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CellExplorer.m
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CellExplorer.m
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function cell_metrics = CellExplorer(varargin)
% CellExplorer is a Matlab GUI and standardized pipeline for exploring and
% classifying spike sorted single units acquired using extracellular electrodes.
%
% Check out the website for extensive documentation and tutorials: https://CellExplorer.org/
%
% Below follows a detailed description of how to call CellExplorer
%
% INPUTS
% varargin (Variable-length input argument list)
%
% - Single session struct with cell_metrics from one or more sessions
% metrics - cell_metrics struc
%
% - Single session inputs
% basepath - Path to session (base directory)
% basename - basename (database session name)
% id - BuzLabDB numeric id
% session - Session struct
%
% - Batch session inputs (when loading multiple session)
% basepaths - Paths to sessions (base directory)
% sessionIDs - BuzLabDB numeric ids
% sessions - session names (basenames)
%
% - Example calls:
% cell_metrics = CellExplorer % Load from current path, assumed to be a basepath
% cell_metrics = CellExplorer('basepath',basepath) % Load from basepath
% cell_metrics = CellExplorer('metrics',cell_metrics) % Load from cell_metrics
% cell_metrics = CellExplorer('session',session) % Load session from session struct
% cell_metrics = CellExplorer('sessionName','rec1') % Load session from database session name
% cell_metrics = CellExplorer('sessionID',10985) % Load session from BuzLabDB session id
% cell_metrics = CellExplorer('sessions',{'rec1','rec2'}) % Load batch from database
% cell_metrics = CellExplorer('basepaths',{'path1','[path1'}) % Load batch from a list with paths
%
% - Summary figure calls:
% CellExplorer('metrics',cell_metrics,'summaryFigures',true,'plotCellIDs',-1) % creates Session summary figure from cell_metrics for all cells
% CellExplorer('metrics',cell_metrics,'summaryFigures',true) % creates Cell summary figures from cell_metrics for all cells
% CellExplorer('metrics',cell_metrics,'summaryFigures',true,'plotCellIDs',[1,4,5]) % creates Cell summary figures for select cells [1,4,5]
%
% OUTPUT
% cell_metrics: struct
% By Peter Petersen
% Shortcuts to built-in functions:
% Initialization: initializeSession, initializeUI,
% Data handling: saveDialog, restoreBackup, importGroundTruth, DatabaseSessionDialog, defineReferenceData, initializeReferenceData, defineGroupData
% UI: hoverCallback, updateUI, GroupAction, defineSpikesPlots, keyPress, FromPlot, GroupSelectFromPlot, ScrolltoZoomInPlot, brainRegionDlg, tSNE_redefineMetrics
% Plotting: customPlot, plotGroupData, plotSummaryFigures, runBenchMark
if isdeployed % Check for if CellExplorer is running as a deployed app (compiled .exe or .app for windows and mac respectively)
if ~isempty(varargin) % If a file name is provided it will load it.
filename = varargin{1};
[basepath1,file1] = fileparts(varargin{1});
else % Otherwise a file load dialog will be shown
[file1,basepath1] = uigetfile('*.mat;*.dat;*.lfp;*.xml','Please select a file with the basename in it from the basepath');
end
if ~isequal(file1,0)
basepath = basepath1;
temp1 = strsplit(file1,'.');
basename = temp1{1};
else
return
end
metrics = [];
id = [];
sessionName = [];
session = [];
sessionIDs = {};
sessionsin = {};
summaryFigures = false;
plotCellIDs = [];
basepaths = {};
else
p = inputParser;
addParameter(p,'metrics',[],@isstruct); % cell_metrics struct
addParameter(p,'basepath',pwd,@isstr); % Path to session (base directory)
addParameter(p,'session',[],@isstruct);
addParameter(p,'basename','',@isstr);
addParameter(p,'sessionID',[],@isnumeric);
addParameter(p,'sessionName',[],@isstr);
% Batch input
addParameter(p,'sessionIDs',{},@iscell);
addParameter(p,'sessions',{},@iscell);
addParameter(p,'basepaths',{},@iscell);
% Extra inputs
addParameter(p,'summaryFigures',false,@islogical); % Creates summary figures
addParameter(p,'plotCellIDs',[],@isnumeric); % Defines which cell ids to plot in the summary figures
% Parsing inputs
parse(p,varargin{:})
metrics = p.Results.metrics;
id = p.Results.sessionID;
sessionName = p.Results.sessionName;
session = p.Results.session;
basepath = p.Results.basepath;
basename = p.Results.basepaths;
% Batch inputs
sessionIDs = p.Results.sessionIDs;
sessionsin = p.Results.sessions;
basepaths = p.Results.basepaths;
% Extra inputs
summaryFigures = p.Results.summaryFigures;
plotCellIDs = p.Results.plotCellIDs;
end
%% % % % % % % % % % % % % % % % % % % % % %
% Initialization of variables and figure
% % % % % % % % % % % % % % % % % % % % % %
UI = []; UI.BatchMode = false; UI.params.ClickedCells = []; UI.params.inbound = [];
UI.drag.mouse = false; UI.drag.startX = []; UI.drag.startY = []; UI.drag.axnum = []; UI.drag.pan = []; UI.scroll = true;
UI.params.ii_history = 1; UI.lists.metrics = []; UI.params.hoverStyle = 2;
UI.params.incoming = []; UI.params.outgoing = []; UI.monoSyn.disp = ''; UI.monoSyn.dispHollowGauss = false;
UI.params.ACGLogIntervals = -3:0.04:1; UI.tableData.Column1 = 'putativeCellType'; UI.tableData.Column2 = 'brainRegion';
UI.tableData.SortBy = 'cellID'; UI.plot.xTitle = ''; UI.plot.yTitle = ''; UI.plot.zTitle = ''; UI.params.alteredCellMetrics = 1;
UI.cells.excitatory = []; UI.cells.inhibitory = []; UI.cells.inhibitory_subset = []; UI.cells.excitatory_subset = [];
UI.cells.excitatoryPostsynaptic = []; UI.cells.inhibitoryPostsynaptic = []; UI.params.outbound = [];
UI.zoom.global = cell(1,10); UI.zoom.globalLog = cell(1,10);
UI.params.chanCoords.x_factor = 40; UI.params.chanCoords.y_factor = 10; UI.colors.toggleButtons = [0. 0.3 0.7];
UI.colorLine = [0, 0.4470, 0.7410;0.8500, 0.3250, 0.0980;0.9290, 0.6940, 0.1250;0.4940, 0.1840, 0.5560;0.4660, 0.6740, 0.1880;0.3010, 0.7450, 0.9330;0.6350, 0.0780, 0.1840];
UI.params.fieldsMenuMetricsToExlude = {'tags','groundTruthClassification','groups','spikes'};
UI.params.plotOptionsToExlude = {'acg_','waveforms_','isi_','responseCurves_thetaPhase','responseCurves_thetaPhase_zscored','responseCurves_firingRateAcrossTime','groups','tags','groundTruthClassification','spikes'};
UI.params.menuOptionsToExlude = {'putativeCellType','tags','groundTruthClassification','groups','spikes'};
UI.params.tableOptionsToExlude = {'putativeCellType','tags','groundTruthClassification','brainRegion','labels','deepSuperficial','groups','spikes'};
UI.params.tableDataSortingList = sort({'cellID', 'putativeCellType','peakVoltage','firingRate','troughToPeak','synapticConnectionsOut','synapticConnectionsIn','animal','sessionName','cv2','brainRegion','electrodeGroup'});
UI.classes.plot2 = []; UI.classes.colors = []; UI.classes.colors2 = []; UI.classes.colors3 = []; UI.classes.plot = []; UI.classes.plot11 = [];
UI.brainRegions.list = []; UI.brainRegions.acronym = []; UI.brainRegions.relational_tree = []; UI.groupData1.groupsList = {'groups','tags','groundTruthClassification'};
plotConnections = [1 1 1]; plotAverage_nbins = 40; colored_string = [];
synConnectOptions = {'None', 'Selected', 'Upstream', 'Downstream', 'Up & downstream', 'All'}; ccf_ratio = [-35.5,30];
plotX = []; plotY = []; plotY1 = []; plotZ = []; plotMarkerSize = [];
fig2_axislimit_x = []; fig2_axislimit_y = []; fig3_axislimit_x = []; fig3_axislimit_y = [];
fig2_axislimit_x_reference = []; fig2_axislimit_y_reference = []; fig2_axislimit_x_groundTruth = []; fig2_axislimit_y_groundTruth = [];
ce_waitbar = []; colorStr = []; iLine = 1; h_scatter2 = [];h_scatter3 = [];groups_ids = []; clusClas = []; meanCCG = [];
hover2highlight = {}; clickPlotRegular = true;
classes2plot = []; classes2plotSubset = []; ii = []; history_classification = []; batchIDs = []; general = []; classificationTrackChanges = [];
cell_class_count = []; plotOptions = ''; colorMenu = []; GroupVal = 1; ColorVal = 1;
plotAcgFit = 0; plotAcgYLog = 0; plotAcgZscore = 0; clasLegend = 0; groups2plot = []; groups2plot2 = []; connectivityGraph = [];
tSNE_metrics = []; spikesPlots = {}; gauss2d = ce_gausswin(10)*ce_gausswin(10)'; gauss2d = 1.*gauss2d/sum(gauss2d(:));
idx_textFilter = []; freeText = {''}; table_metrics = []; table_fieldsNames = {}; tableDataOrder = [];
groundTruthSelection = []; subsetGroundTruth = []; groundTruthCelltypesList = {''}; db = {}; gt = {};
customPlotOptions = {}; timerInterface = tic; timerHover = tic; highlightCurrentCell = true;
spikes = []; events = []; states = [];
referenceData=[]; reference_cell_metrics = []; groundTruth_cell_metrics = []; groundTruthData=[];
createStruct.Interpreter = 'tex'; createStruct.WindowStyle = 'modal';
createStruct1.Interpreter = 'none'; createStruct1.WindowStyle = 'modal';
polygon1.handle = gobjects(0); fig = 1;
set(groot, 'DefaultFigureVisible', 'on','DefaultAxesLooseInset',[.01,.01,.01,.01],'DefaultTextInterpreter', 'none'),
maxFigureSize = get(groot,'ScreenSize');
UI.params.figureSize = [50, 50, min([1200,maxFigureSize(3)-300]), min([800,maxFigureSize(4)-300])];
if isempty(basename)
basename = basenameFromBasepath(basepath);
end
CellExplorerVersion = 1.72;
UI.fig = figure('Name',['CellExplorer v' num2str(CellExplorerVersion)],'NumberTitle','off','renderer','opengl', 'MenuBar', 'None','windowscrollWheelFcn',@ScrolltoZoomInPlot,'KeyPressFcn', {@keyPress},'DefaultAxesLooseInset',[.01,.01,.01,.01],'visible','off','WindowButtonMotionFcn', @hoverCallback,'pos',[0,0,1600,800],'DefaultTextInterpreter', 'none', 'DefaultLegendInterpreter', 'none'); % ,'WindowButtonDownFcn',@mousebuttonPress,'WindowButtonUpFcn',@mousebuttonRelease
hManager = uigetmodemanager(UI.fig);
% % % % % % % % % % % % % % % % % % % % % %
% User preferences
% % % % % % % % % % % % % % % % % % % % % %
% Setting default preferences and settings
UI = preferences_CellExplorer(UI);
% Setting last used preferences
UI.preferences = setLayout_CellExplorer(UI.preferences,1);
% % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% Checking for Matlab version requirement (Matlab R2017a)
% % % % % % % % % % % % % % % % % % % % % % % % % % % % %
if verLessThan('matlab', '9.2')
warning('CellExplorer is only fully compatible and tested with Matlab version 9.2 and forward (Matlab R2017a)')
return
end
% % % % % % % % % % % % % % % % % % % % % %
% Turning off select warnings
% % % % % % % % % % % % % % % % % % % % % %
warning('off','MATLAB:deblank:NonStringInput')
warning('off','MATLAB:HandleGraphics:ObsoletedProperty:JavaFrame')
warning('off','MATLAB:Axes:NegativeDataInLogAxis')
% % % % % % % % % % % % % % % % % % % % % %
% Database initialization
% % % % % % % % % % % % % % % % % % % % % %
if exist('db_load_settings','file')
db_settings = db_load_settings;
if ~strcmp(db_settings.credentials.username,'user')
enableDatabase = 1;
else
enableDatabase = 0;
end
else
enableDatabase = 0;
end
% % % % % % % % % % % % % % % % % % % % % %
% Initialization of inputs and session
% % % % % % % % % % % % % % % % % % % % % %
if isstruct(metrics)
cell_metrics = metrics;
initializeSession
elseif ~isempty(id) || ~isempty(sessionName) || ~isempty(session)
if enableDatabase
disp('Loading session from database')
if ~isempty(id)
try
[session, basename, basepath] = db_set_session('sessionId',id,'saveMat',false);
catch
warning('Failed to load dataset');
return
end
elseif ~isempty(sessionName)
try
[session, basename, basepath] = db_set_session('sessionName',sessionName,'saveMat',false);
catch
warning('Failed to load dataset');
return
end
else
try
[session, basename, basepath] = db_set_session('session',session,'saveMat',false);
catch
warning('Failed to load session');
return
end
end
try
LoadSession;
if ~exist('cell_metrics','var')
return
end
catch
warning('Failed to load cell_metrics');
return
end
else
warning('BuzLabDB tools not available');
return
end
elseif ~isempty(sessionIDs)
if enableDatabase
try
cell_metrics = loadCellMetricsBatch('sessionIDs',sessionIDs);
initializeSession
catch
warning('Failed to load dataset');
return
end
else
warning('BuzLabDB tools not available');
return
end
elseif ~isempty(sessionsin)
if enableDatabase
try
cell_metrics = loadCellMetricsBatch('sessions',sessionsin);
initializeSession
catch
warning('Failed to load dataset');
return
end
else
warning('BuzLabDB tools not available');
return
end
elseif ~isempty(basepaths)
try
cell_metrics = loadCellMetricsBatch('basepaths',basepaths);
initializeSession
catch
warning('Failed to load dataset from basepaths');
return
end
else
try
cd(basepath)
catch
warning('basepath not available')
close(UI.fig)
return
end
basename = basenameFromBasepath(basepath);
if exist(fullfile(basepath,[basename,'.cell_metrics.cellinfo.mat']),'file')
disp('Loading local cell_metrics')
load(fullfile(basepath,[basename,'.cell_metrics.cellinfo.mat']));
cell_metrics.general.basepath = basepath;
initializeSession
else
if enableDatabase
DatabaseSessionDialog;
else
loadFromFile
end
if ~exist('cell_metrics','var') || isempty(cell_metrics)
disp('No dataset selected - closing CellExplorer')
if ishandle(UI.fig)
close(UI.fig)
end
return
end
end
end
%% % % % % % % % % % % % % % % % % % % % % %
% Menu
% % % % % % % % % % % % % % % % % % % % % %
% Matlab version specific code
if ~verLessThan('matlab', '9.3')
menuLabel = 'Text';
menuSelectedFcn = 'MenuSelectedFcn';
else
menuLabel = 'Label';
menuSelectedFcn = 'Callback';
end
initializeMenu
setMenuPreferences
%% % % % % % % % % % % % % % % % % % % % % %
% UI panels and basic UI elements
% % % % % % % % % % % % % % % % % % % % % %
initializeUI
setUiPreferences
setUIfromCellMetrics
% % % % % % % % % % % % % % % % % % % % % %
% If Summary figures
% % % % % % % % % % % % % % % % % % % % % %
if summaryFigures
MsgLog('Generating summary figures',-1)
UI.params.subset = 1:length(cell_metrics.cellID);
% highlightCurrentCell = false;
plotSummaryFigures
% closes the UI
if ishandle(UI.fig)
close(UI.fig)
end
MsgLog('Summary figure(s) generated. Saved to /summaryFigures',-1)
return
end
% % % % % % % % % % % % % % % % % % %
% Maximazing figure to full screen
% % % % % % % % % % % % % % % % % % %
if ~verLessThan('matlab', '9.4')
set(UI.fig,'WindowState','maximize','visible','on'), drawnow nocallbacks;
else
set(UI.fig,'visible','on')
drawnow nocallbacks; frame_h = get(UI.fig,'JavaFrame'); set(frame_h,'Maximized',1); drawnow nocallbacks;
end
DragMouseBegin
%% % % % % % % % % % % % % % % % % % % % % %
% Main loop of UI
% % % % % % % % % % % % % % % % % % % % % %
while ii <= cell_metrics.general.cellCount
% breaking if figure has been closed
if ~ishandle(UI.fig)
break
end
updateUI
% Show popup for first time user
if UI.preferences.showIntroduction
introPopUpCellExplorer
UI.preferences.showIntroduction = false;
end
% Waiting for uiresume call
uiwait(UI.fig);
end
%% % % % % % % % % % % % % % % % % % % % % %
% Calls when closing
% % % % % % % % % % % % % % % % % % % % % %
if ishandle(UI.fig)
% Closing CellExplorer figure if still open
close(UI.fig);
end
% Saving layout-preferences
setLayout_CellExplorer(UI.preferences,2);
% Tracking usage (anonymously)
trackGoogleAnalytics('CellExplorer',CellExplorerVersion,'metrics',cell_metrics); % Anonymous tracking of usage
% Cleaning cell_metrics before returning it to workspace
cell_metrics = saveCellMetricsStruct(cell_metrics);
%% % % % % % % % % % % % % % % % % % % % % %
% Embedded functions
% % % % % % % % % % % % % % % % % % % % % %
function updateUI
timerInterface = tic;
if ishandle(UI.fig)
UI.benchmark.String = '';
end
% Keeping track of selected cells
if UI.params.ii_history(end) ~= ii
UI.params.ii_history = [UI.params.ii_history,ii];
end
% Instantiates batch metrics
if UI.BatchMode
batchIDs = cell_metrics.batchIDs(ii);
general = cell_metrics.general.batch{batchIDs};
else
batchIDs = 1;
general = cell_metrics.general;
end
% Resetting list of highlighted cells
if ~UI.preferences.stickySelection
UI.params.ClickedCells = [];
end
% Resetting polygon selection
clickPlotRegular = true;
% Resetting zoom levels for subplots
UI.zoom.global = cell(1,10);
UI.zoom.globalLog = cell(1,10);
UI.drag.mouse = false;
% Updating cell specific fields
if highlightCurrentCell
UI.listbox.deepSuperficial.Value = cell_metrics.deepSuperficial_num(ii);
UI.pushbutton.brainRegion.String = ['Region: ', cell_metrics.brainRegion{ii}];
UI.pushbutton.labels.String = ['Label: ', cell_metrics.labels{ii}];
% Updating putative cell type listbox
UI.listbox.cellClassification.Value = clusClas(ii);
else
UI.listbox.deepSuperficial.Value = 1;
UI.pushbutton.brainRegion.String = '';
UI.pushbutton.labels.String = '';
% Updating putative cell type listbox
UI.listbox.cellClassification.Value = 1;
end
% Defining the subset of cells to display
UI.params.subset = find(ismember(clusClas,classes2plot));
% Updating ground truth tags
if isfield(UI.tabs,'groundTruthClassification')
updateGroundTruth
end
% Updating tags
updateTags
% Rotation of common coordinate framework
if any(strcmp(UI.preferences.customPlot,'Common Coordinate Framework'))
idx = find(strcmp(UI.preferences.customPlot,'Common Coordinate Framework'));
[ccf_ratio1(1),ccf_ratio1(2)] = view(UI.axes(idx+3));
if all(ccf_ratio1 ~= [0,90])
ccf_ratio = ccf_ratio1;
end
end
% Group data
% Filters tagged cells ('tags','groups','groundTruthClassification')
if ~isempty(UI.groupData1)
dataTypes = {'tags','groups','groundTruthClassification'};
filter_pos = [];
filter_neg = [];
for jjj = 1:numel(dataTypes)
if isfield(UI.groupData1,dataTypes{jjj}) && isfield(UI.groupData1.(dataTypes{jjj}),'plus_filter') && any(struct2array(UI.groupData1.(dataTypes{jjj}).plus_filter))
if isfield(UI.groupData1,dataTypes{jjj}) && isfield(UI.groupData1.(dataTypes{jjj}),'plus_filter')
fields1 = fieldnames(UI.groupData1.(dataTypes{jjj}).plus_filter);
for jj = 1:numel(fields1)
if UI.groupData1.(dataTypes{jjj}).plus_filter.(fields1{jj}) == 1 && isfield(cell_metrics.(dataTypes{jjj}),fields1{jj}) && ~isempty(cell_metrics.(dataTypes{jjj}).(fields1{jj}))
filter_pos = [filter_pos,cell_metrics.(dataTypes{jjj}).(fields1{jj})];
end
end
end
end
if isfield(UI.groupData1,dataTypes{jjj}) && isfield(UI.groupData1.(dataTypes{jjj}),'minus_filter') && any(struct2array(UI.groupData1.(dataTypes{jjj}).minus_filter))
if isfield(UI.groupData1,dataTypes{jjj}) && isfield(UI.groupData1.(dataTypes{jjj}),'minus_filter')
fields1 = fieldnames(UI.groupData1.(dataTypes{jjj}).minus_filter);
for jj = 1:numel(fields1)
if UI.groupData1.(dataTypes{jjj}).minus_filter.(fields1{jj}) == 1 && isfield(cell_metrics.(dataTypes{jjj}),fields1{jj}) && ~isempty(cell_metrics.(dataTypes{jjj}).(fields1{jj}))
filter_neg = [filter_neg,cell_metrics.(dataTypes{jjj}).(fields1{jj})];
end
end
end
end
end
if ~isempty(filter_neg)
UI.params.subset = setdiff(UI.params.subset,filter_neg);
end
if ~isempty(filter_pos)
UI.params.subset = intersect(UI.params.subset,filter_pos);
end
end
if ~isempty(groups2plot2) && GroupVal >1
if ColorVal ~= 2
subset2 = find(ismember(UI.classes.plot11,groups2plot2));
UI.classes.plot = UI.classes.plot11;
else
subset2 = find(ismember(UI.classes.plot2,groups2plot2));
end
UI.params.subset = intersect(UI.params.subset,subset2);
end
% text filter
if ~isempty(idx_textFilter)
UI.params.subset = intersect(UI.params.subset,idx_textFilter);
end
[~,UI.preferences.troughToPeakSorted] = sort(cell_metrics.(UI.preferences.sortingMetric)(UI.params.subset));
% Regrouping cells if comparison checkbox is checked
if ColorVal == 5 % major brain regions
if ~isfield(UI.params,'brainRegionsLevel') | isempty(UI.params.brainRegionsLevel) | ~isfield(UI.params,'subset_pre') | numel(UI.params.subset) ~= numel(UI.params.subset_pre) | UI.params.subset ~= UI.params.subset_pre
if isempty(UI.brainRegions.relational_tree)
temp = load('brainRegions_relational_tree.mat','relational_tree');
UI.brainRegions.relational_tree = temp.relational_tree;
end
kk = 1;
idx = find(UI.brainRegions.relational_tree.graph_depth == UI.preferences.graph_depth);
brainRegionsLevel1 = UI.brainRegions.relational_tree.acronyms(idx);
UI.params.brainRegionsLevel = {};
UI.classes.plotBrainRegions = ones(1,length(UI.classes.plot));
for i = 1:numel(brainRegionsLevel1)
acronym_out = getBrainRegionChildren(brainRegionsLevel1{i},UI.brainRegions.relational_tree);
idx2 = ismember(lower(cell_metrics.brainRegion(UI.params.subset)),lower([acronym_out,brainRegionsLevel1{i}]));
if any(idx2)
UI.classes.plotBrainRegions(UI.params.subset(idx2)) = kk;
UI.params.brainRegionsLevel{kk} = brainRegionsLevel1{i};
kk = kk + 1;
end
end
end
if isempty(UI.params.brainRegionsLevel)
warning('No brain regions detected or existing in atlas')
return
end
UI.classes.plot = UI.classes.plotBrainRegions;
classes2plotSubset = unique(UI.classes.plot(UI.params.subset));
UI.classes.labels = UI.params.brainRegionsLevel(unique(UI.classes.plot(UI.params.subset)));
UI.params.subset_pre = UI.params.subset;
elseif ColorVal == 4 % Compare to rest
UI.classes.plot = ones(1,length(UI.classes.plot));
UI.classes.plot(UI.params.subset) = 2;
UI.params.subset = 1:length(UI.classes.plot);
classes2plotSubset = unique(UI.classes.plot);
UI.classes.labels = {'Other cells','Selected cells'};
elseif ColorVal == 3 % Single group
UI.classes.plot(1:numel(UI.classes.plot)) = 1;
classes2plotSubset = unique(UI.classes.plot);
UI.classes.labels = {'Selected cells'};
elseif ColorVal == 2 % Cell type color groups
classes2plotSubset = intersect(UI.classes.plot(UI.params.subset),classes2plot);
elseif ColorVal == 1 % Regular grouping
if GroupVal == 1
groups2plot = classes2plot;
classes2plotSubset = unique(UI.classes.plot);
end
classes2plotSubset = intersect(UI.classes.plot(UI.params.subset),groups2plot);
end
% Defining synaptic connections
UI = defineSynapticConnections(UI);
% Defining synaptically identified projecting cell
if UI.preferences.displayExcitatory && ~isempty(UI.cells.excitatory)
UI.cells.excitatory_subset = intersect(UI.params.subset,UI.cells.excitatory);
else
UI.cells.excitatory_subset = [];
end
if UI.preferences.displayInhibitory && ~isempty(UI.cells.inhibitory)
UI.cells.inhibitory_subset = intersect(UI.params.subset,UI.cells.inhibitory);
else
UI.cells.inhibitory_subset = [];
end
if UI.preferences.displayExcitatoryPostsynapticCells && ~isempty(UI.cells.excitatoryPostsynaptic)
UI.cells.excitatoryPostsynaptic_subset = intersect(UI.params.subset,UI.cells.excitatoryPostsynaptic);
else
UI.cells.excitatoryPostsynaptic_subset = [];
end
if UI.preferences.displayInhibitoryPostsynapticCells && ~isempty(UI.cells.inhibitoryPostsynaptic)
UI.cells.inhibitoryPostsynaptic_subset = intersect(UI.params.subset,UI.cells.inhibitoryPostsynaptic);
else
UI.cells.inhibitoryPostsynaptic_subset = [];
end
% Group display definition
if ColorVal > 2 && ColorVal < 5
UI.classes.colors = UI.preferences.cellTypeColors(intersect(classes2plotSubset,UI.classes.plot(UI.params.subset)),:);
elseif ColorVal == 2 || (GroupVal == 1 && ColorVal < 5)
UI.classes.colors = UI.preferences.cellTypeColors(intersect(classes2plot,UI.classes.plot(UI.params.subset)),:);
else
UI.classes.colors = hsv(length(nanUnique(UI.classes.plot(UI.params.subset))))*0.8;
if isnan(UI.classes.colors)
UI.classes.colors = UI.preferences.cellTypeColors(1,:);
end
end
% Ground truth and reference data colors
if ~strcmp(UI.preferences.referenceData, 'None')
UI.classes.colors2 = UI.preferences.cellTypeColors(intersect(referenceData.clusClas,referenceData.selection),:);
end
if ~strcmp(UI.preferences.groundTruthData, 'None')
UI.classes.colors3 = UI.preferences.groundTruthColors(intersect(groundTruthData.clusClas,groundTruthData.selection),:);
end
% Updating table for selected cell
updateTableColumnWidth
if UI.preferences.metricsTable==1 && highlightCurrentCell
UI.table.Data(:,2) = table_metrics(:,ii);
elseif UI.preferences.metricsTable==2 && highlightCurrentCell
updateCellTableData;
end
% Updating title
if isfield(cell_metrics,'sessionName') && isfield(cell_metrics.general,'batch') && highlightCurrentCell
UI.title.String = ['Cell class: ', UI.preferences.cellTypes{clusClas(ii)},', ' , num2str(ii),'/', num2str(cell_metrics.general.cellCount),' (batch ',num2str(batchIDs),'/',num2str(length(cell_metrics.general.batch)),') - UID: ', num2str(cell_metrics.UID(ii)),'/',num2str(general.cellCount),', electrode group: ', num2str(cell_metrics.electrodeGroup(ii)),', session: ', cell_metrics.sessionName{ii},', animal: ',cell_metrics.animal{ii}];
elseif highlightCurrentCell
UI.title.String = ['Cell Class: ', UI.preferences.cellTypes{clusClas(ii)},', ', num2str(ii),'/', num2str(cell_metrics.general.cellCount),' - electrode group: ', num2str(cell_metrics.electrodeGroup(ii))];
else
UI.title.String = '';
end
% Enabling axes panning
UI.drag.pan.Enable = 'on';
enableInteractions
UI.pan.allow = true(1,9);
%% % % % % % % % % % % % % % % % % % % % % %
% Subfig 1
% % % % % % % % % % % % % % % % % % % % % %
if any(UI.preferences.customPlotHistograms == [1,3,4])
% Saving view for previous cell
[az,el] = view(UI.axes(1));
end
% Deleting children of axes
delete(UI.axes(1).Children)
delete(h_scatter2.Children)
delete(h_scatter3.Children)
% % % % % Regular plot with/without histograms
if any(UI.preferences.customPlotHistograms == [1,2])
if UI.preferences.customPlotHistograms == 2 || strcmp(UI.preferences.referenceData, 'Histogram') || strcmp(UI.preferences.groundTruthData, 'Histogram')
% Double kernel-histogram with scatter plot
set(UI.axes(1),'Position', [0.30 0.30 0.685 0.675], 'visible', 'on', 'Clipping','on');
set(h_scatter2,'Position', [0.30 0.01 0.685 0.2], 'visible', 'on');
set(h_scatter3,'Position', [0.01 0.30 0.2 0.675], 'visible', 'on');
h_scatter2.YLabel.String = UI.preferences.rainCloudNormalization;
h_scatter3.YLabel.String = UI.preferences.rainCloudNormalization;
% set(UI.fig,'CurrentAxes',UI.axes(1))
% view(h_scatter3,[90 -90])
if UI.checkbox.logx.Value == 1
set(h_scatter2, 'XScale', 'log')
else
set(h_scatter2, 'XScale', 'linear')
end
linkaxes([UI.axes(1) h_scatter2],'x')
if UI.checkbox.logy.Value == 1
set(h_scatter3, 'XScale', 'log')
else
set(h_scatter3, 'XScale', 'linear')
end
set(UI.fig,'CurrentAxes',UI.axes(1))
else
set(UI.axes(1),'Position', [0.058812664907652 0.049837398373984 0.931187335092348 0.923495934959349], 'visible', 'on', 'Clipping','on');
set(h_scatter2,'Position', [-1 -1 0.1 0.1], 'visible', 'off');
set(h_scatter3,'Position', [-1 -1 0.1 0.1], 'visible', 'off');
set(UI.fig,'CurrentAxes',UI.axes(1))
end
if ((strcmp(UI.preferences.referenceData, 'Image') && ~isempty(reference_cell_metrics)) || (strcmp(UI.preferences.groundTruthData, 'Image')) && ~isempty(groundTruth_cell_metrics)) && UI.checkbox.logy.Value == 1
yyaxis right, hold on
UI.axes(1).YAxis(1).Color = 'k';
UI.axes(1).YAxis(2).Color = 'k';
end
UI.axes(1).YLabel.String = UI.labels.(UI.plot.yTitle);
UI.axes(1).XLabel.String = UI.labels.(UI.plot.xTitle);
set(UI.axes(1), 'XTickMode', 'auto', 'XTickLabelMode', 'auto', 'YTickMode', 'auto', 'YTickLabelMode', 'auto', 'ZTickMode', 'auto', 'ZTickLabelMode', 'auto'),
xlim auto, ylim auto, zlim auto, axis tight
% Setting linear/log scale
if UI.checkbox.logx.Value == 1
set(UI.axes(1), 'XScale', 'log')
else
set(UI.axes(1), 'XScale', 'linear')
end
if UI.checkbox.logy.Value == 1
set(UI.axes(1), 'YScale', 'log')
else
set(UI.axes(1), 'YScale', 'linear')
end
% % % % % 2D plot
% set(UI.axes(1),'ButtonDownFcn',@ClicktoSelectFromPlot),
view([0 90]);
if UI.checkbox.logx.Value == 1
AA = cell_metrics.(UI.plot.xTitle)(UI.params.subset);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_x = [nanmin(AA),max(AA)];
else
AA = cell_metrics.(UI.plot.xTitle)(UI.params.subset);
AA = AA( ~isnan(AA) & ~isinf(AA));
fig1_axislimit_x = [nanmin(AA),max(AA)];
end
if isempty(fig1_axislimit_x)
fig1_axislimit_x = [0 1];
elseif diff(fig1_axislimit_x) == 0
fig1_axislimit_x = fig1_axislimit_x + [-1 1];
end
if UI.checkbox.logy.Value == 1
AA = cell_metrics.(UI.plot.yTitle)(UI.params.subset);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_y = [nanmin(AA),max(AA)];
else
AA = cell_metrics.(UI.plot.yTitle)(UI.params.subset);
AA = AA( ~isnan(AA) & ~isinf(AA));
fig1_axislimit_y = [nanmin(AA),max(AA)];
end
if isempty(fig1_axislimit_y)
fig1_axislimit_y = [0 1];
elseif diff(fig1_axislimit_y) == 0
fig1_axislimit_y = fig1_axislimit_y + [-1 1];
end
% Reference data
if strcmp(UI.preferences.referenceData, 'Points') && ~isempty(reference_cell_metrics) && isfield(reference_cell_metrics,UI.plot.xTitle) && isfield(reference_cell_metrics,UI.plot.yTitle)
idx = find(ismember(referenceData.clusClas,referenceData.selection));
ce_gscatter(reference_cell_metrics.(UI.plot.xTitle)(idx), reference_cell_metrics.(UI.plot.yTitle)(idx), referenceData.clusClas(idx), UI.classes.colors2,8,'x');
elseif strcmp(UI.preferences.referenceData, 'Image') && ~isempty(reference_cell_metrics) && UI.checkbox.logx.Value == 0 && isfield(reference_cell_metrics,UI.plot.xTitle) && isfield(reference_cell_metrics,UI.plot.yTitle)
if ~exist('referenceData1','var') || ~isfield(referenceData1,'z') || ~strcmp(referenceData1.x_field,UI.plot.xTitle) || ~strcmp(referenceData1.y_field,UI.plot.yTitle) || referenceData1.x_log ~= UI.checkbox.logx.Value || referenceData1.y_log ~= UI.checkbox.logy.Value || ~strcmp(referenceData1.plotType, 'Image')
if UI.checkbox.logx.Value == 1
referenceData1.x = linspace(log10(nanmin([reference_cell_metrics.(UI.plot.xTitle),fig1_axislimit_x(1)])),log10(nanmax([reference_cell_metrics.(UI.plot.xTitle),fig1_axislimit_x(2)])),UI.preferences.binCount);
xdata = log10(reference_cell_metrics.(UI.plot.xTitle));
else
referenceData1.x = linspace(nanmin([reference_cell_metrics.(UI.plot.xTitle),fig1_axislimit_x(1)]),nanmax([reference_cell_metrics.(UI.plot.xTitle),fig1_axislimit_x(2)]),UI.preferences.binCount);
xdata = reference_cell_metrics.(UI.plot.xTitle);
end
if UI.checkbox.logy.Value == 1
AA = reference_cell_metrics.(UI.plot.yTitle);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
referenceData1.y = linspace(log10(nanmin([AA,fig1_axislimit_y(1)])),log10(nanmax([AA,fig1_axislimit_y(2)])),UI.preferences.binCount);
ydata = log10(reference_cell_metrics.(UI.plot.yTitle));
else
AA = reference_cell_metrics.(UI.plot.yTitle);
AA = AA( ~isnan(AA) & ~isinf(AA));
referenceData1.y = linspace(nanmin([AA,fig1_axislimit_y(1)]),nanmax([AA,fig1_axislimit_y(2)]),UI.preferences.binCount);
ydata = reference_cell_metrics.(UI.plot.yTitle);
end
referenceData1.x_field = UI.plot.xTitle;
referenceData1.y_field = UI.plot.yTitle;
referenceData1.x_log = UI.checkbox.logx.Value;
referenceData1.y_log = UI.checkbox.logy.Value;
referenceData1.plotType = 'Image';
colors = (1-(UI.preferences.cellTypeColors)) * 250;
referenceData1.z = zeros(length(referenceData1.x)-1,length(referenceData1.y)-1,3,size(colors,1));
for m = referenceData.selection
idx = find(referenceData.clusClas==m);
[z_referenceData_temp,~,~] = histcounts2(xdata(idx), ydata(idx),referenceData1.x,referenceData1.y,'norm','probability');
referenceData1.z(:,:,:,m) = bsxfun(@times,repmat(conv2(z_referenceData_temp,gauss2d,'same'),1,1,3),reshape(colors(m,:),1,1,[]));
end
referenceData1.x = referenceData1.x(1:end-1)+(referenceData1.x(2)-referenceData1.x(1))/2;
referenceData1.y = referenceData1.y(1:end-1)+(referenceData1.y(2)-referenceData1.y(1))/2;
end
if strcmp(UI.preferences.referenceData, 'Image') && ~isempty(reference_cell_metrics) && UI.checkbox.logy.Value == 1
yyaxis left, hold on
set(gca,'YTick',[])
end
% Image plot
referenceData1.image = 1-sum(referenceData1.z(:,:,:,referenceData.selection),4);
referenceData1.image = flip(referenceData1.image,2);
referenceData1.image = rot90(referenceData1.image);
legendScatter2 = image(referenceData1.x,referenceData1.y,referenceData1.image,'HitTest','off', 'PickableParts', 'none'); axis tight
set(legendScatter2,'HitTest','off')
if ~isempty(reference_cell_metrics) && UI.checkbox.logy.Value == 1
yyaxis right, hold on
end
end
% Ground truth data
if strcmp(UI.preferences.groundTruthData, 'Points') && ~isempty(groundTruth_cell_metrics) && isfield(groundTruth_cell_metrics,UI.plot.xTitle) && isfield(groundTruth_cell_metrics,UI.plot.yTitle)
idx = find(ismember(groundTruthData.clusClas,groundTruthData.selection));
ce_gscatter(groundTruth_cell_metrics.(UI.plot.xTitle)(idx), groundTruth_cell_metrics.(UI.plot.yTitle)(idx), groundTruthData.clusClas(idx), UI.classes.colors3,8,UI.preferences.groundTruthMarker);
elseif strcmp(UI.preferences.groundTruthData, 'Image') && ~isempty(groundTruth_cell_metrics) && UI.checkbox.logx.Value == 0 && isfield(groundTruth_cell_metrics,UI.plot.xTitle) && isfield(groundTruth_cell_metrics,UI.plot.yTitle)
if ~exist('groundTruthData1','var') || ~isfield(groundTruthData1,'z') || ~strcmp(groundTruthData1.x_field,UI.plot.xTitle) || ~strcmp(groundTruthData1.y_field,UI.plot.yTitle) || groundTruthData1.x_log ~= UI.checkbox.logx.Value || groundTruthData1.y_log ~= UI.checkbox.logy.Value
if UI.checkbox.logx.Value == 1
groundTruthData1.x = linspace(log10(nanmin([fig1_axislimit_x(1),groundTruth_cell_metrics.(UI.plot.xTitle)])),log10(nanmax([fig1_axislimit_x(2),groundTruth_cell_metrics.(UI.plot.xTitle)])),UI.preferences.binCount);
xdata = log10(groundTruth_cell_metrics.(UI.plot.xTitle));
else
groundTruthData1.x = linspace(nanmin([fig1_axislimit_x(1),groundTruth_cell_metrics.(UI.plot.xTitle)]),nanmax([fig1_axislimit_x(2),groundTruth_cell_metrics.(UI.plot.xTitle)]),UI.preferences.binCount);
xdata = groundTruth_cell_metrics.(UI.plot.xTitle);
end
if UI.checkbox.logy.Value == 1
groundTruthData1.y = linspace(log10(nanmin([fig1_axislimit_y(1),groundTruth_cell_metrics.(UI.plot.yTitle)])),log10(nanmax([fig1_axislimit_y(2),groundTruth_cell_metrics.(UI.plot.yTitle)])),UI.preferences.binCount);
ydata = log10(groundTruth_cell_metrics.(UI.plot.yTitle));
else
groundTruthData1.y = linspace(nanmin([fig1_axislimit_y(1),groundTruth_cell_metrics.(UI.plot.yTitle)]),nanmax([fig1_axislimit_y(2),groundTruth_cell_metrics.(UI.plot.yTitle)]),UI.preferences.binCount);
ydata = groundTruth_cell_metrics.(UI.plot.yTitle);
end
groundTruthData1.x_field = UI.plot.xTitle;
groundTruthData1.y_field = UI.plot.yTitle;
groundTruthData1.x_log = UI.checkbox.logx.Value;
groundTruthData1.y_log = UI.checkbox.logy.Value;
colors = (1-(UI.preferences.groundTruthColors)) * 250;
groundTruthData1.z = zeros(length(groundTruthData1.x)-1,length(groundTruthData1.y)-1,3,size(colors,1));
for m = unique(groundTruthData.clusClas)
idx = find(groundTruthData.clusClas==m);
[z_referenceData_temp,~,~] = histcounts2(xdata(idx), ydata(idx),groundTruthData1.x,groundTruthData1.y,'norm','probability');
groundTruthData1.z(:,:,:,m) = bsxfun(@times,repmat(conv2(z_referenceData_temp,gauss2d,'same'),1,1,3),reshape(colors(m,:),1,1,[]));
end
groundTruthData1.x = groundTruthData1.x(1:end-1)+(groundTruthData1.x(2)-groundTruthData1.x(1))/2;
groundTruthData1.y = groundTruthData1.y(1:end-1)+(groundTruthData1.y(2)-groundTruthData1.y(1))/2;
end
if strcmp(UI.preferences.groundTruthData, 'Image') && ~isempty(groundTruth_cell_metrics) && UI.checkbox.logy.Value == 1
yyaxis left, hold on
set(gca,'YTick',[])
end
% Image plot
groundTruthData1.image = 1-sum(groundTruthData1.z(:,:,:,groundTruthData.selection),4);
groundTruthData1.image = flip(groundTruthData1.image,2);
groundTruthData1.image = rot90(groundTruthData1.image);
legendScatter2 = image(groundTruthData1.x,groundTruthData1.y,groundTruthData1.image,'HitTest','off', 'PickableParts', 'none'); axis tight
set(legendScatter2,'HitTest','off'),legendScatter2.AlphaData = 0.9;
alpha(0.3)
if strcmp(UI.preferences.referenceData, 'Image') && ~isempty(groundTruth_cell_metrics) && UI.checkbox.logy.Value == 1
yyaxis right, hold on
end
end
plotGroupData(plotX,plotY,plotConnections(1),highlightCurrentCell)
if UI.preferences.plotLinearFits
plotLinearFits(plotX,plotY)
end
% Axes limits
if ~strcmp(UI.preferences.groundTruthData, 'None')
idx = find(ismember(groundTruthData.clusClas,groundTruthData.selection));
if UI.checkbox.logx.Value == 1
AA = groundTruth_cell_metrics.(UI.plot.xTitle)(idx);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_x_groundTruth = [nanmin(AA),max(AA)];
else
fig1_axislimit_x_groundTruth = [min(groundTruth_cell_metrics.(UI.plot.xTitle)(idx)),max(groundTruth_cell_metrics.(UI.plot.xTitle)(idx))];
end
if isempty(fig1_axislimit_x_groundTruth)
fig1_axislimit_x_groundTruth = [0 1];
elseif diff(fig1_axislimit_x_groundTruth) == 0
fig1_axislimit_x_groundTruth = fig1_axislimit_x_groundTruth + [-1 1];
end
if UI.checkbox.logy.Value == 1
AA = groundTruth_cell_metrics.(UI.plot.yTitle)(idx);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_y_groundTruth = [nanmin(AA),max(AA)];
else
fig1_axislimit_y_groundTruth = [min(groundTruth_cell_metrics.(UI.plot.yTitle)(idx)),max(groundTruth_cell_metrics.(UI.plot.yTitle)(idx))];
end
if isempty(fig1_axislimit_y_groundTruth)
fig1_axislimit_y_groundTruth = [0 1];
elseif diff(fig1_axislimit_y_groundTruth) == 0
fig1_axislimit_y_groundTruth = fig1_axislimit_y_groundTruth + [-1 1];
end
end
if ~strcmp(UI.preferences.referenceData, 'None')
idx = find(ismember(referenceData.clusClas,referenceData.selection));
if UI.checkbox.logx.Value == 1
AA = reference_cell_metrics.(UI.plot.xTitle)(idx);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_x_reference = [nanmin(AA),max(AA)];
else
fig1_axislimit_x_reference = [min(reference_cell_metrics.(UI.plot.xTitle)(idx)),max(reference_cell_metrics.(UI.plot.xTitle)(idx))];
end
if isempty(fig1_axislimit_x_reference)
fig1_axislimit_x_reference = [0 1];
elseif diff(fig1_axislimit_x_reference) == 0
fig1_axislimit_x_reference = fig1_axislimit_x_reference + [-1 1];
end
if UI.checkbox.logy.Value == 1
AA = reference_cell_metrics.(UI.plot.yTitle)(idx);
AA = AA( ~isnan(AA) & ~isinf(AA) & AA>0);
fig1_axislimit_y_reference = [nanmin(AA),max(AA)];
else
fig1_axislimit_y_reference = [min(reference_cell_metrics.(UI.plot.yTitle)(idx)),max(reference_cell_metrics.(UI.plot.yTitle)(idx))];
end
if isempty(fig1_axislimit_y_reference)
fig1_axislimit_y_reference = [0 1];
elseif diff(fig1_axislimit_y_reference) == 0
fig1_axislimit_y_reference = fig1_axislimit_y_reference + [-1 1];
end
end
if contains(UI.plot.xTitle,'_num')
xticks([1:length(groups_ids.(UI.plot.xTitle))]), xticklabels(groups_ids.(UI.plot.xTitle)),xtickangle(20),
xlim([0.5,length(groups_ids.(UI.plot.xTitle))+0.5001]),
% UI.axes(1).XLabel.String = UI.plot.xTitle(1:end-4);
UI.axes(1).XLabel.Interpreter = 'none';
end
if contains(UI.plot.yTitle,'_num')
yticks([1:length(groups_ids.(UI.plot.yTitle))]), yticklabels(groups_ids.(UI.plot.yTitle)),ytickangle(65),
ylim([0.5,length(groups_ids.(UI.plot.yTitle))+0.5001]),
% UI.axes(1).YLabel.String = UI.plot.yTitle(1:end-4);
UI.axes(1).YLabel.Interpreter = 'none';
end
if length(unique(UI.classes.plot(UI.params.subset)))==2
% G1 = plotX(UI.params.subset);
G = findgroups(UI.classes.plot(UI.params.subset));
if ~isempty(UI.params.subset(G==1)) && ~isempty(UI.params.subset(G==2))
if ~all(plotX(UI.params.subset(G==1))) && ~all(plotX(UI.params.subset(G==2)))
[h,p] = kstest2(plotX(UI.params.subset(G==1)),plotX(UI.params.subset(G==2)));
text(0.97,0.02,['h=', num2str(h), ', p=',num2str(p,3)],'Units','normalized','Rotation',90,'Interpreter', 'none','Interpreter', 'none','HitTest','off','BackgroundColor',[1 1 1 0.7],'margin',0.5)
end
if ~all(plotY(UI.params.subset(G==1))) && ~all(plotY(UI.params.subset(G==2)))
[h,p] = kstest2(plotY(UI.params.subset(G==1)),plotY(UI.params.subset(G==2)));
text(0.02,0.97,['h=', num2str(h), ', p=',num2str(p,3)],'Units','normalized','Interpreter', 'none','Interpreter', 'none','HitTest','off','BackgroundColor',[1 1 1 0.7],'margin',0.5)
end
end
end
[az,el] = view;
if strcmp(UI.preferences.groundTruthData, 'None') && ~strcmp(UI.preferences.referenceData, 'None')
xlim([min(fig1_axislimit_x(1),fig1_axislimit_x_reference(1)),max(fig1_axislimit_x(2),fig1_axislimit_x_reference(2))])
ylim([min(fig1_axislimit_y(1),fig1_axislimit_y_reference(1)),max(fig1_axislimit_y(2),fig1_axislimit_y_reference(2))])
elseif ~strcmp(UI.preferences.groundTruthData, 'None') && strcmp(UI.preferences.referenceData, 'None') && ~isempty(fig1_axislimit_x_groundTruth) && ~isempty(fig1_axislimit_y_groundTruth)
xlim([min(fig1_axislimit_x(1),fig1_axislimit_x_groundTruth(1)),max(fig1_axislimit_x(2),fig1_axislimit_x_groundTruth(2))])
ylim([min(fig1_axislimit_y(1),fig1_axislimit_y_groundTruth(1)),max(fig1_axislimit_y(2),fig1_axislimit_y_groundTruth(2))])
elseif ~strcmp(UI.preferences.groundTruthData, 'None') && ~strcmp(UI.preferences.referenceData, 'None')
xlim([min([fig1_axislimit_x(1),fig1_axislimit_x_groundTruth(1),fig1_axislimit_x_reference(1)]),max([fig1_axislimit_x(2),fig1_axislimit_x_groundTruth(2),fig1_axislimit_x_reference(2)])])
ylim([min([fig1_axislimit_y(1),fig1_axislimit_y_groundTruth(1),fig1_axislimit_y_reference(1)]),max([fig1_axislimit_y(2),fig1_axislimit_y_groundTruth(2),fig1_axislimit_y_reference(2)])])
else
xlim(fig1_axislimit_x), ylim(fig1_axislimit_y)
end
xlim11 = xlim;
ylim11 = ylim;
if UI.preferences.customPlotHistograms == 2
plotClas_subset = UI.classes.plot(UI.params.subset);
ids = nanUnique(plotClas_subset);
ids_count = histc(plotClas_subset, ids);
for m = 1:length(unique(UI.classes.plot(UI.params.subset)))
temp9 = UI.params.subset(find(plotClas_subset==ids(m)));
if length(temp9)>1 && any(~isnan(plotX(temp9)))
densityPlot(plotX(temp9),h_scatter2,UI.classes.colors(m,:),UI.classes.colors(m,:),UI.checkbox.logx.Value)
end
end
if UI.preferences.plotLinearFits
plotLinearFits(plotX,plotY)
end
xlim(h_scatter2, xlim11)
for m = 1:length(unique(UI.classes.plot(UI.params.subset)))
temp1 = UI.params.subset(find(plotClas_subset==ids(m)));
if length(temp1)>1 && any(~isnan(plotY(temp1)))
densityPlot(plotY(temp1),h_scatter3,UI.classes.colors(m,:),UI.classes.colors(m,:),UI.checkbox.logy.Value)
end
end
xlim(h_scatter3,ylim11)
end
if strcmp(UI.preferences.groundTruthData, 'Histogram') && ~isempty(groundTruth_cell_metrics) && isfield(groundTruth_cell_metrics,UI.plot.xTitle) && isfield(groundTruth_cell_metrics,UI.plot.yTitle)
groundTruthData1.x = densityPlotRefData(groundTruth_cell_metrics.(UI.plot.xTitle),h_scatter2,num2cell(UI.classes.colors3,2),UI.checkbox.logx.Value,xlim11,groundTruthData);