normalized','outerposition',[0.325 0.25 0.35 0.5],'Visible',figures_visibility)
plot_RGB_overlay(footprints_projections_corrected,RGB_indexes,overlapping_FOV)
% loading configurations to GUI:
set(handles.microns_per_pixel,'string',num2str(round(100*data_struct.microns_per_pixel)/100));
set(handles.reference_session_index,'string',num2str(data_struct.reference_session_index))
set(handles.list_of_sessions,'string',data_struct.sessions_list)
if strcmp(data_struct.alignment_type,'Translations')
set(handles.translations,'Value',1);
elseif strcmp(data_struct.alignment_type,'Translations and Rotations')
set(handles.translations_rotations,'Value',1);
else
set(handles.non_rigid,'Value',1);
end
end
handles.data_struct=data_struct;
guidata(hObject, handles)
disp('Done')
msgbox_timed('Finished loading aligned sessions',3)
% --- Executes on button press in load_modeled_data.
function load_modeled_data_Callback(hObject, eventdata, handles)
% hObject handle to load_modeled_data (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% This callback loads data that were already modeled.
% For such data the initial registration should be the next step.
msgbox_timed('Please choose the file containing the modeled data structure: ',3)
[file_name,file_path]=uigetfile('*.mat',...
'Choose the file containing the modeled data','MultiSelect','off');
disp('Loading modeled data')
modeled_data_struct=load(fullfile(file_path,file_name));
if ~isstruct(modeled_data_struct)
errordlg('This file does not contain data with the required format')
error('This file does not contain data with the required format')
elseif ~isfield(modeled_data_struct,'modeled_data_struct')
errordlg('This file does not contain data with the required format')
error('This file does not contain data with the required format')
else
% loading the aligned data:
msgbox_timed('Please select the folder in which the results will be saved',3)
results_directory=uigetdir(file_path); % the directory which the final results will be saved
data_struct=modeled_data_struct.modeled_data_struct;
data_struct.results_directory=results_directory;
figures_directory=fullfile(results_directory,'Figures');
data_struct.figures_directory=figures_directory;
if exist(figures_directory,'dir')~=7
mkdir(figures_directory);
end
% plotting the data:
footprints_projections_corrected=data_struct.footprints_projections_corrected;
overlapping_FOV=data_struct.overlapping_FOV;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
plot_all_sessions_projections(footprints_projections_corrected,figures_directory,figures_visibility)
number_of_sessions=length(footprints_projections_corrected);
if number_of_sessions>2
RGB_indexes=[1 2 3];
else
RGB_indexes=[1 2];
end
axes(handles.axes1);
plot_RGB_overlay(footprints_projections_corrected,RGB_indexes,overlapping_FOV)
figure('units','normalized','outerposition',[0.325 0.25 0.35 0.5],'Visible',figures_visibility)
plot_RGB_overlay(footprints_projections_corrected,RGB_indexes,overlapping_FOV)
% loading configurations to GUI:
set(handles.microns_per_pixel,'string',num2str(round(100*data_struct.microns_per_pixel)/100));
set(handles.reference_session_index,'string',num2str(data_struct.reference_session_index))
set(handles.list_of_sessions,'string',data_struct.sessions_list)
set(handles.model_maximal_distance,'string',num2str(data_struct.maximal_distance));
if strcmp(data_struct.alignment_type,'Translations')
set(handles.translations,'Value',1);
elseif strcmp(data_struct.alignment_type,'Translations and Rotations')
set(handles.translations_rotations,'Value',1);
else
set(handles.non_rigid,'Value',1);
end
end
handles.data_struct=data_struct;
guidata(hObject, handles)
disp('Done')
msgbox_timed('Finished loading modeled data',1)
% --- Executes on button press in transform_sessions.
function transform_sessions_Callback(hObject,~, handles)
% hObject handle to transform_sessions (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Stage 2: Aligning all the sessions to a reference coordinate system using
% rigid-body transformation
% This callback performs rigid-body transfomration to all the sessions
% according to a chosen reference ssseion. This stage:
% 1) Corrects sessions for translation/rotations and transforming the
% spatial footprints into a single coordinate frame
% 2) Matches the sizes of all the spatial footprints from the different sessions
% to the intersction of the different FOVs
% 3) Evaluate whether or not it is suitable for
% longitudinal analysis
use_parallel_processing=true; % either true or false
data_struct=handles.data_struct;
number_of_sessions=data_struct.number_of_sessions;
spatial_footprints=data_struct.spatial_footprints;
results_directory=data_struct.results_directory;
figures_directory=data_struct.figures_directory;
microns_per_pixel=data_struct.microns_per_pixel;
% defining the aligned data structure:
aligned_data_struct=struct;
aligned_data_struct.number_of_sessions=number_of_sessions;
aligned_data_struct.spatial_footprints=spatial_footprints;
aligned_data_struct.results_directory=results_directory;
aligned_data_struct.figures_directory=figures_directory;
aligned_data_struct.microns_per_pixel=microns_per_pixel;
aligned_data_struct.footprints_projections=data_struct.footprints_projections;
aligned_data_struct.sessions_list=data_struct.sessions_list;
aligned_data_struct.file_names=data_struct.file_names;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
% Defining the parameters for image alignment:
translations_value=get(handles.translations,'Value');
rotations_value=get(handles.translations_rotations,'Value');
if translations_value==1
alignment_type='Translations';
elseif rotations_value==1
alignment_type='Translations and Rotations';
else
alignment_type='Non-rigid';
end
if strcmp(alignment_type,'Translations and Rotations')
maximal_rotation=str2num(get(handles.maximal_rotation,'string'));
end
if strcmp(alignment_type,'Non-rigid')
transformation_smoothness=str2num(get(handles.transformation_smoothness,'string'));
if transformation_smoothness>3 || transformation_smoothness<0.5
errordlg('FOV smoothing parameter should be between 0.5-3')
error('FOV smoothing parameter should be between 0.5-3')
end
end
reference_session_index=str2num(get(handles.reference_session_index,'string'));
reference_valid=1;
if isempty(reference_session_index) || reference_session_index<1 || reference_session_index>number_of_sessions
reference_valid=0;
end
while reference_valid==0
set(handles.reference_session_index,'value',0)
msgbox_timed('Please insert a valid reference session number and press enter ',3);
waitfor(handles.reference_session_index,'value',1);
reference_session_index=str2num(get(handles.reference_session_index,'string'));
if ~isempty(reference_session_index) && reference_session_index>=1 && reference_session_index<=number_of_sessions
reference_valid=1;
end
end
% Preparing the data for alignment:
disp('Stage 2 - Aligning sessions')
if ~isempty(data_struct.temp_dir)
[normalized_spatial_footprints]=normalize_spatial_footprints(spatial_footprints, data_struct.temp_dir);
else
[normalized_spatial_footprints]=normalize_spatial_footprints(spatial_footprints);
end
[adjusted_spatial_footprints,adjusted_FOV,adjusted_x_size,adjusted_y_size,adjustment_zero_padding]=...
adjust_FOV_size(normalized_spatial_footprints);
[adjusted_footprints_projections]=compute_footprints_projections(adjusted_spatial_footprints);
[centroid_locations]=compute_centroid_locations(adjusted_spatial_footprints,microns_per_pixel);
[centroid_projections]=compute_centroids_projections(centroid_locations,adjusted_spatial_footprints);
% Aligning the cells according to the tranlations/rotations that maximize their similarity:
sufficient_correlation_centroids=0.2; % smaller correlation imply no similarity between sessions
sufficient_correlation_footprints=0.2; % smaller correlation imply no similarity between sessions
if strcmp(alignment_type,'Translations and Rotations')
[spatial_footprints_corrected,centroid_locations_corrected,footprints_projections_corrected,centroid_projections_corrected,maximal_cross_correlation,alignment_translations,overlapping_FOV]=...
align_images(adjusted_spatial_footprints,centroid_locations,adjusted_footprints_projections,centroid_projections,adjusted_FOV,microns_per_pixel,reference_session_index,alignment_type,sufficient_correlation_centroids,sufficient_correlation_footprints,use_parallel_processing,maximal_rotation);
elseif strcmp(alignment_type,'Non-rigid')
[spatial_footprints_corrected,centroid_locations_corrected,footprints_projections_corrected,centroid_projections_corrected,maximal_cross_correlation,alignment_translations,overlapping_FOV,displacement_fields]=...
align_images(adjusted_spatial_footprints,centroid_locations,adjusted_footprints_projections,centroid_projections,adjusted_FOV,microns_per_pixel,reference_session_index,alignment_type,sufficient_correlation_centroids,sufficient_correlation_footprints,use_parallel_processing,transformation_smoothness);
else
[spatial_footprints_corrected,centroid_locations_corrected,footprints_projections_corrected,centroid_projections_corrected,maximal_cross_correlation,alignment_translations,overlapping_FOV]=...
align_images(adjusted_spatial_footprints,centroid_locations,adjusted_footprints_projections,centroid_projections,adjusted_FOV,microns_per_pixel,reference_session_index,alignment_type,sufficient_correlation_centroids,sufficient_correlation_footprints,use_parallel_processing);
end
% Evaluating data quality:
[all_projections_correlations,number_of_cells_per_session]=...
evaluate_data_quality(spatial_footprints_corrected,centroid_projections_corrected,footprints_projections_corrected,maximal_cross_correlation,alignment_translations,reference_session_index,sufficient_correlation_footprints,alignment_type);
% plotting alignment results:
if strcmp(alignment_type,'Non-rigid')
plot_alignment_results(adjusted_spatial_footprints,centroid_locations,spatial_footprints_corrected,centroid_locations_corrected,adjusted_footprints_projections,footprints_projections_corrected,reference_session_index,all_projections_correlations,maximal_cross_correlation,alignment_translations,overlapping_FOV,alignment_type,number_of_cells_per_session,figures_directory,figures_visibility,displacement_fields)
else
plot_alignment_results(adjusted_spatial_footprints,centroid_locations,spatial_footprints_corrected,centroid_locations_corrected,adjusted_footprints_projections,footprints_projections_corrected,reference_session_index,all_projections_correlations,maximal_cross_correlation,alignment_translations,overlapping_FOV,alignment_type,number_of_cells_per_session,figures_directory,figures_visibility)
end
if number_of_sessions>2
RGB_indexes=[1 2 3];
else
RGB_indexes=[1 2];
end
axes(handles.axes1);
plot_RGB_overlay(footprints_projections_corrected,RGB_indexes,overlapping_FOV)
% saving the results into the data struct for the GUI
data_struct.reference_session_index=reference_session_index;
data_struct.alignment_type=alignment_type;
data_struct.centroid_locations=centroid_locations;
data_struct.spatial_footprints_corrected=spatial_footprints_corrected;
data_struct.centroid_locations_corrected=centroid_locations_corrected;
data_struct.adjusted_footprints_projections=adjusted_footprints_projections;
data_struct.footprints_projections_corrected=footprints_projections_corrected;
data_struct.adjusted_x_size=adjusted_x_size;
data_struct.adjusted_y_size=adjusted_y_size;
data_struct.overlapping_FOV=overlapping_FOV;
data_struct.maximal_cross_correlation=maximal_cross_correlation;
data_struct.alignment_translations=alignment_translations;
data_struct.adjustment_zero_padding=adjustment_zero_padding;
% saving the results into the aligned data structure:
aligned_data_struct.reference_session_index=reference_session_index;
aligned_data_struct.alignment_type=alignment_type;
aligned_data_struct.centroid_locations=centroid_locations;
aligned_data_struct.spatial_footprints_corrected=spatial_footprints_corrected;
aligned_data_struct.centroid_locations_corrected=centroid_locations_corrected;
aligned_data_struct.adjusted_footprints_projections=adjusted_footprints_projections;
aligned_data_struct.footprints_projections_corrected=footprints_projections_corrected;
aligned_data_struct.adjusted_x_size=adjusted_x_size;
aligned_data_struct.adjusted_y_size=adjusted_y_size;
aligned_data_struct.overlapping_FOV=overlapping_FOV;
aligned_data_struct.maximal_cross_correlation=maximal_cross_correlation;
aligned_data_struct.alignment_translations=alignment_translations;
aligned_data_struct.adjustment_zero_padding=adjustment_zero_padding;
handles.data_struct=data_struct;
disp('Saving the aligned data structure')
save(fullfile(results_directory,'aligned_data_struct.mat'),'aligned_data_struct','-v7.3')
guidata(hObject,handles)
if use_parallel_processing
delete(gcp);
end
disp('Done')
msgbox_timed('Finished aligning sessions',1)
% --- Executes on button press in compute_model.
function compute_model_Callback(hObject,~, handles)
% hObject handle to compute_model (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Stage 3: Computing a probabilistic model of the spatial footprints similarities
% of neighboring cell-pairs from different sessions using the centroid_locations
% distances and spatial correlations
% This callback computes the probability model for the same cells and
% different cells according to either spatial correlations, centroid
% distances, or both measures. The output is all the probabilities of
% neighboring cell-pairs to be the same cell - P_same.
% reseting figures
cla(handles.axes2,'reset')
axes(handles.axes2);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes3,'reset')
axes(handles.axes3);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes4,'reset')
axes(handles.axes4);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes5,'reset')
axes(handles.axes5);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes6,'reset')
axes(handles.axes6);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
data_struct=handles.data_struct;
spatial_footprints_corrected=data_struct.spatial_footprints_corrected;
centroid_locations_corrected=data_struct.centroid_locations_corrected;
microns_per_pixel=data_struct.microns_per_pixel;
results_directory=data_struct.results_directory;
figures_directory=data_struct.figures_directory;
% defining the modeled data structure:
modeled_data_struct=struct;
modeled_data_struct.number_of_sessions=data_struct.number_of_sessions;
modeled_data_struct.spatial_footprints=data_struct.spatial_footprints;
modeled_data_struct.results_directory=results_directory;
modeled_data_struct.figures_directory=figures_directory;
modeled_data_struct.microns_per_pixel=microns_per_pixel;
modeled_data_struct.reference_session_index=data_struct.reference_session_index;
modeled_data_struct.alignment_type=data_struct.alignment_type;
modeled_data_struct.centroid_locations=data_struct.centroid_locations;
modeled_data_struct.spatial_footprints_corrected=spatial_footprints_corrected;
modeled_data_struct.centroid_locations_corrected=centroid_locations_corrected;
modeled_data_struct.adjusted_footprints_projections=data_struct.adjusted_footprints_projections;
modeled_data_struct.footprints_projections_corrected=data_struct.footprints_projections_corrected;
modeled_data_struct.adjusted_x_size=data_struct.adjusted_x_size;
modeled_data_struct.adjusted_y_size=data_struct.adjusted_y_size;
modeled_data_struct.overlapping_FOV=data_struct.overlapping_FOV;
modeled_data_struct.maximal_cross_correlation=data_struct.maximal_cross_correlation;
modeled_data_struct.alignment_translations=data_struct.alignment_translations;
modeled_data_struct.adjustment_zero_padding=data_struct.adjustment_zero_padding;
modeled_data_struct.footprints_projections=data_struct.footprints_projections;
modeled_data_struct.sessions_list=data_struct.sessions_list;
modeled_data_struct.file_names=data_struct.file_names;
if get(handles.figures_visibility_on,'Value')
figures_visibility='On';
else
figures_visibility='Off';
end
% Defining the parameters for the probabilstic modeling:
maximal_distance=str2num(get(handles.model_maximal_distance,'string'));
normalized_maximal_distance=maximal_distance/microns_per_pixel;
p_same_certainty_threshold=0.95; % certain cells are those with p_same>threshld or <1-threshold
[number_of_bins,centers_of_bins]=estimate_number_of_bins(spatial_footprints_corrected,normalized_maximal_distance);
disp('Stage 3 - Calculating a probabilistic model of the data')
[all_to_all_indexes,all_to_all_spatial_correlations,all_to_all_centroid_distances,neighbors_spatial_correlations,neighbors_centroid_distances,neighbors_x_displacements,neighbors_y_displacements,NN_spatial_correlations,NNN_spatial_correlations,NN_centroid_distances,NNN_centroid_distances]=...
compute_data_distribution(spatial_footprints_corrected,centroid_locations_corrected,normalized_maximal_distance);
% saving the results into the data struct for the GUI
data_struct.all_to_all_indexes=all_to_all_indexes;
data_struct.all_to_all_spatial_correlations=all_to_all_spatial_correlations;
data_struct.all_to_all_centroid_distances=all_to_all_centroid_distances;
data_struct.neighbors_spatial_correlations=neighbors_spatial_correlations;
data_struct.neighbors_centroid_distances=neighbors_centroid_distances;
data_struct.neighbors_x_displacements=neighbors_x_displacements;
data_struct.neighbors_y_displacements=neighbors_y_displacements;
data_struct.NN_spatial_correlations=NN_spatial_correlations;
data_struct.NNN_spatial_correlations=NNN_spatial_correlations;
data_struct.NN_centroid_distances=NN_centroid_distances;
data_struct.NNN_centroid_distances=NNN_centroid_distances;
% saving the results into the modeled data structure:
modeled_data_struct.all_to_all_indexes=all_to_all_indexes;
modeled_data_struct.all_to_all_spatial_correlations=all_to_all_spatial_correlations;
modeled_data_struct.all_to_all_centroid_distances=all_to_all_centroid_distances;
modeled_data_struct.neighbors_spatial_correlations=neighbors_spatial_correlations;
modeled_data_struct.neighbors_centroid_distances=neighbors_centroid_distances;
modeled_data_struct.neighbors_x_displacements=neighbors_x_displacements;
modeled_data_struct.neighbors_y_displacements=neighbors_y_displacements;
modeled_data_struct.NN_spatial_correlations=NN_spatial_correlations;
modeled_data_struct.NNN_spatial_correlations=NNN_spatial_correlations;
modeled_data_struct.NN_centroid_distances=NN_centroid_distances;
modeled_data_struct.NNN_centroid_distances=NNN_centroid_distances;
handles.data_struct=data_struct;
guidata(hObject, handles)
% Plotting the (x,y) displacements:
x_y_displacements=plot_x_y_displacements(neighbors_x_displacements,neighbors_y_displacements,microns_per_pixel,normalized_maximal_distance,number_of_bins,centers_of_bins,figures_directory,figures_visibility);
axes(handles.axes2)
plot_x_y_displacements_GUI(x_y_displacements,microns_per_pixel,centers_of_bins,normalized_maximal_distance,number_of_bins)
disp('Calculating a probabilistic model of the data')
% Modeling the distribution of centroid distances:
[centroid_distances_model_parameters,p_same_given_centroid_distance,centroid_distances_distribution,centroid_distances_model_same_cells,centroid_distances_model_different_cells,centroid_distances_model_weighted_sum,MSE_centroid_distances_model,centroid_distance_intersection]=...
compute_centroid_distances_model(neighbors_centroid_distances,microns_per_pixel,centers_of_bins);
% Modeling the distribution of spatial correlations:
[spatial_correlations_model_parameters,p_same_given_spatial_correlation,spatial_correlations_distribution,spatial_correlations_model_same_cells,spatial_correlations_model_different_cells,spatial_correlations_model_weighted_sum,MSE_spatial_correlations_model,spatial_correlation_intersection]=...
compute_spatial_correlations_model(neighbors_spatial_correlations,centers_of_bins);
% estimating registration accuracy:
[p_same_centers_of_bins,uncertain_fraction_centroid_distances,cdf_p_same_centroid_distances,false_positive_per_distance_threshold,true_positive_per_distance_threshold,uncertain_fraction_spatial_correlations,cdf_p_same_spatial_correlations,false_positive_per_correlation_threshold,true_positive_per_correlation_threshold]=...
estimate_registration_accuracy(p_same_certainty_threshold,neighbors_centroid_distances,centroid_distances_model_same_cells,centroid_distances_model_different_cells,p_same_given_centroid_distance,centers_of_bins,neighbors_spatial_correlations,spatial_correlations_model_same_cells,spatial_correlations_model_different_cells,p_same_given_spatial_correlation);
% Checking which model is better according to a defined cost function:
[best_model_string]=choose_best_model(MSE_centroid_distances_model,centroid_distances_model_same_cells,centroid_distances_model_different_cells,p_same_given_centroid_distance,MSE_spatial_correlations_model,spatial_correlations_model_same_cells,spatial_correlations_model_different_cells,p_same_given_spatial_correlation);
% change the initial and final registration according to the best model:
if strcmp(best_model_string,'Spatial correlation')
set(handles.spatial_correlations,'Value',1);
set(handles.spatial_correlations_2,'Value',1);
set(handles.distance_threshold,'enable','off')
set(handles.correlation_threshold,'enable','on')
else
set(handles.centroid_distances,'Value',1);
set(handles.centroid_distances_2,'Value',1);
set(handles.correlation_threshold,'enable','off')
set(handles.distance_threshold,'enable','on')
end
% Plotting the probabilistic models and estimated registration accuracy:
plot_models(centroid_distances_model_parameters,NN_centroid_distances,NNN_centroid_distances,centroid_distances_distribution,centroid_distances_model_same_cells,centroid_distances_model_different_cells,centroid_distances_model_weighted_sum,centroid_distance_intersection,centers_of_bins,microns_per_pixel,normalized_maximal_distance,figures_directory,figures_visibility,spatial_correlations_model_parameters,NN_spatial_correlations,NNN_spatial_correlations,spatial_correlations_distribution,spatial_correlations_model_same_cells,spatial_correlations_model_different_cells,spatial_correlations_model_weighted_sum,spatial_correlation_intersection)
plot_estimated_registration_accuracy(p_same_centers_of_bins,p_same_certainty_threshold,p_same_given_centroid_distance,centroid_distances_distribution,cdf_p_same_centroid_distances,uncertain_fraction_centroid_distances,true_positive_per_distance_threshold,false_positive_per_distance_threshold,centers_of_bins,normalized_maximal_distance,microns_per_pixel,figures_directory,figures_visibility,p_same_given_spatial_correlation,spatial_correlations_distribution,cdf_p_same_spatial_correlations,uncertain_fraction_spatial_correlations,true_positive_per_correlation_threshold,false_positive_per_correlation_threshold)
plot_estimated_accuracy_GUI(handles,p_same_centers_of_bins,p_same_certainty_threshold,p_same_given_centroid_distance,centroid_distances_distribution,cdf_p_same_centroid_distances,true_positive_per_distance_threshold,false_positive_per_distance_threshold,centers_of_bins,normalized_maximal_distance,microns_per_pixel,p_same_given_spatial_correlation,spatial_correlations_distribution,cdf_p_same_spatial_correlations,true_positive_per_correlation_threshold,false_positive_per_correlation_threshold)
% Computing the P_same for each neighboring cell-pair according to the different models:
[all_to_all_p_same_centroid_distance_model,all_to_all_p_same_spatial_correlation_model]=...
compute_p_same(all_to_all_centroid_distances,p_same_given_centroid_distance,centers_of_bins,all_to_all_spatial_correlations,p_same_given_spatial_correlation);
% saving the results into the data struct for GUI:
data_struct.best_model_string=best_model_string;
data_struct.maximal_distance=maximal_distance;
data_struct.number_of_bins=number_of_bins;
data_struct.centers_of_bins=centers_of_bins;
data_struct.false_positive_per_distance_threshold=false_positive_per_distance_threshold;
data_struct.true_positive_per_distance_threshold=true_positive_per_distance_threshold;
data_struct.cdf_p_same_centroid_distances=cdf_p_same_centroid_distances;
data_struct.uncertain_fraction_centroid_distances=uncertain_fraction_centroid_distances;
data_struct.p_same_given_centroid_distance=p_same_given_centroid_distance;
data_struct.neighbors_centroid_distances=neighbors_centroid_distances;
data_struct.MSE_centroid_distances_model=MSE_centroid_distances_model;
data_struct.centroid_distances_model_parameters=centroid_distances_model_parameters;
data_struct.centroid_distances_distribution=centroid_distances_distribution;
data_struct.centroid_distance_intersection=centroid_distance_intersection;
data_struct.all_to_all_p_same_centroid_distance_model=all_to_all_p_same_centroid_distance_model;
% saving the results into the modeled data structure:
modeled_data_struct.best_model_string=best_model_string;
modeled_data_struct.maximal_distance=maximal_distance;
modeled_data_struct.number_of_bins=number_of_bins;
modeled_data_struct.centers_of_bins=centers_of_bins;
modeled_data_struct.false_positive_per_distance_threshold=false_positive_per_distance_threshold;
modeled_data_struct.true_positive_per_distance_threshold=true_positive_per_distance_threshold;
modeled_data_struct.cdf_p_same_centroid_distances=cdf_p_same_centroid_distances;
modeled_data_struct.uncertain_fraction_centroid_distances=uncertain_fraction_centroid_distances;
modeled_data_struct.p_same_given_centroid_distance=p_same_given_centroid_distance;
modeled_data_struct.neighbors_centroid_distances=neighbors_centroid_distances;
modeled_data_struct.MSE_centroid_distances_model=MSE_centroid_distances_model;
modeled_data_struct.centroid_distances_model_parameters=centroid_distances_model_parameters;
modeled_data_struct.centroid_distances_distribution=centroid_distances_distribution;
modeled_data_struct.centroid_distance_intersection=centroid_distance_intersection;
modeled_data_struct.all_to_all_p_same_centroid_distance_model=all_to_all_p_same_centroid_distance_model;
data_struct.false_positive_per_correlation_threshold=false_positive_per_correlation_threshold;
data_struct.true_positive_per_correlation_threshold=true_positive_per_correlation_threshold;
data_struct.cdf_p_same_spatial_correlations=cdf_p_same_spatial_correlations;
data_struct.uncertain_fraction_spatial_correlations=uncertain_fraction_spatial_correlations;
data_struct.all_to_all_spatial_correlations=all_to_all_spatial_correlations;
data_struct.MSE_spatial_correlations_model=MSE_spatial_correlations_model;
data_struct.spatial_correlations_model_parameters=spatial_correlations_model_parameters;
data_struct.p_same_given_spatial_correlation=p_same_given_spatial_correlation;
data_struct.spatial_correlations_distribution=spatial_correlations_distribution;
data_struct.spatial_correlation_intersection=spatial_correlation_intersection;
data_struct.all_to_all_p_same_spatial_correlation_model=all_to_all_p_same_spatial_correlation_model;
% saving the results into the modeled data structure:
modeled_data_struct.false_positive_per_correlation_threshold=false_positive_per_correlation_threshold;
modeled_data_struct.true_positive_per_correlation_threshold=true_positive_per_correlation_threshold;
modeled_data_struct.cdf_p_same_spatial_correlations=cdf_p_same_spatial_correlations;
modeled_data_struct.uncertain_fraction_spatial_correlations=uncertain_fraction_spatial_correlations;
modeled_data_struct.all_to_all_spatial_correlations=all_to_all_spatial_correlations;
modeled_data_struct.MSE_spatial_correlations_model=MSE_spatial_correlations_model;
modeled_data_struct.spatial_correlations_model_parameters=spatial_correlations_model_parameters;
modeled_data_struct.p_same_given_spatial_correlation=p_same_given_spatial_correlation;
modeled_data_struct.spatial_correlations_distribution=spatial_correlations_distribution;
modeled_data_struct.spatial_correlation_intersection=spatial_correlation_intersection;
modeled_data_struct.all_to_all_p_same_spatial_correlation_model=all_to_all_p_same_spatial_correlation_model;
% setting the intersection point as the threshold
set(handles.correlation_threshold,'string',num2str(spatial_correlation_intersection))
set(handles.distance_threshold,'string',num2str(centroid_distance_intersection))
handles.data_struct=data_struct;
disp('Saving the modeled data structure')
save(fullfile(results_directory,'modeled_data_struct.mat'),'modeled_data_struct','-v7.3')
guidata(hObject, handles)
disp('Done')
msgbox_timed(['Finished computing probabilistic model - The ' best_model_string ' model is best suited for the data'],3)
% --- Executes on button press in register_cells_initial.
function register_cells_initial_Callback(hObject,~,handles)
% hObject handle to register_cells_initial (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Stage 4: Obtaining an initial cell registration according to an optimized
% registration threshold.
% This callback performs initial cell registration according to either
% spatial correlations or centroid distances:
data_struct=handles.data_struct;
microns_per_pixel=data_struct.microns_per_pixel;
spatial_footprints_corrected=data_struct.spatial_footprints_corrected;
centroid_locations_corrected=data_struct.centroid_locations_corrected;
normalized_maximal_distance=data_struct.maximal_distance/microns_per_pixel;
number_of_bins=data_struct.number_of_bins;
figures_directory=data_struct.figures_directory;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
% Computing the initial registration according to a simple threshold:
if get(handles.spatial_correlations,'Value')==1 % if spatial correlations are used
initial_registration_type='Spatial correlation';
initial_threshold=str2num(get(handles.correlation_threshold,'string'));
[cell_to_index_map,registered_cells_spatial_correlations,non_registered_cells_spatial_correlations]=...
initial_registration_spatial_correlations(normalized_maximal_distance,initial_threshold,spatial_footprints_corrected,centroid_locations_corrected);
plot_initial_registration(cell_to_index_map,number_of_bins,spatial_footprints_corrected,initial_registration_type,figures_directory,figures_visibility,registered_cells_spatial_correlations,non_registered_cells_spatial_correlations)
else
initial_registration_type='Centroid distances';
initial_threshold=str2num(get(handles.distance_threshold,'string'));
centroid_distances_distribution_threshold=initial_threshold/microns_per_pixel;
[cell_to_index_map,registered_cells_centroid_distances,non_registered_cells_centroid_distances]=...
initial_registration_centroid_distances(normalized_maximal_distance,centroid_distances_distribution_threshold,centroid_locations_corrected);
plot_initial_registration(cell_to_index_map,number_of_bins,spatial_footprints_corrected,initial_registration_type,figures_directory,figures_visibility,registered_cells_centroid_distances,non_registered_cells_centroid_distances,microns_per_pixel,normalized_maximal_distance)
end
disp([num2str(size(cell_to_index_map,1)) ' cells were found'])
disp('Done')
data_struct.initial_registration_type=initial_registration_type;
data_struct.cell_to_index_map=cell_to_index_map;
data_struct.initial_threshold=initial_threshold;
data_struct.initial_registration_type=initial_registration_type;
handles.data_struct=data_struct;
guidata(hObject, handles)
msgbox_timed(['Finished performing initial cell registration - ' num2str(size(cell_to_index_map,1)) ' were found'],3)
% --- Executes on button press in register_cells_final.
function register_cells_final_Callback(hObject,~, handles)
% hObject handle to register_cells_final (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Stage 5: Obtaining the final cell registration based on a correlation clustering algorithm.
% This callback performs the final cell registration according to the
% probability model for same cells and different cells. P_same can be
% either according to centroid distances, spatial correlations or both:
data_struct=handles.data_struct;
if ~isfield(data_struct,'cell_to_index_map')
errordlg('Final registration cannot be performed before initial registration')
error('Final registration cannot be performed before initial registration')
end
if get(handles.spatial_correlations_2,'Value')==1
if isfield(data_struct,'all_to_all_p_same_spatial_correlation_model')
all_to_all_p_same_spatial_correlation_model=data_struct.all_to_all_p_same_spatial_correlation_model;
all_to_all_indexes=data_struct.all_to_all_indexes;
all_to_all_spatial_correlations=data_struct.all_to_all_spatial_correlations;
else
errordlg('Please compute the spatial correlations probability model before performing final cell registration')
error('Please compute the spatial correlations probability model before performing final cell registration')
end
elseif get(handles.centroid_distances_2,'Value')==1
if isfield(data_struct,'all_to_all_p_same_centroid_distance_model')
all_to_all_p_same_centroid_distance_model=data_struct.all_to_all_p_same_centroid_distance_model;
all_to_all_indexes=data_struct.all_to_all_indexes;
all_to_all_centroid_distances=data_struct.all_to_all_centroid_distances;
end
end
results_directory=data_struct.results_directory;
figures_directory=data_struct.figures_directory;
overlapping_FOV=data_struct.overlapping_FOV;
cell_to_index_map=data_struct.cell_to_index_map;
centroid_locations_corrected=data_struct.centroid_locations_corrected;
spatial_footprints_corrected=data_struct.spatial_footprints_corrected;
number_of_sessions=data_struct.number_of_sessions;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance;
normalized_maximal_distance=maximal_distance/microns_per_pixel;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
if get(handles.spatial_correlations_2,'Value')==1;
model_type='Spatial correlation';
else
model_type='Centroid distance';
end
transform_data=false;
if get(handles.use_model,'Value')==1;
p_same_threshold=str2num(get(handles.decision_thresh,'string'));
final_threshold=p_same_threshold;
registration_approach='Probabilistic';
else
registration_approach='Simple threshold';
if strcmp(model_type,'Spatial correlation')
final_threshold=str2num(get(handles.simple_correlation_threshold,'string'));
elseif strcmp(model_type,'Centroid distance')
final_threshold=str2num(get(handles.simple_distance_threshold,'string'));
centroid_distances_distribution_threshold=(maximal_distance-final_threshold)/maximal_distance;
transform_data=true;
end
end
data_struct.final_threshold=final_threshold;
data_struct.registration_approach=registration_approach;
data_struct.model_type=model_type;
% Registering the cells with the clustering algorithm:
disp('Stage 5 - Performing final registration')
if strcmp(registration_approach,'Probabilistic')
if strcmp(model_type,'Spatial correlation')
[optimal_cell_to_index_map,registered_cells_centroids,cell_scores,cell_scores_positive,cell_scores_negative,cell_scores_exclusive,p_same_registered_pairs]=...
cluster_cells(cell_to_index_map,all_to_all_p_same_spatial_correlation_model,all_to_all_indexes,normalized_maximal_distance,p_same_threshold,centroid_locations_corrected,registration_approach,transform_data);
elseif strcmp(model_type,'Centroid distance')
[optimal_cell_to_index_map,registered_cells_centroids,cell_scores,cell_scores_positive,cell_scores_negative,cell_scores_exclusive,p_same_registered_pairs]=...
cluster_cells(cell_to_index_map,all_to_all_p_same_centroid_distance_model,all_to_all_indexes,normalized_maximal_distance,p_same_threshold,centroid_locations_corrected,registration_approach,transform_data);
end
plot_cell_scores(cell_scores_positive,cell_scores_negative,cell_scores_exclusive,cell_scores,p_same_registered_pairs,figures_directory,figures_visibility)
elseif strcmp(registration_approach,'Simple threshold')
if strcmp(model_type,'Spatial correlation')
[optimal_cell_to_index_map,registered_cells_centroids]=...
cluster_cells(cell_to_index_map,all_to_all_spatial_correlations,all_to_all_indexes,normalized_maximal_distance,final_threshold,centroid_locations_corrected,registration_approach,transform_data);
elseif strcmp(model_type,'Centroid distance')
[optimal_cell_to_index_map,registered_cells_centroids]=...
cluster_cells(cell_to_index_map,all_to_all_centroid_distances,all_to_all_indexes,normalized_maximal_distance,centroid_distances_distribution_threshold,centroid_locations_corrected,registration_approach,transform_data);
end
end
[is_in_overlapping_FOV]=check_if_in_overlapping_FOV(registered_cells_centroids,overlapping_FOV);
% Plotting the registration results with the cell maps from all sessions:
plot_all_registered_projections(spatial_footprints_corrected,optimal_cell_to_index_map,figures_directory,figures_visibility)
% saving the clustering results:
disp('Saving the results')
cell_registered_struct=struct;
cell_registered_struct.cell_to_index_map=optimal_cell_to_index_map;
if strcmp(registration_approach,'Probabilistic');
cell_registered_struct.cell_scores=cell_scores';
cell_registered_struct.true_positive_scores=cell_scores_positive';
cell_registered_struct.true_negative_scores=cell_scores_negative';
cell_registered_struct.exclusivity_scores=cell_scores_exclusive';
cell_registered_struct.p_same_registered_pairs=p_same_registered_pairs';
end
cell_registered_struct.is_cell_in_overlapping_FOV=is_in_overlapping_FOV';
cell_registered_struct.registered_cells_centroids=registered_cells_centroids';
cell_registered_struct.centroid_locations_corrected=centroid_locations_corrected';
cell_registered_struct.spatial_footprints_corrected=spatial_footprints_corrected';
cell_registered_struct.alignment_x_translations=data_struct.alignment_translations(1,:);
cell_registered_struct.alignment_y_translations=data_struct.alignment_translations(2,:);
if strcmp(data_struct.alignment_type,'Translations and Rotations')
cell_registered_struct.alignment_rotations=data_struct.alignment_translations(3,:);
end
cell_registered_struct.adjustment_x_zero_padding=data_struct.adjustment_zero_padding(1,:);
cell_registered_struct.adjustment_y_zero_padding=data_struct.adjustment_zero_padding(2,:);
save(fullfile(results_directory,['cellRegistered_' datestr(clock,'yyyymmdd_HHMMss') '.mat']),'cell_registered_struct','-v7.3')
% Saving a log file with all the chosen parameters:
comments=get(handles.comments,'string');
file_names=data_struct.file_names;
adjusted_x_size=data_struct.adjusted_x_size;
adjusted_y_size=data_struct.adjusted_y_size;
alignment_type=data_struct.alignment_type;
reference_session_index=data_struct.reference_session_index;
number_of_bins=data_struct.number_of_bins;
initial_registration_type=data_struct.initial_registration_type;
initial_threshold=data_struct.initial_threshold;
if strcmp(registration_approach,'Probabilistic')
if strcmp(model_type,'Spatial correlation')
uncertain_fraction_spatial_correlations=data_struct.uncertain_fraction_spatial_correlations;
false_positive_per_correlation_threshold=data_struct.false_positive_per_correlation_threshold;
true_positive_per_correlation_threshold=data_struct.true_positive_per_correlation_threshold;
MSE_spatial_correlations_model=data_struct.MSE_spatial_correlations_model;
save_log_file(results_directory,file_names,microns_per_pixel,adjusted_x_size,adjusted_y_size,alignment_type,reference_session_index,maximal_distance,number_of_bins,initial_registration_type,initial_threshold,registration_approach,model_type,final_threshold,optimal_cell_to_index_map,cell_registered_struct,comments,uncertain_fraction_spatial_correlations,false_positive_per_correlation_threshold,true_positive_per_correlation_threshold,MSE_spatial_correlations_model)
elseif strcmp(model_type,'Centroid distance')
uncertain_fraction_centroid_distances=data_struct.uncertain_fraction_centroid_distances;
false_positive_per_distance_threshold=data_struct.false_positive_per_distance_threshold;
true_positive_per_distance_threshold=data_struct.true_positive_per_distance_threshold;
MSE_centroid_distances_model=data_struct.MSE_centroid_distances_model;
save_log_file(results_directory,file_names,microns_per_pixel,adjusted_x_size,adjusted_y_size,alignment_type,reference_session_index,maximal_distance,number_of_bins,initial_registration_type,initial_threshold,registration_approach,model_type,final_threshold,optimal_cell_to_index_map,cell_registered_struct,comments,uncertain_fraction_centroid_distances,false_positive_per_distance_threshold,true_positive_per_distance_threshold,MSE_centroid_distances_model)
end
elseif strcmp(registration_approach,'Simple threshold')
if strcmp(model_type,'Spatial correlation')
save_log_file(results_directory,file_names,microns_per_pixel,adjusted_x_size,adjusted_y_size,alignment_type,reference_session_index,maximal_distance,number_of_bins,initial_registration_type,initial_threshold,registration_approach,model_type,final_threshold,optimal_cell_to_index_map,cell_registered_struct,comments)
elseif strcmp(model_type,'Centroid distance')
save_log_file(results_directory,file_names,microns_per_pixel,adjusted_x_size,adjusted_y_size,alignment_type,reference_session_index,maximal_distance,number_of_bins,initial_registration_type,initial_threshold,registration_approach,model_type,final_threshold,optimal_cell_to_index_map,cell_registered_struct,comments)
end
end
if ~isempty(data_struct.temp_dir)
for file_n = 1:length(spatial_footprints_corrected)
split_name = strsplit(spatial_footprints_corrected{file_n},filesep);
f_name = split_name{end};
footprints = get_spatial_footprints(spatial_footprints_corrected{file_n});
footprints = footprints.load_footprints;
footprints = footprints.footprints;
footprint = mat_to_sparse_cell(footprints);
spatial_footprints_corrected{file_n} = fullfile(results_directory,f_name);
save(fullfile(results_directory,f_name),'footprint');
end
rmdir(data_struct.temp_dir,'s');
end
disp([num2str(size(optimal_cell_to_index_map,1)) ' cells were found'])
disp('End of cell registration procedure')
handles.data_struct=data_struct;
guidata(hObject, handles)
msgbox_timed(['Finished performing final cell registration - ' num2str(size(optimal_cell_to_index_map,1)) ' were found'],3)
% --- Executes on button press in reset.
function reset_Callback(hObject,~, handles)
% hObject handle to reset (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% defining data struct:
guidata(hObject, handles);
data_struct=struct;
data_struct.sessions_list=[];
% Reseting Figures and GUI parameters:
cla(handles.axes1,'reset')
axes(handles.axes1);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes2,'reset')
axes(handles.axes2);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes3,'reset')
axes(handles.axes3);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes4,'reset')
axes(handles.axes4);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes5,'reset')
axes(handles.axes5);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
cla(handles.axes6,'reset')
axes(handles.axes6);
plot([],[])
set(gca,'xtick',[])
set(gca,'ytick',[])
set(handles.list_of_sessions,'value',1)
set(handles.list_of_sessions,'string',[]);
set(handles.red_session,'string','1')
set(handles.green_session,'string','2')
set(handles.blue_session,'string','3')
set(handles.decision_thresh,'string','0.5')
set(handles.initial_p_same_slider,'value',0.5);
set(handles.initial_p_same_threshold,'string','0.5');
set(handles.final_p_same_slider,'value',0.5);
set(handles.model_maximal_distance,'string','14')
set(handles.distance_threshold,'string','5')
set(handles.correlation_threshold,'string','0.65')
set(handles.simple_distance_threshold,'string','5')
set(handles.simple_correlation_threshold,'string','0.65')
set(handles.figures_visibility_on,'Value',1);
set(handles.translations_rotations,'Value',1);
set(handles.spatial_correlations_2,'Value',1);
set(handles.use_model,'Value',1);
set(handles.spatial_correlations,'Value',1);
set(handles.microns_per_pixel,'value',0)
set(handles.microns_per_pixel,'string',[])
set(handles.microns_per_pixel,'backgroundColor',[1 1 1]);
set(handles.reference_session_index,'string','1')
set(handles.maximal_rotation','string','30')
set(handles.maximal_rotation','enable','on')
set(handles.transformation_smoothness,'string','2')
set(handles.transformation_smoothness,'enable','off')
set(handles.distance_threshold,'enable','off')
set(handles.correlation_threshold,'enable','on')
set(handles.simple_distance_threshold,'enable','off')
set(handles.simple_correlation_threshold,'enable','off')
set(handles.comments,'string',[])
handles.data_struct=data_struct;
guidata(hObject, handles)
% --- Executes during object creation, after setting all properties.
function correlation_threshold_CreateFcn(~,hObject,~)
% hObject handle to correlation_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function distance_threshold_CreateFcn(hObject,~,~)
% hObject handle to distance_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function list_of_sessions_CreateFcn(hObject, ~,~)
% hObject handle to list_of_sessions (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: listbox controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function maximal_distance_CreateFcn(hObject,~,~)
% hObject handle to maximal_distance (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function number_of_bins_CreateFcn(hObject,~,~)
% hObject handle to number_of_bins (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function compute_model_CreateFcn(~,~,~)
% hObject handle to compute_model (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% --- Executes during object creation, after setting all properties.
function transformation_type_CreateFcn(~,~,~)
% hObject handle to transformation_type (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
function microns_per_pixel_Callback(~,~, handles)
% hObject handle to microns_per_pixel (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of microns_per_pixel as text
% str2num(get(hObject,'String')) returns contents of microns_per_pixel as a double
microns_per_pixel=get(handles.microns_per_pixel,'string');
if ~isempty(microns_per_pixel)
set(handles.microns_per_pixel,'value',1)
end
% --- Executes during object creation, after setting all properties.
function microns_per_pixel_CreateFcn(hObject,~,~)
% hObject handle to microns_per_pixel (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function reference_session_index_Callback(~,~, handles)
% hObject handle to reference_session_index (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of reference_session_index as text
% str2num(get(hObject,'String')) returns contents of reference_session_index as a double
reference_session_index=get(handles.reference_session_index,'string');
if ~isempty(reference_session_index)
set(handles.reference_session_index,'value',1)
end
% --- Executes during object creation, after setting all properties.
function reference_session_index_CreateFcn(hObject, ~, ~)
% hObject handle to reference_session_index (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function model_maximal_distance_CreateFcn(hObject,~,~)
% hObject handle to model_maximal_distance (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function cluster_maximal_distance_CreateFcn(hObject,~,~)
% hObject handle to cluster_maximal_distance (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function max_iterations_CreateFcn(hObject,~,~)
% hObject handle to max_iterations (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function decision_thresh_CreateFcn(hObject,~,~)
% hObject handle to decision_thresh (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function load_Callback(~,~,~)
% hObject handle to load (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% --- Executes on selection change in list_of_sessions.
function list_of_sessions_Callback(hObject, eventdata, handles)
% hObject handle to list_of_sessions (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns list_of_sessions contents as cell array
% contents{get(hObject,'Value')} returns selected item from list_of_sessions
function model_maximal_distance_Callback(hObject, eventdata, handles)
% hObject handle to model_maximal_distance (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of model_maximal_distance as text
% str2num(get(hObject,'String')) returns contents of model_maximal_distance as a double
function number_of_bins_Callback(hObject, eventdata, handles)
% hObject handle to number_of_bins (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of number_of_bins as text
% str2num(get(hObject,'String')) returns contents of number_of_bins as a double
% --- Executes when selected object is changed in transformation_type.
function transformation_type_SelectionChangeFcn(hObject, eventdata, handles)
% hObject handle to the selected object in transformation_type
% eventdata struct with the following fields (see UIBUTTONGROUP)
% EventName: string 'SelectionChanged' (read only)
% OldValue: handle of the previously selected object or empty if none was selected
% NewValue: handle of the currently selected object
% handles struct with handles and user data (see GUIDATA)
if get(handles.translations,'Value')==1;
set(handles.maximal_rotation','enable','off')
else
set(handles.maximal_rotation','enable','on')
set(handles.maximal_rotation','string','30')
end
function maximal_rotation_Callback(hObject, eventdata, handles)
% hObject handle to maximal_rotation (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of maximal_rotation as text
% str2num(get(hObject,'String')) returns contents of maximal_rotation as a double
% --- Executes during object creation, after setting all properties.
function maximal_rotation_CreateFcn(hObject, eventdata, handles)
% hObject handle to maximal_rotation (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes during object creation, after setting all properties.
function figure1_CreateFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% --- Executes when selected object is changed in initial_register_select.
function initial_register_select_SelectionChangedFcn(hObject, eventdata, handles)
% hObject handle to the selected object in initial_register_select
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
if get(handles.spatial_correlations,'Value')==1;
set(handles.distance_threshold,'enable','off')
set(handles.correlation_threshold,'enable','on')
else
set(handles.correlation_threshold,'enable','off')
set(handles.distance_threshold,'enable','on')
end
function comments_Callback(hObject, eventdata, handles)
% hObject handle to comments (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of comments as text
% str2num(get(hObject,'String')) returns contents of comments as a double
% --- Executes during object creation, after setting all properties.
function comments_CreateFcn(hObject, eventdata, handles)
% hObject handle to comments (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in display_rgb.
function display_rgb_Callback(hObject, eventdata, handles)
% hObject handle to display_rgb (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
data_struct=handles.data_struct;
if ~isfield(data_struct,'footprints_projections_corrected')
errordlg('RGB overlay cannot be displayed before transformation is performed')
else
spatial_footprints_projections=data_struct.footprints_projections_corrected;
overlapping_FOV=data_struct.overlapping_FOV;
if get(handles.figures_visibility_on,'Value');
figures_visibility='On';
else
figures_visibility='Off';
end
number_of_sessions=data_struct.number_of_sessions;
red_session=str2num(get(handles.red_session,'string'));
green_session=str2num(get(handles.green_session,'string'));
if number_of_sessions>2
blue_session=str2num(get(handles.blue_session,'string'));
RGB_indexes=[red_session green_session blue_session];
else
RGB_indexes=[red_session green_session];
end
axes(handles.axes1);
plot_RGB_overlay(spatial_footprints_projections,RGB_indexes,overlapping_FOV)
figure('units','normalized','outerposition',[0.325 0.25 0.35 0.5],'Visible',figures_visibility)
plot_RGB_overlay(spatial_footprints_projections,RGB_indexes,overlapping_FOV)
end
handles.data_struct=data_struct;
guidata(hObject, handles)
function red_session_Callback(hObject, eventdata, handles)
% hObject handle to red_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of red_session as text
% str2num(get(hObject,'String')) returns contents of red_session as a double
% --- Executes during object creation, after setting all properties.
function red_session_CreateFcn(hObject, eventdata, handles)
% hObject handle to red_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function green_session_Callback(hObject, eventdata, handles)
% hObject handle to green_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of green_session as text
% str2num(get(hObject,'String')) returns contents of green_session as a double
% --- Executes during object creation, after setting all properties.
function green_session_CreateFcn(hObject, eventdata, handles)
% hObject handle to green_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function blue_session_Callback(hObject, eventdata, handles)
% hObject handle to blue_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of blue_session as text
% str2num(get(hObject,'String')) returns contents of blue_session as a double
% --- Executes during object creation, after setting all properties.
function blue_session_CreateFcn(hObject, eventdata, handles)
% hObject handle to blue_session (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on slider movement.
function initial_p_same_slider_Callback(hObject, eventdata, handles)
% hObject handle to initial_p_same_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
data_struct=handles.data_struct;
initial_p_same_slider_value=round(100*get(handles.initial_p_same_slider,'value'))/100;
set(handles.initial_p_same_threshold,'string',num2str(initial_p_same_slider_value));
centers_of_bins=data_struct.centers_of_bins;
if get(handles.spatial_correlations,'Value')==1
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
else
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
end
handles.data_struct=data_struct;
guidata(hObject, handles)
% --- Executes during object creation, after setting all properties.
function initial_p_same_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to initial_p_same_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function initial_p_same_threshold_Callback(hObject, eventdata, handles)
% hObject handle to initial_all_to_all_centroid_distances (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of initial_all_to_all_centroid_distances as text
% str2num(get(hObject,'String')) returns contents of initial_all_to_all_centroid_distances as a double
data_struct=handles.data_struct;
initial_p_same_slider_value=str2num(get(handles.initial_p_same_threshold,'string'));
set(handles.initial_p_same_slider,'value',initial_p_same_slider_value);
centers_of_bins=data_struct.centers_of_bins;
if get(handles.spatial_correlations,'Value')==1
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
else
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
end
handles.data_struct=data_struct;
guidata(hObject, handles)
% --- Executes during object creation, after setting all properties.
function initial_p_same_threshold_CreateFcn(hObject, eventdata, handles)
% hObject handle to initial_all_to_all_centroid_distances (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in default_initial_registration.
function default_initial_registration_Callback(hObject, eventdata, handles)
% hObject handle to default_initial_registration (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
initial_p_same_slider_value=0.5;
data_struct=handles.data_struct;
set(handles.initial_p_same_slider,'value',initial_p_same_slider_value);
set(handles.initial_p_same_threshold,'string',num2str(initial_p_same_slider_value));
centers_of_bins=data_struct.centers_of_bins;
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(initial_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
handles.data_struct=data_struct;
guidata(hObject, handles)
% --- Executes on slider movement.
function final_p_same_slider_Callback(hObject, eventdata, handles)
% hObject handle to final_p_same_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
if get(handles.use_model,'Value')==1;
data_struct=handles.data_struct;
final_p_same_slider_value=round(100*get(handles.final_p_same_slider,'value'))/100;
set(handles.decision_thresh,'string',num2str(final_p_same_slider_value));
centers_of_bins=data_struct.centers_of_bins;
if get(handles.spatial_correlations_2,'Value')==1
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
else
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
end
handles.data_struct=data_struct;
guidata(hObject, handles)
end
% --- Executes during object creation, after setting all properties.
function final_p_same_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to final_p_same_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
% --- Executes on button press in default_final_registration.
function default_final_registration_Callback(hObject, eventdata, handles)
% hObject handle to default_final_registration (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
if get(handles.use_model,'Value')==1;
final_p_same_slider_value=0.5;
data_struct=handles.data_struct;
set(handles.final_p_same_slider,'value',final_p_same_slider_value);
set(handles.decision_thresh,'string',num2str(final_p_same_slider_value));
optimal_threshold=data_struct.spatial_correlation_intersection;
set(handles.simple_correlation_threshold,'string',optimal_threshold);
centers_of_bins=data_struct.centers_of_bins;
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
handles.data_struct=data_struct;
guidata(hObject, handles)
end
function decision_thresh_Callback(hObject, eventdata, handles)
% hObject handle to decision_thresh (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of decision_thresh as text
% str2num(get(hObject,'String')) returns contents of decision_thresh as a double
if get(handles.use_model,'Value')==1;
data_struct=handles.data_struct;
final_p_same_slider_value=str2num(get(handles.decision_thresh,'string'));
set(handles.final_p_same_slider,'value',final_p_same_slider_value);
centers_of_bins=data_struct.centers_of_bins;
if get(handles.spatial_correlations_2,'Value')==1
spatial_correlations_distribution=data_struct.spatial_correlations_distribution;
p_same_given_spatial_correlation=data_struct.p_same_given_spatial_correlation;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_spatial_correlation)));
spatial_correlation_threshold=round(100*centers_of_bins{2}(p_same_ind))/100;
set(handles.correlation_threshold,'string',num2str(spatial_correlation_threshold));
axes(handles.axes4)
cla(handles.axes4,'reset')
plot_p_same_spatial_correlation_slider(spatial_correlations_distribution,p_same_given_spatial_correlation,spatial_correlation_threshold,centers_of_bins)
else
centroid_distances_distribution=data_struct.centroid_distances_distribution;
p_same_given_centroid_distance=data_struct.p_same_given_centroid_distance;
microns_per_pixel=data_struct.microns_per_pixel;
maximal_distance=data_struct.maximal_distance/microns_per_pixel;
[~,p_same_ind]=min(abs(final_p_same_slider_value-(p_same_given_centroid_distance)));
centroid_distance_threshold=round(microns_per_pixel*100*centers_of_bins{1}(p_same_ind))/100;
set(handles.distance_threshold,'string',num2str(centroid_distance_threshold));
axes(handles.axes3)
cla(handles.axes3,'reset')
plot_p_same_centroid_distance_slider(centroid_distances_distribution,p_same_given_centroid_distance,centroid_distance_threshold,centers_of_bins,maximal_distance,microns_per_pixel)
end
handles.data_struct=data_struct;
guidata(hObject, handles)
end
% --- Executes on button press in use_simple_threshold.
function use_simple_threshold_Callback(hObject, eventdata, handles)
% hObject handle to use_simple_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of use_simple_threshold
% --- Executes on button press in use_model.
function use_model_Callback(hObject, eventdata, handles)
% hObject handle to use_model (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of use_model
% --- Executes during object creation, after setting all properties.
function Registration_approach_CreateFcn(hObject, eventdata, handles)
% hObject handle to Registration_approach (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% --- Executes when selected object is changed in Registration_approach.
function Registration_approach_SelectionChangeFcn(hObject, eventdata, handles)
% hObject handle to the selected object in transformation_type
% eventdata struct with the following fields (see UIBUTTONGROUP)
% EventName: string 'SelectionChanged' (read only)
% OldValue: handle of the previously selected object or empty if none was selected
% NewValue: handle of the currently selected object
% handles struct with handles and user data (see GUIDATA)
if get(handles.use_model,'Value')==1;
set(handles.use_simple_threshold,'value',0)
set(handles.decision_thresh,'enable','on')
set(handles.simple_correlation_threshold,'enable','off')
set(handles.simple_distance_threshold,'enable','off')
elseif get(handles.use_simple_threshold,'Value')==1;
set(handles.use_model,'value',0)
set(handles.decision_thresh,'enable','off')
if get(handles.spatial_correlations_2,'value')==1
set(handles.simple_correlation_threshold,'enable','on')
set(handles.simple_distance_threshold,'enable','off')
elseif get(handles.centroid_distances_2,'value')==1
set(handles.simple_correlation_threshold,'enable','off')
set(handles.simple_distance_threshold,'enable','on')
end
end
function simple_correlation_threshold_Callback(hObject, eventdata, handles)
% hObject handle to simple_correlation_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of simple_correlation_threshold as text
% str2num(get(hObject,'String')) returns contents of simple_correlation_threshold as a double
% --- Executes during object creation, after setting all properties.
function simple_correlation_threshold_CreateFcn(hObject, eventdata, handles)
% hObject handle to simple_correlation_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes when selected object is changed in probability_model_select.
function probability_model_select_SelectionChangedFcn(hObject, eventdata, handles)
% hObject handle to the selected object in probability_model_select
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
if get(handles.use_model,'Value')==1;
set(handles.use_simple_threshold,'value',0)
set(handles.decision_thresh,'enable','on')
set(handles.simple_correlation_threshold,'enable','off')
set(handles.simple_distance_threshold,'enable','off')
elseif get(handles.use_simple_threshold,'Value')==1;
set(handles.use_model,'value',0)
set(handles.decision_thresh,'enable','off')
if get(handles.spatial_correlations_2,'value')==1
set(handles.simple_correlation_threshold,'enable','on')
set(handles.simple_distance_threshold,'enable','off')
elseif get(handles.centroid_distances_2,'value')==1
set(handles.simple_correlation_threshold,'enable','off')
set(handles.simple_distance_threshold,'enable','on')
end
end
function simple_distance_threshold_Callback(hObject, eventdata, handles)
% hObject handle to simple_distance_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles struct with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of simple_distance_threshold as text
% str2num(get(hObject,'String')) returns contents of simple_distance_threshold as a double
% --- Executes during object creation, after setting all properties.
function simple_distance_threshold_CreateFcn(hObject, eventdata, handles)
% hObject handle to simple_distance_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function distance_threshold_Callback(hObject, eventdata, handles)
% hObject handle to distance_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of distance_threshold as text
% str2double(get(hObject,'String')) returns contents of distance_threshold as a double
function correlation_threshold_Callback(hObject, eventdata, handles)
% hObject handle to correlation_threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of correlation_threshold as text
% str2double(get(hObject,'String')) returns contents of correlation_threshold as a double
% --- Executes on button press in non_rigid.
function non_rigid_Callback(hObject, eventdata, handles)
% hObject handle to non_rigid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of non_rigid
if get(handles.non_rigid,'Value')==1
set(handles.maximal_rotation,'enable','off')
set(handles.transformation_smoothness,'enable','on')
end
function transformation_smoothness_Callback(hObject, eventdata, handles)
% hObject handle to transformation_smoothness (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of transformation_smoothness as text
% str2double(get(hObject,'String')) returns contents of transformation_smoothness as a double
transformation_smoothness=str2num(get(handles.transformation_smoothness,'string'));
if transformation_smoothness>3 || transformation_smoothness<0.5
errordlg('FOV smoothing parameter should be between 0.5-3')
error('FOV smoothing parameter should be between 0.5-3')
end
% --- Executes during object creation, after setting all properties.
function transformation_smoothness_CreateFcn(hObject, eventdata, handles)
% hObject handle to transformation_smoothness (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in translations.
function translations_Callback(hObject, eventdata, handles)
% hObject handle to translations (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of translations
if get(handles.translations,'Value')==1
set(handles.maximal_rotation,'enable','off')
set(handles.transformation_smoothness,'enable','off')
end
% --- Executes on button press in translations_rotations.
function translations_rotations_Callback(hObject, eventdata, handles)
% hObject handle to translations_rotations (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of translations_rotations
if get(handles.translations_rotations,'Value')==1
set(handles.maximal_rotation,'enable','on')
set(handles.transformation_smoothness,'enable','off')
end

CellReg.m at commit 7066851, under GPL-2.0 · at the source

Overview

Authors: Wei Zheng¹, Xiaoxing Liu², Tangsheng Lu³, Xinyou Lv⁴, Xuefang Guan⁵, Yifan Yu², Xue Li³, Zhe Wang⁶, Kai Yuan², Jeffrey W Grimm⁷, Trevor W Robbins^8,9, Jie Shi³, Lin Lu^1,2,3,6, Yan-Xue Xue^3,10

ORCID iDs: Wei Zheng, Xiaoxing Liu, Xuefang Guan, Yifan Yu, Trevor W Robbins, Jie Shi, Lin Lu, Yan-Xue Xue

Institutions: Peking University (China); Center for Life Sciences (China); Peking University Sixth Hospital (China); University of Science and Technology of China (China); Henan University (China); Shandong First Medical University (China); Western Washington University (United States); University of Cambridge (United Kingdom); Fudan University (China); Frontiers Center for Brain Science of the Ministry of Education (China); Chinese Institute for Brain Research (China)

Journal: Nature communications, volume 17, issue 1, article 3462

Dates: received 3 October 2025; accepted 17 March 2026; published online 3 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-71357-4 · PMID 41932919 · PMCID PMC13076667 · OpenAlex W7148871776

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: rat (organism), other condition (population), systems (subfield)

Methods: Connectivity, Statistics, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging

Keywords: Reward, Addiction

MeSH: Dopaminergic Neurons*, Drug-Seeking Behavior*, Reward*, Social Behavior*, Substance-Related Disorders*, Ventral Tegmental Area*, Animals, Dopamine, Dorsal Raphe Nucleus, Male, Mesolimbic System, Optogenetics, Rats, Rats, Sprague-Dawley (* major topic)

Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (National Science Foundation of China) (82071498)

Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Drugs of abuse promote substance use disorder (SUD) by hijacking mesolimbic circuits that normally process natural rewards. Among these, social rewards exhibit therapeutic potential, but the underlying neural substrates remain unclear. Using a multimodal approach integrating in vivo single-neuron calcium imaging, optogenetic manipulation, and electrophysiology in male rats, we identified two distinct dopaminergic ensembles in the ventral tegmental area (VTA) that respectively encode social reward and drug seeking. Notably, these antagonistic ensembles exert reciprocal influence through competitive interactions that shape behavioral outcomes. Furthermore, circuit mapping revealed divergent connectivity patterns, with social reward-responsive dopaminergic ensembles receiving preferential input from the dorsal raphe nucleus (DRN). Activation of the DRN-VTA pathway recapitulates the protective effects of social reward against drug seeking. In this study, we uncovered a dynamic competition between functionally specialized dopaminergic ensembles through which social reward attenuates drug seeking, offering insights that may inform development of novel strategies for SUD treatment.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

zhoupc/CNMF_E

License: GPL-3.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 0f49eb08186c6aca2df0fd4873ec733d0810f1a2, 2 August 2024

Languages: MATLAB (260), Python (2), C++ (1), Jupyter (1)

Size: 334 files, 264 scripts

Software Heritage: not archived

Found in: the text, “Calcium imaging pre-processing and fluorescence ”

Holds: README, license file, 1 notebook

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Image Processing Toolbox (41 files), Signal Processing Toolbox (8 files), Statistics and Machine Learning Toolbox (6 files), Optimization Toolbox (4 files), Matplotlib (2 files), NumPy (2 files), SciPy (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

266 files

GUI/cnmfe.m, MATLAB, 33 lines
GUI/cnmfe_main.m, MATLAB, 15 lines
GUI/gui_callbacks/edit_dir_callback.m, MATLAB, 10 lines
GUI/gui_callbacks/edit_file_callback.m, MATLAB, 29 lines
GUI/gui_callbacks/edit_gsig_callback.m, MATLAB, 6 lines
GUI/gui_callbacks/edit_gsiz_callback.m, MATLAB, 6 lines
GUI/gui_callbacks/gui_load_mat.m, MATLAB, 25 lines
GUI/gui_callbacks/push_corr_callback.m, MATLAB, 19 lines
GUI/gui_callbacks/push_dir_callback.m, MATLAB, 11 lines
GUI/gui_callbacks/push_file_callback.m, MATLAB, 39 lines
GUI/gui_callbacks/push_init_callback.m, MATLAB, 17 lines
GUI/gui_callbacks/push_load_callback.m, MATLAB, 22 lines
GUI/gui_callbacks/radio_dilate_callback.m, MATLAB, 18 lines
GUI/gui_callbacks/radio_ellipse_callback.m, MATLAB, 16 lines
GUI/modules/cnmfe_background.m, MATLAB, 273 lines
GUI/modules/cnmfe_data.m, MATLAB, 231 lines
GUI/modules/cnmfe_init.m, MATLAB, 224 lines
GUI/modules/cnmfe_load.m, MATLAB, 240 lines
GUI/modules/cnmfe_neuron.m, MATLAB, 185 lines
OASIS_matlab/deconvolveCa.m, MATLAB, 355 lines
OASIS_matlab/examples/Paper/fig1.m, MATLAB, 36 lines
OASIS_matlab/examples/Paper/fig2.m, MATLAB, 17 lines
OASIS_matlab/examples/Paper/fig3.m, MATLAB, 144 lines
OASIS_matlab/examples/Paper/fig4.m, MATLAB, 150 lines
OASIS_matlab/examples/Paper/fig5.m, MATLAB, 191 lines
OASIS_matlab/examples/Paper/fig6.m, MATLAB, 7 lines
OASIS_matlab/examples/Paper/scripts/compute_rss_g.m, MATLAB, 65 lines
OASIS_matlab/examples/Paper/scripts/fig2_demo_deconvolveAR1.m, MATLAB, 222 lines
OASIS_matlab/examples/Paper/scripts/fig4_plot_trace.m, MATLAB, 16 lines
OASIS_matlab/examples/Paper/scripts/show_results.m, MATLAB, 29 lines
OASIS_matlab/examples/Paper/test_all.m, MATLAB, 8 lines
OASIS_matlab/examples/ar1_constrained_foopsi.m, MATLAB, 74 lines
OASIS_matlab/examples/ar1_foopsi.m, MATLAB, 47 lines
OASIS_matlab/examples/ar1_mcmc.m, MATLAB, 27 lines
OASIS_matlab/examples/ar1_thresholded_foopsi.m, MATLAB, 38 lines
OASIS_matlab/examples/ar2_foopsi.m, MATLAB, 36 lines
OASIS_matlab/examples/ar2_mcmc.m, MATLAB, 26 lines
OASIS_matlab/examples/ar2_thresholded_foopsi.m, MATLAB, 54 lines
OASIS_matlab/examples/foopsi_kernel.m, MATLAB, 157 lines
OASIS_matlab/examples/foopsi_onnls.m, MATLAB, 190 lines
OASIS_matlab/examples/kernel_foopsi.m, MATLAB, 63 lines
OASIS_matlab/examples/kernel_thresholded_foopsi.m, MATLAB, 39 lines
OASIS_matlab/examples/show_results.m, MATLAB, 39 lines
OASIS_matlab/examples/test_all.m, MATLAB, 85 lines
OASIS_matlab/functions/GetSn.m, MATLAB, 50 lines
OASIS_matlab/functions/ar2exp.m, MATLAB, 13 lines
OASIS_matlab/functions/choose_smin.m, MATLAB, 42 lines
OASIS_matlab/functions/constrained_foopsi_cvx.m, MATLAB, 58 lines
OASIS_matlab/functions/estimate_baseline_noise.m, MATLAB, 35 lines
OASIS_matlab/functions/estimate_parameters.m, MATLAB, 44 lines
OASIS_matlab/functions/estimate_time_constant.m, MATLAB, 67 lines
OASIS_matlab/functions/exp2ar.m, MATLAB, 8 lines
OASIS_matlab/functions/exp2kernel.m, MATLAB, 17 lines
OASIS_matlab/functions/fit_gauss1.m, MATLAB, 83 lines
OASIS_matlab/functions/foopsi.m, MATLAB, 59 lines
OASIS_matlab/functions/gen_data.m, MATLAB, 62 lines
OASIS_matlab/functions/gen_sinusoidal_data.m, MATLAB, 63 lines
OASIS_matlab/functions/init_fig.m, MATLAB, 17 lines
OASIS_matlab/functions/max_ht.m, MATLAB, 28 lines
OASIS_matlab/oasis_setup.m, MATLAB, 36 lines
OASIS_matlab/packages/MCMC/cont_ca_sampler.m, MATLAB, 420 lines
OASIS_matlab/packages/MCMC/plot_continuous_samples.m, MATLAB, 143 lines
OASIS_matlab/packages/MCMC/sampling_demo_ar2.m, MATLAB, 64 lines
OASIS_matlab/packages/MCMC/utilities/HMC_exact2.m, MATLAB, 229 lines
OASIS_matlab/packages/MCMC/utilities/addSpike.m, MATLAB, 65 lines
OASIS_matlab/packages/MCMC/utilities/get_initial_sample.m, MATLAB, 51 lines
OASIS_matlab/packages/MCMC/utilities/get_next_spikes.m, MATLAB, 159 lines
OASIS_matlab/packages/MCMC/utilities/lambda_rate.m, MATLAB, 8 lines
OASIS_matlab/packages/MCMC/utilities/make_G_matrix.m, MATLAB, 23 lines
OASIS_matlab/packages/MCMC/utilities/make_mean_sample.m, MATLAB, 58 lines
OASIS_matlab/packages/MCMC/utilities/plot_marginals.m, MATLAB, 58 lines
OASIS_matlab/packages/MCMC/utilities/removeSpike.m, MATLAB, 63 lines
OASIS_matlab/packages/MCMC/utilities/replaceSpike.m, MATLAB, 73 lines
OASIS_matlab/packages/MCMC/utilities/samples_cell2mat.m, MATLAB, 24 lines
OASIS_matlab/packages/MCMC/utilities/tau_c2d.m, MATLAB, 15 lines
OASIS_matlab/packages/MCMC/utilities/tau_d2c.m, MATLAB, 15 lines
OASIS_matlab/packages/MCMC/wrapper.m, MATLAB, 13 lines
OASIS_matlab/packages/constrained-foopsi/MCEM_foopsi.m, MATLAB, 135 lines
OASIS_matlab/packages/constrained-foopsi/constrained_foopsi.m, MATLAB, 299 lines
OASIS_matlab/packages/constrained-foopsi/cvx_foopsi.m, MATLAB, 46 lines
OASIS_matlab/packages/constrained-foopsi/lars_regression_noise.m, MATLAB, 286 lines
OASIS_matlab/packages/oasis/choose_lambda.m, MATLAB, 41 lines
OASIS_matlab/packages/oasis/constrained_oasisAR1.m, MATLAB, 261 lines
OASIS_matlab/packages/oasis/constrained_oasisAR2.m, MATLAB, 252 lines
OASIS_matlab/packages/oasis/create_kernel.m, MATLAB, 83 lines
OASIS_matlab/packages/oasis/deconvCa.m, MATLAB, 356 lines
OASIS_matlab/packages/oasis/dsKernel.m, MATLAB, 42 lines
OASIS_matlab/packages/oasis/foopsi_oasisAR1.m, MATLAB, 180 lines
OASIS_matlab/packages/oasis/foopsi_oasisAR2.m, MATLAB, 191 lines
OASIS_matlab/packages/oasis/oasisAR1.m, MATLAB, 109 lines
OASIS_matlab/packages/oasis/oasisAR2.m, MATLAB, 156 lines
OASIS_matlab/packages/oasis/onnls.m, MATLAB, 214 lines
OASIS_matlab/packages/oasis/test_oasis.m, MATLAB, 34 lines
OASIS_matlab/packages/oasis/thresholded_nnls.m, MATLAB, 220 lines
OASIS_matlab/packages/oasis/thresholded_oasisAR1.m, MATLAB, 277 lines
OASIS_matlab/packages/oasis/thresholded_oasisAR2.m, MATLAB, 322 lines
OASIS_matlab/packages/oasis/update_g.m, MATLAB, 83 lines
OASIS_matlab/packages/oasis/update_kernel_exp2.m, MATLAB, 131 lines
OASIS_matlab/packages/oasis/update_lam.m, MATLAB, 78 lines
OASIS_matlab/packages/oasis/update_tau.m, MATLAB, 144 lines
OASIS_matlab/packages/oasis_kernel/choose_smin.m, MATLAB, 42 lines
OASIS_matlab/packages/oasis_kernel/create_kernel.m, MATLAB, 83 lines
OASIS_matlab/packages/oasis_kernel/deconvCa.m, MATLAB, 358 lines
OASIS_matlab/packages/oasis_kernel/dsKernel.m, MATLAB, 42 lines
OASIS_matlab/packages/oasis_kernel/test_oasis.m, MATLAB, 34 lines
OASIS_matlab/packages/oasis_kernel/update_kernel_exp2.m, MATLAB, 130 lines
ca_source_extraction/@Sources2D/MergeNeighbors.m, MATLAB, 115 lines
ca_source_extraction/@Sources2D/Sources2D.m, MATLAB, 2,177 lines
ca_source_extraction/@Sources2D/correlation_pnr_parallel.m, MATLAB, 128 lines
ca_source_extraction/@Sources2D/deconvTemporal.m, MATLAB, 106 lines
ca_source_extraction/@Sources2D/decorrTemporal.m, MATLAB, 65 lines
ca_source_extraction/@Sources2D/displayNeurons.m, MATLAB, 163 lines
ca_source_extraction/@Sources2D/downSample.m, MATLAB, 29 lines
ca_source_extraction/@Sources2D/initComponents_2p.m, MATLAB, 292 lines
ca_source_extraction/@Sources2D/initComponents_batch.m, MATLAB, 113 lines
ca_source_extraction/@Sources2D/initComponents_corr.m, MATLAB, 200 lines
ca_source_extraction/@Sources2D/initComponents_endoscope.m, MATLAB, 204 lines
ca_source_extraction/@Sources2D/initComponents_parallel.m, MATLAB, 499 lines
ca_source_extraction/@Sources2D/initComponents_residual_parallel.m, MATLAB, 432 lines
ca_source_extraction/@Sources2D/initTemporal.m, MATLAB, 303 lines
ca_source_extraction/@Sources2D/manual_merge_multi_pairs.m, MATLAB, 215 lines
ca_source_extraction/@Sources2D/merge_close_neighbors.m, MATLAB, 240 lines
ca_source_extraction/@Sources2D/merge_high_corr.m, MATLAB, 239 lines
ca_source_extraction/@Sources2D/merge_neurons_dist_corr.m, MATLAB, 246 lines
ca_source_extraction/@Sources2D/post_process_spatial.m, MATLAB, 34 lines
ca_source_extraction/@Sources2D/quickMerge.m, MATLAB, 127 lines
ca_source_extraction/@Sources2D/set_parameters.m, MATLAB, 183 lines
ca_source_extraction/@Sources2D/show_demixed_video.m, MATLAB, 171 lines
ca_source_extraction/@Sources2D/upSample.m, MATLAB, 41 lines
ca_source_extraction/@Sources2D/updateBG.m, MATLAB, 114 lines
ca_source_extraction/@Sources2D/updateSpatial_endoscope.m, MATLAB, 65 lines
ca_source_extraction/@Sources2D/updateTemporal_endoscope.m, MATLAB, 95 lines
ca_source_extraction/@Sources2D/update_background_batch.m, MATLAB, 24 lines
ca_source_extraction/@Sources2D/update_background_parallel.m, MATLAB, 334 lines
ca_source_extraction/@Sources2D/update_spatial_batch.m, MATLAB, 45 lines
ca_source_extraction/@Sources2D/update_spatial_parallel.m, MATLAB, 366 lines
ca_source_extraction/@Sources2D/update_temporal_batch.m, MATLAB, 33 lines
ca_source_extraction/@Sources2D/update_temporal_parallel.m, MATLAB, 355 lines
ca_source_extraction/@Sources2D/viewNeurons.m, MATLAB, 208 lines
ca_source_extraction/CNMFSetParms.m, MATLAB, 315 lines
ca_source_extraction/compress_weights.m, MATLAB, 25 lines
ca_source_extraction/constrained_foopsi.m, MATLAB, 298 lines
ca_source_extraction/demo_memmap.m, MATLAB, 55 lines
ca_source_extraction/demo_script.m, MATLAB, 108 lines
ca_source_extraction/demo_script_class.m, MATLAB, 105 lines
ca_source_extraction/endoscope/GetSn_hist.m, MATLAB, 32 lines
ca_source_extraction/endoscope/HALS_spatial_threshold.m, MATLAB, 44 lines
ca_source_extraction/endoscope/bsplineM.m, MATLAB, 187 lines
ca_source_extraction/endoscope/circular_constraints.m, MATLAB, 55 lines
ca_source_extraction/endoscope/connectivity_constraint.m, MATLAB, 19 lines
ca_source_extraction/endoscope/correlation_image_endoscope.m, MATLAB, 97 lines
ca_source_extraction/endoscope/deconv_temporal.m, MATLAB, 89 lines
ca_source_extraction/endoscope/detrend_data.m, MATLAB, 42 lines
ca_source_extraction/endoscope/distribute_data.m, MATLAB, 208 lines
ca_source_extraction/endoscope/dsData.m, MATLAB, 43 lines
ca_source_extraction/endoscope/extract_ac.m, MATLAB, 107 lines
ca_source_extraction/endoscope/extract_ac_2p.m, MATLAB, 98 lines
ca_source_extraction/endoscope/fit_nmf_model.m, MATLAB, 23 lines
ca_source_extraction/endoscope/fit_ring_model.m, MATLAB, 129 lines
ca_source_extraction/endoscope/fit_svd_model.m, MATLAB, 42 lines
ca_source_extraction/endoscope/get_data_dimension.m, MATLAB, 78 lines
ca_source_extraction/endoscope/get_date.m, MATLAB, 3 lines
ca_source_extraction/endoscope/get_fullname.m, MATLAB, 12 lines
ca_source_extraction/endoscope/get_minute.m, MATLAB, 3 lines
ca_source_extraction/endoscope/get_nhood.m, MATLAB, 25 lines
ca_source_extraction/endoscope/get_patch_data.m, MATLAB, 144 lines
ca_source_extraction/endoscope/greedyROI_endoscope.m, MATLAB, 498 lines
ca_source_extraction/endoscope/lle.m, MATLAB, 107 lines
ca_source_extraction/endoscope/local_background.m, MATLAB, 152 lines
ca_source_extraction/endoscope/nnls_spatial.m, MATLAB, 110 lines
ca_source_extraction/endoscope/nnls_spatial_thresh.m, MATLAB, 107 lines
ca_source_extraction/endoscope/pair_neurons.m, MATLAB, 45 lines
ca_source_extraction/endoscope/remove_baseline.m, MATLAB, 10 lines
ca_source_extraction/endoscope/savegcf.m, MATLAB, 5 lines
ca_source_extraction/endoscope/spatial_constraints.m, MATLAB, 54 lines
ca_source_extraction/endoscope/string2hash.m, MATLAB, 48 lines
ca_source_extraction/endoscope/struct2neuron.m, MATLAB, 13 lines
ca_source_extraction/endoscope/svdsecon.m, MATLAB, 40 lines
ca_source_extraction/endoscope/tif2mat.m, MATLAB, 52 lines
ca_source_extraction/endoscope/update_spatial_components_nb.m, MATLAB, 134 lines
ca_source_extraction/endoscope/update_temporal_components_nb.m, MATLAB, 378 lines
ca_source_extraction/endoscope/writeTiff.m, MATLAB, 6 lines
ca_source_extraction/initialize_components.m, MATLAB, 129 lines
ca_source_extraction/merge_components.m, MATLAB, 159 lines
ca_source_extraction/preprocess_data.m, MATLAB, 148 lines
ca_source_extraction/run_CNMF_patches.m, MATLAB, 246 lines
ca_source_extraction/update_spatial_components.m, MATLAB, 144 lines
ca_source_extraction/update_temporal_components.m, MATLAB, 389 lines
ca_source_extraction/update_temporal_parallel.m, MATLAB, 1 line
ca_source_extraction/utilities/HALS.m, MATLAB, 57 lines
ca_source_extraction/utilities/HALS_2d.m, MATLAB, 61 lines
ca_source_extraction/utilities/HALS_spatial.m, MATLAB, 45 lines
ca_source_extraction/utilities/HALS_spatial_thresh.m, MATLAB, 54 lines
ca_source_extraction/utilities/HALS_temporal.m, MATLAB, 119 lines
ca_source_extraction/utilities/MCEM_foopsi.m, MATLAB, 135 lines
ca_source_extraction/utilities/bigread2.m, MATLAB, 155 lines
ca_source_extraction/utilities/classify_components.m, MATLAB, 36 lines
ca_source_extraction/utilities/com.m, MATLAB, 28 lines
ca_source_extraction/utilities/correlation_image.m, MATLAB, 77 lines
ca_source_extraction/utilities/cvx_foopsi.m, MATLAB, 46 lines
ca_source_extraction/utilities/determine_search_location.m, MATLAB, 104 lines
ca_source_extraction/utilities/dsData.m, MATLAB, 44 lines
ca_source_extraction/utilities/extract_DF_F.m, MATLAB, 79 lines
ca_source_extraction/utilities/find_unsaturatedPixels.m, MATLAB, 24 lines
ca_source_extraction/utilities/get_noise_fft.m, MATLAB, 83 lines
ca_source_extraction/utilities/graph_conn_comp_mex.cpp, C++, 116 lines
ca_source_extraction/utilities/graph_connected_comp.m, MATLAB, 27 lines
ca_source_extraction/utilities/greedyROI.m, MATLAB, 287 lines
ca_source_extraction/utilities/greedyROI_corr.m, MATLAB, 237 lines
ca_source_extraction/utilities/interp_missing_data.m, MATLAB, 28 lines
ca_source_extraction/utilities/kde.m, MATLAB, 174 lines
ca_source_extraction/utilities/kmeans_pp.m, MATLAB, 67 lines
ca_source_extraction/utilities/lagrangian_foopsi_temporal.m, MATLAB, 25 lines
ca_source_extraction/utilities/lars_regression_noise.m, MATLAB, 286 lines
ca_source_extraction/utilities/lars_spatial.m, MATLAB, 151 lines
ca_source_extraction/utilities/make_G_matrix.m, MATLAB, 13 lines
ca_source_extraction/utilities/make_patch_video.m, MATLAB, 181 lines
ca_source_extraction/utilities/manually_refine_components.m, MATLAB, 132 lines
ca_source_extraction/utilities/memmap/construct_patches.m, MATLAB, 65 lines
ca_source_extraction/utilities/memmap/memmap_file.m, MATLAB, 64 lines
ca_source_extraction/utilities/memmap/memmap_file_sequence.m, MATLAB, 39 lines
ca_source_extraction/utilities/order_ROIs.m, MATLAB, 38 lines
ca_source_extraction/utilities/order_components.m, MATLAB, 59 lines
ca_source_extraction/utilities/plain_foopsi.m, MATLAB, 54 lines
ca_source_extraction/utilities/plot_components_GUI.m, MATLAB, 211 lines
ca_source_extraction/utilities/plot_contours.m, MATLAB, 102 lines
ca_source_extraction/utilities/run_movie.m, MATLAB, 120 lines
ca_source_extraction/utilities/running_percentile.m, MATLAB, 122 lines
ca_source_extraction/utilities/smod_bigread2.m, MATLAB, 453 lines
ca_source_extraction/utilities/sparse_NMF_initialization.m, MATLAB, 96 lines
ca_source_extraction/utilities/threshold_components.m, MATLAB, 60 lines
ca_source_extraction/utilities/tiff_reader.m, MATLAB, 17 lines
ca_source_extraction/utilities/update_order.m, MATLAB, 30 lines
ca_source_extraction/utilities/view_components.m, MATLAB, 178 lines
cnmfe_setup.m, MATLAB, 53 lines
demos/demo_batch_1p.m, MATLAB, 146 lines
demos/demo_batch_2p.m, MATLAB, 135 lines
demos/demo_endoscope.m, MATLAB, 230 lines
demos/demo_large_data_1p.m, MATLAB, 250 lines
demos/demo_large_data_2p.m, MATLAB, 205 lines
demos/demo_sfn_2018.m, MATLAB, 252 lines
python_wrapper/analyze_cnmfe_matlab.ipynb, Jupyter, 104 lines
python_wrapper/fix_mat_files.m, MATLAB, 20 lines
python_wrapper/python_utils.py, Python, 205 lines
python_wrapper/run_cnmfe_matlab.m, MATLAB, 249 lines
python_wrapper/run_cnmfe_matlab.py, Python, 115 lines
scripts/cnmfe_choose_data.m, MATLAB, 67 lines
scripts/cnmfe_demix_video.m, MATLAB, 71 lines
scripts/cnmfe_full.m, MATLAB, 49 lines
scripts/cnmfe_load_data.m, MATLAB, 18 lines
scripts/cnmfe_manual_merge.m, MATLAB, 60 lines
scripts/cnmfe_manual_merge_cancel.m, MATLAB, 9 lines
scripts/cnmfe_manual_merge_merge_neurons.m, MATLAB, 32 lines
scripts/cnmfe_manual_merge_pick_neuron.m, MATLAB, 65 lines
scripts/cnmfe_manual_merge_remove_neuron.m, MATLAB, 51 lines
scripts/cnmfe_merge_neighbors.m, MATLAB, 57 lines
scripts/cnmfe_quick_merge.m, MATLAB, 55 lines
scripts/cnmfe_save_video.m, MATLAB, 119 lines
scripts/cnmfe_show_corr_pnr.m, MATLAB, 20 lines
scripts/cnmfe_svd_BG.m, MATLAB, 7 lines
scripts/cnmfe_unzip_results.m, MATLAB, 42 lines
scripts/cnmfe_update_BG.m, MATLAB, 20 lines
scripts/cnmfe_version.m, MATLAB, 18 lines
scripts/cnmfe_zip_results.m, MATLAB, 16 lines
LICENSE.txt, License, 674 lines
README.md, Text, 73 lines

At the source: github.com/zhoupc/CNMF_E

zivlab/CellReg

License: GPL-2.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 70668513c665e1cb0de218c33264b8736f2976eb, 2 July 2026

Languages: MATLAB (60)

Size: 84 files, 60 scripts

Software Heritage: not archived

Found in: the text, “Cell registration across sessions”

Holds: README, license file, documentation

Not found: CITATION.cff, environment file, tests, continuous integration

Tools: Image Processing Toolbox (1 file), Optimization Toolbox (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

62 files

CellReg/adjust_FOV_size.m, MATLAB, 67 lines
CellReg/align_images.m, MATLAB, 446 lines
CellReg/check_if_in_overlapping_FOV.m, MATLAB, 23 lines
CellReg/choose_best_model.m, MATLAB, 48 lines
CellReg/cluster_cells.m, MATLAB, 335 lines
CellReg/compute_centroid_distances_model.m, MATLAB, 76 lines
CellReg/compute_centroid_locations.m, MATLAB, 75 lines
CellReg/compute_centroids_projections.m, MATLAB, 33 lines
CellReg/compute_data_distribution.m, MATLAB, 181 lines
CellReg/compute_footprints_projections.m, MATLAB, 33 lines
CellReg/compute_p_same.m, MATLAB, 63 lines
CellReg/compute_scores.m, MATLAB, 103 lines
CellReg/compute_spatial_correlations_model.m, MATLAB, 137 lines
CellReg/demo.m, MATLAB, 307 lines
CellReg/demo_2P.m, MATLAB, 422 lines
CellReg/display_progress_bar.m, MATLAB, 70 lines
CellReg/estimate_beta_mixture_params.m, MATLAB, 36 lines
CellReg/estimate_number_of_bins.m, MATLAB, 38 lines
CellReg/estimate_registration_accuracy.m, MATLAB, 80 lines
CellReg/evaluate_data_quality.m, MATLAB, 80 lines
CellReg/freezeColors.m, MATLAB, 268 lines
CellReg/gaussfit.m, MATLAB, 100 lines
CellReg/initial_registration_centroid_distances.m, MATLAB, 115 lines
CellReg/initial_registration_spatial_correlations.m, MATLAB, 189 lines
CellReg/interpolate_pixel_value.m, MATLAB, 40 lines
CellReg/load_multiple_sessions.m, MATLAB, 30 lines
CellReg/load_single_session.m, MATLAB, 21 lines
CellReg/normalize_spatial_footprints.m, MATLAB, 55 lines
CellReg/plot_RGB_overlay.m, MATLAB, 39 lines
CellReg/plot_alignment_results.m, MATLAB, 357 lines
CellReg/plot_all_registered_projections.m, MATLAB, 107 lines
CellReg/plot_all_sessions_projections.m, MATLAB, 41 lines
CellReg/plot_cell_scores.m, MATLAB, 194 lines
CellReg/plot_estimated_registration_accuracy.m, MATLAB, 204 lines
CellReg/plot_initial_registration.m, MATLAB, 71 lines
CellReg/plot_models.m, MATLAB, 127 lines
CellReg/plot_single_session_projections.m, MATLAB, 19 lines
CellReg/plot_x_y_displacements.m, MATLAB, 67 lines
CellReg/rotate_cell.m, MATLAB, 42 lines
CellReg/rotate_image_interp.m, MATLAB, 36 lines
CellReg/rotate_spatial_footprint.m, MATLAB, 44 lines
CellReg/save_log_file.m, MATLAB, 142 lines
CellReg/transform_distance_to_similarity.m, MATLAB, 17 lines
CellReg/translate_projections.m, MATLAB, 30 lines
CellReg/translate_spatial_footprint.m, MATLAB, 39 lines
CellReg_setup.m, MATLAB, 5 lines
GUI/CellReg.m, MATLAB, 2,236 lines, 1 match
GUI/msgbox_timed.m, MATLAB, 16 lines
GUI/plot_estimated_accuracy_GUI.m, MATLAB, 125 lines
GUI/plot_p_same_centroid_distance_slider.m, MATLAB, 54 lines
GUI/plot_p_same_spatial_correlation_slider.m, MATLAB, 54 lines
GUI/plot_x_y_displacements_GUI.m, MATLAB, 33 lines
GUI/warndlg_timed.m, MATLAB, 16 lines
Helper/format_conversion_Suite2p_CNMF_e.m, MATLAB, 46 lines
Helper/format_conversion_inscopix.m, MATLAB, 29 lines
Helper/get_spatial_footprints.m, MATLAB, 62 lines
Helper/load_footprint_data.m, MATLAB, 26 lines
Helper/mat_to_sparse_cell.m, MATLAB, 8 lines
Helper/s2pToCellReg.m, MATLAB, 51 lines
Helper/s2pToCellRegSparseCell.m, MATLAB, 62 lines
LICENSE.txt, License, 339 lines
README.md, Text, 55 lines

At the source: github.com/zivlab/CellReg

Zenodo 18279500

License: CC-BY-4.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

At the source:

Code availability

Raw Ca²⁺ imaging data were processed using commercial software (Thinkerbiotech, Nanjing, China). The custom MATLAB scripts developed for this data processing are publicly available on Zenodo (10.5281/zenodo.18279500).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
324 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

figshare:29908157, at figshare; found in DataCite

Data availability

All the data generated in this study are provided in the article and the Supplementary Information. The relevant raw data are provided as a Source data file. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 14 MeSH terms, 1 funder, 65 references.

Cite

This paper

Zheng, W., Liu, X., Lu, T., Lv, X., Guan, X., Yu, Y., Li, X., Wang, Z., Yuan, K., Grimm, J. W., Robbins, T. W., Shi, J., Lu, L., & Xue, Y.-X. (2026). Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse. Nature communications, 17(1), 3462. https://doi.org/10.1038/s41467-026-71357-4

BibTeX

@article{zheng2026social, author = {Zheng, Wei and Liu, Xiaoxing and Lu, Tangsheng and Lv, Xinyou and Guan, Xuefang and Yu, Yifan and Li, Xue and Wang, Zhe and Yuan, Kai and Grimm, Jeffrey W and Robbins, Trevor W and Shi, Jie and Lu, Lin and Xue, Yan-Xue}, title = {{Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse}}, journal = {Nature communications}, year = {2026}, month = apr, volume = {17}, number = {1}, pages = {3462}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-71357-4}, url = {https://doi.org/10.1038/s41467-026-71357-4}, pmid = {41932919}, pmcid = {PMC13076667} }

RIS

TY - JOUR AU - Zheng, Wei AU - Liu, Xiaoxing AU - Lu, Tangsheng AU - Lv, Xinyou AU - Guan, Xuefang AU - Yu, Yifan AU - Li, Xue AU - Wang, Zhe AU - Yuan, Kai AU - Grimm, Jeffrey W AU - Robbins, Trevor W AU - Shi, Jie AU - Lu, Lin AU - Xue, Yan-Xue TI - Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/04/03 VL - 17 IS - 1 SP - 3462 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-71357-4 UR - https://doi.org/10.1038/s41467-026-71357-4 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-71357-4", "type": "article-journal", "title": "Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse", "container-title": "Nature communications", "author": [ { "family": "Zheng", "given": "Wei" }, { "family": "Liu", "given": "Xiaoxing" }, { "family": "Lu", "given": "Tangsheng" }, { "family": "Lv", "given": "Xinyou" }, { "family": "Guan", "given": "Xuefang" }, { "family": "Yu", "given": "Yifan" }, { "family": "Li", "given": "Xue" }, { "family": "Wang", "given": "Zhe" }, { "family": "Yuan", "given": "Kai" }, { "family": "Grimm", "given": "Jeffrey W" }, { "family": "Robbins", "given": "Trevor W" }, { "family": "Shi", "given": "Jie" }, { "family": "Lu", "given": "Lin" }, { "family": "Xue", "given": "Yan-Xue" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "3462", "DOI": "10.1038/s41467-026-71357-4", "PMID": "41932919", "PMCID": "PMC13076667", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-71357-4", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 3 ] ] } }

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Social reward outcompetes drug seeking dopaminergic ensembles to prevent relapse.

Code ↔ Paper

Paper

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Abstract

Repositories

zhoupc/CNMF_E

zivlab/CellReg

Zenodo 18279500

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Data

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Version 1, 28 September 2026: the first record

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