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icatb_dfnc_stats.m
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icatb_dfnc_stats.m
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function icatb_dfnc_stats(param_file, outputDir)
%% Do stats on dfnc correlations.
%
icatb_defaults;
global UI_FS;
if (~exist('param_file', 'var'))
param_file = icatb_selectEntry('title', 'Select dFNC Parameter File', 'typeEntity', 'file', 'typeSelection', ...
'single', 'filter', '*dfnc.mat');
drawnow;
end
if (isempty(param_file))
error('Please select dFNC parameter file');
end
if (ischar(param_file))
load(param_file);
dfncOutputDir = fileparts(param_file);
if (isempty(dfncOutputDir))
dfncOutputDir = pwd;
end
if (~exist('dfncInfo', 'var'))
error(['Selected file ', param_file, ' is not a valid dFNC parameter file']);
end
else
dfncInfo = param_file;
dfncOutputDir = dfncInfo.userInput.outputDir;
clear param_file;
end
drawnow;
check_nuisance = icatb_questionDialog('title', 'Remove nuisance covariates', 'textbody', 'Do you want to remove variance associated with nuisance covariates?');
nuisance_cov_file = '';
drawnow;
if (check_nuisance)
nuisance_cov_file = icatb_selectEntry('title', 'Select nuisance covariates file (continuous covariates only)', 'typeEntity', 'file', 'typeSelection', 'single', 'filter', '*.asc;*.dat');
end
drawnow;
%% Check post process file
post_process_file = fullfile(dfncOutputDir, [dfncInfo.prefix, '_post_process.mat']);
if (~exist(post_process_file, 'file'))
error('Please run postprocess step in order to use dfnc cluster stats');
end
load (post_process_file, 'clusterInfo');
Nwin = length(clusterInfo.IDXall) / length(dfncInfo.outputFiles);
if (~exist('outputDir', 'var'))
outputDir = icatb_selectEntry('title', 'Select stats output directory ...', 'typeEntity', 'directory');
end
drawnow;
if (isempty(outputDir))
outputDir = pwd;
end
defaultWindowSize = min([Nwin, 10]);
% %% Compute cluster stats
% cluster_stats_file = fullfile(outputDir, [dfncInfo.prefix, '_cluster_stats.mat']);
% % open input dialog box
% prompt = {['Enter threshold in windows to compute median of correlations across windows (Max is ', num2str(Nwin), ')']};
% dlg_title = 'Threshold in windows';
% num_lines = 1;
% def = {num2str(defaultWindowSize)};
% % save the file with the file name specified
% answer = icatb_inputdlg2(prompt, dlg_title, num_lines, def);
% if (isempty(answer))
% error('Threshold in windows is not selected');
% end
% thresholdWindows = str2num(answer{1});
% disp(['Atleast ', num2str(thresholdWindows), ' windows must be present in each state to compute median of dFNC correlations']);
% disp('Using only median of dFNC correlations for each cluster state...');
% icatb_dfnc_cluster_stats(dfncInfo, outputDir, thresholdWindows);
% drawnow;
%
% load (cluster_stats_file, 'dfnc_corrs');
%
% drawnow;
desCriteriaOptions = {'One sample t-test', 'Two sample t-test', 'Paired T-test'};
figureData.outputDir = outputDir;
figureData.prefix = dfncInfo.prefix;
figureData.dfncOutputDir = dfncOutputDir;
figureData.Nwin = Nwin;
%figureData.cluster_stats_file = cluster_stats_file;
figureData.numOfSub = dfncInfo.userInput.numOfSub;
figureData.numOfSess = dfncInfo.userInput.numOfSess;
figureData.cov = [];
figureData.contrasts = [];
figureData.threshdesc = 'none';
figureData.p_thresh = 0.05;
figureData.dfncInfo = dfncInfo;
figureData.nuisance_cov_file = nuisance_cov_file;
clear loading_coeff;
%% Cleanup existing figures
figureTag = 'setup_dfnc_stats_gui';
figHandle = findobj('tag', figureTag);
if (~isempty(figHandle))
delete(figHandle);
end
%% Setup figure for GUI
InputHandle = icatb_getGraphics('dFNC Stats GUI', 'normal', figureTag, 'on');
set(InputHandle, 'menubar', 'none');
set(InputHandle, 'userdata', figureData);
defaultsmenu = uimenu('parent', InputHandle, 'label', 'Stats-Defaults', 'tag', 'dfnc_stats_defaults');
set(defaultsmenu, 'callback', {@defaultsCallback, InputHandle});
displaymenu = uimenu('parent', InputHandle, 'label', 'HTML Report', 'tag', 'dfnc_stats_defaults_html');
set(displaymenu, 'callback', {@displayCallback, InputHandle});
%% Plot controls
xOffset = 0.04; yOffset = 0.04;
popupTextHeight = 0.05; popupTextWidth = 0.45; yPos = 0.95;
popupTextPos = [xOffset, yPos - yOffset - popupTextHeight, popupTextWidth, popupTextHeight];
% Plot Text
designTextH = icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'text', ...
'position', popupTextPos, 'String', 'Select design criteria', 'fontsize', UI_FS - 1, ...
'horizontalalignment', 'center');
designTextH = icatb_wrapStaticText(designTextH);
popupTextPos = get(designTextH, 'position');
popupWidth = 0.32;
popupPos = popupTextPos;
popupPos(1) = popupPos(1) + popupPos(3) + xOffset;
popupPos(3) = popupWidth;
% Plot popup
designPopupH = icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'popup', ...
'position', popupPos, 'String', desCriteriaOptions, 'fontsize', UI_FS - 1, 'tag', 'design_criteria', 'callback', {@designCallback, InputHandle});
promptHeight = 0.05;
promptWidth = popupTextWidth;
promptPos = [xOffset, popupPos(2) - popupPos(4) - 2*yOffset, promptWidth, promptHeight];
promptH = icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'text', ...
'position', promptPos, 'String', ['Enter threshold in windows (Max = ', num2str(Nwin), ')'], 'fontsize', UI_FS - 1, ...
'horizontalalignment', 'center');
promptH = icatb_wrapStaticText(promptH);
popupPos = get(promptH, 'position');
popupPos(1) = popupPos(1) + popupPos(3) + xOffset;
popupPos(3) = 0.12;
popupPos(4) = promptHeight;
editH = icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'edit', 'position', popupPos, 'String', num2str(defaultWindowSize), 'fontsize', ...
UI_FS - 1, 'tag', 'threshold_windows');
% controlWidth = 0.4;
% listboxHeight = controlWidth; listboxWidth = controlWidth;
%
% %% Covariates listbox
% promptPos = [0.5 - 0.5*controlWidth, popupTextPos(2) - popupTextPos(4) - 2*yOffset, promptWidth, promptHeight];
%
% textH = icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'text', 'position', promptPos, 'string', 'Add Covariates', 'tag', ...
% 'prompt_covariates', 'fontsize', UI_FS - 1);
% icatb_wrapStaticText(textH);
% listboxXOrigin = promptPos(1);
% listboxYOrigin = promptPos(2) - 2*yOffset - listboxHeight;
% listboxPos = [listboxXOrigin, listboxYOrigin, listboxWidth, listboxHeight];
% covNames = '';
% icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'listbox', 'position', listboxPos, 'string', covNames, 'tag', ...
% 'cov', 'enable', 'off', 'fontsize', UI_FS - 1, 'min', 0, 'max', 2, 'value', 1, 'callback', {@addCov, InputHandle});
% addButtonPos = [listboxPos(1) + listboxPos(3) + xOffset, listboxPos(2) + 0.5*listboxPos(4) + 0.5*promptHeight, promptHeight + 0.01, promptHeight - 0.01];
% removeButtonPos = [listboxPos(1) + listboxPos(3) + xOffset, listboxPos(2) + 0.5*listboxPos(4) - 0.5*promptHeight, promptHeight + 0.01, promptHeight - 0.01];
% icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'pushbutton', 'position', addButtonPos, 'string', '+', 'tag', 'add_cov_button', 'fontsize',...
% UI_FS - 1, 'enable', 'off', 'callback', {@addCov, InputHandle});
% icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'pushbutton', 'position', removeButtonPos, 'string', '-', 'tag', 'remove_cov_button', 'fontsize',...
% UI_FS - 1, 'enable', 'off', 'callback', {@removeCov, InputHandle});
%
calculateWidth = 0.16;
calculateHeight = 0.05;
yPos = 0.16;
calculatePos = [0.5 - 0.5*calculateWidth, yPos, calculateWidth, calculateHeight];
icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'pushbutton', 'position', calculatePos, 'string', 'Calculate', ...
'tag', 'calculate_button', 'fontsize', UI_FS - 1, 'callback', {@calculateCallback, InputHandle});
% calculateWidth = 0.16;
% calculateHeight = 0.05;
% calculatePos = [0.75 - 0.5*calculateWidth, listboxPos(2) - calculateHeight - 1.5*yOffset, calculateWidth, calculateHeight];
% icatb_uicontrol('parent', InputHandle, 'units', 'normalized', 'style', 'pushbutton', 'position', calculatePos, 'string', 'Display', ...
% 'tag', 'display_button', 'fontsize', UI_FS - 1, 'callback', {@displayCallback, InputHandle}, 'tooltipstring', 'Open HTML in web browser ...');
%designCallback(designPopupH, [], InputHandle);
function addCov(hObject, event_data, figH)
%% Add Covariates
%
icatb_defaults;
global UI_FS;
figureTag = 'add_cov_mancovan';
covFigHandle = findobj('tag', figureTag);
if (~isempty(covFigHandle))
delete(covFigHandle);
end
statsInfo = get(figH, 'userdata');
listH = findobj(figH, 'tag', 'cov');
covName = '';
transformationName = '';
covVals = '';
cov_type = 'continuous';
covTypes = {'Continuous', 'Categorical'};
covTypeVal = 1;
if (listH == hObject)
if (~strcmpi(get(figH, 'selectionType'), 'open'))
return;
end
val = get(listH, 'value');
try
covName = statsInfo.cov(val).name;
covVals = statsInfo.cov(val).value;
if (isnumeric(covVals))
covVals = covVals(:);
covVals = num2str(covVals);
end
covVals = cellstr(covVals);
covVals = covVals(:)';
transformationName = statsInfo.cov(val).transformation;
cov_type = statsInfo.cov(val).type;
covTypeVal = strmatch(lower(cov_type), lower(covTypes), 'exact');
catch
end
end
covFigHandle = icatb_getGraphics('Select Covariates', 'normal', figureTag);
set(covFigHandle, 'menubar', 'none');
promptWidth = 0.52; controlWidth = 0.32; promptHeight = 0.05;
xOffset = 0.02; yOffset = promptHeight; yPos = 0.9;
okWidth = 0.12; okHeight = promptHeight;
%% Covariate name and value
promptPos = [xOffset, yPos - 0.5*yOffset, promptWidth, promptHeight];
textH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'text', 'position', promptPos, 'string', 'Enter Covariate Name', 'fontsize', UI_FS - 1);
icatb_wrapStaticText(textH);
promptPos = get(textH, 'position');
editPos = promptPos;
editPos(1) = editPos(1) + editPos(3) + xOffset;
editPos(3) = controlWidth;
editH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'edit', 'position', editPos, 'string', covName, 'tag', 'cov_name', 'fontsize', UI_FS - 1);
%% Type of covariate (Continuous or categorical)
promptPos = [xOffset, editPos(2) - 0.5*promptHeight - yOffset, promptWidth, promptHeight];
textH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'text', 'position', promptPos, 'string', 'Select Type Of Covariate', 'fontsize', UI_FS - 1);
icatb_wrapStaticText(textH);
promptPos = get(textH, 'position');
editPos = promptPos;
editPos(1) = editPos(1) + editPos(3) + xOffset;
editPos(3) = controlWidth;
covH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'popup', 'position', editPos, 'string', {'Continuous', 'Categorical'}, 'tag', 'cov_type', 'fontsize', UI_FS - 1, ...
'value', covTypeVal);
promptPos(2) = promptPos(2) - 0.5*promptHeight - yOffset;
promptPos(1) = 0.5 - 0.5*promptWidth;
promptPos(3) = promptWidth;
textH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'text', 'position', promptPos, 'string', 'Add Covariate', 'fontsize', UI_FS - 1);
icatb_wrapStaticText(textH);
promptPos = get(textH, 'position');
editWidth = promptWidth;
editHeight = 0.3;
editPos = [0.5 - 0.5*editWidth, promptPos(2) - yOffset - editHeight, editWidth, editHeight];
editH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'edit', 'position', editPos, 'string', covVals, 'fontsize', UI_FS - 1, 'tag', 'cov_value', 'min', 0, 'max', 2, ...
'callback', {@covValueCallback, figH});
cmenu = uicontextmenu;
set(editH, 'uicontextmenu', cmenu);
uimenu(cmenu, 'Label', 'Load File', 'callback', {@editContextMenuCallback, covFigHandle, figH});
%% Transformation name and value
promptPos(2) = editPos(2) - promptHeight - yOffset;
promptPos(1) = xOffset;
editPos = promptPos;
textH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'text', 'position', promptPos, 'string', 'Enter transformation function (like log, atanh). Leave it as empty if you don''t want to apply transformation.', 'fontsize', UI_FS - 1, ...
'tag', 'prompt_cov_transformation');
icatb_wrapStaticText(textH);
promptPos = get(textH, 'position');
promptPos(2) = promptPos(2) + (editPos(4) - promptPos(4));
set(textH, 'position', promptPos);
editPos(1) = editPos(1) + editPos(3) + xOffset;
editPos(2) = promptPos(2) + 0.5*promptPos(4) - 0.5*editPos(4);
editPos(3) = controlWidth;
transformH = icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'edit', 'position', editPos, 'string', transformationName, 'tag', 'cov_transformation', 'fontsize', UI_FS - 1);
okPos = [0.5 - 0.5*okWidth, yOffset + 0.5*okHeight, okWidth, okHeight];
icatb_uicontrol('parent', covFigHandle, 'units', 'normalized', 'style', 'pushbutton', 'position', okPos, 'string', 'Done', 'tag', 'cov_done', 'fontsize', UI_FS - 1, 'callback', ...
{@setCovCallback, covFigHandle, figH});
set(findobj(covFigHandle, 'tag', 'cov_type'), 'callback', {@covTypeCallback, covFigHandle, transformH, textH});
covTypeCallback(findobj(covFigHandle, 'tag', 'cov_type'), [], covFigHandle, transformH, textH);
function editContextMenuCallback(hObject, event_data, handles, figH)
%% Context menu callback
statsInfo = get(figH, 'userdata');
txtFile = icatb_selectEntry('title', 'Select covariate file' , 'filter', '*.txt;*.asc', 'typeEntity', 'file', 'typeSelection', 'single');
drawnow;
covTypeH = findobj(handles, 'tag', 'cov_type');
opts = cellstr(get(covTypeH, 'string'));
covVal = get(covTypeH, 'value');
try
val = icatb_mancovan_load_covariates(txtFile, opts{covVal}, statsInfo.numOfSub);
covValueH = findobj(handles, 'tag', 'cov_value');
set(covValueH, 'string', val);
catch
icatb_errorDialog(lasterr, 'Covariate Selection');
end
function setCovCallback(hObject, event_data, covFigH, handles)
%% Set covariate name, value and type
statsInfo = get(handles, 'userdata');
covNameH = findobj(covFigH, 'tag', 'cov_name');
covValueH = findobj(covFigH, 'tag', 'cov_value');
covTypeH = findobj(covFigH, 'tag', 'cov_type');
covTransformationH = findobj(covFigH, 'tag', 'cov_transformation');
% Covariate name, value and type
cov_name = get(covNameH, 'string');
cov_value = get(covValueH, 'string');
opts = cellstr(get(covTypeH, 'string'));
val = get(covTypeH, 'value');
covType = lower(opts{val});
cov_transformation = get(covTransformationH, 'string');
try
if (isempty(cov_name))
error('Covariate name is not entered');
end
if (isempty(cov_value))
error('Covariate vector is not entered');
end
if (length(statsInfo.cov) > 0)
chk = strmatch(lower(cov_name), lower(cellstr(char(statsInfo.cov.name))), 'exact');
if (~isempty(chk))
ind = chk;
end
end
if (~exist('ind', 'var'))
ind = length(statsInfo.cov) + 1;
end
statsInfo.cov(ind).name = cov_name;
statsInfo.cov(ind).value = deblank(cov_value(:)');
statsInfo.cov(ind).transformation = lower(cov_transformation);
statsInfo.cov(ind).type = covType;
set(handles, 'userdata', statsInfo);
covListH = findobj(handles, 'tag', 'cov');
set(covListH, 'string', cellstr(char(statsInfo.cov.name)));
delete(covFigH);
catch
icatb_errorDialog(lasterr, 'Covariate Selection');
end
function removeCov(hObject, event_data, figH)
%% Remove Covariate
%
statsInfo = get(figH, 'userdata');
listH = findobj(figH, 'tag', 'cov');
val = get(listH, 'value');
if (~isempty(val))
check = icatb_questionDialog('title', 'Remove Covariate', 'textbody', 'Do you want to remove the covariate from the list?');
if (~check)
return;
end
end
try
strs = cellstr(char(statsInfo.cov.name));
statsInfo.cov(val) = [];
strs(val) = [];
set(listH, 'value', 1);
set(listH, 'string', strs);
set(figH, 'userdata', statsInfo);
catch
end
function covValueCallback(hObject, event_data, figH)
%% Covariate value callback
%
statsInfo = get(figH, 'userdata');
val = cellstr(get(hObject, 'string'));
inds = icatb_good_cells(val);
val = val(inds);
if (length(val) == 1)
val = textscan(val{1}, '%s', 'delimiter', '\t,', 'multipleDelimsAsOne', 1);
val = val{1};
end
eval('val2 = val;');
%eval(['val2 = [', [val{:}], '];']);
if (length(val2) ~= numel(val2))
error('Covariate must be entered in a vector');
end
if (length(val2) ~= statsInfo.numOfSub)
error(['Covariate vector length must equal the no. of subjects (', num2str(statsInfo.numOfSub), ')']);
end
% if (isnumeric(val2))
% val2 = val2(:);
% val2 = num2str(val2);
% end
val2 = strtrim(cellstr(val2));
set(hObject, 'string', val2);
function loading_coeff = loadCoeff(fileName, extn)
% Load coefficients
if (strcmpi(extn, '.nii') || strcmpi(extn, '.img'))
loading_coeff = icatb_loadData(fileName);
else
loading_coeff = icatb_load_ascii_or_mat(fileName);
end
loading_coeff = squeeze(loading_coeff);
if (length(loading_coeff) == numel(loading_coeff))
loading_coeff = loading_coeff(:);
end
function covTypeCallback(hObject, ed, handles, transformH, promptH)
%% Covariates Type callback
%
str = cellstr(get(hObject, 'string'));
str = str{get(hObject, 'value')};
set(promptH, 'visible', 'off');
set(promptH, 'enable', 'off');
set(transformH, 'visible', 'off');
set(transformH, 'enable', 'off');
function calculateCallback(hObject, ed, handles)
%% Calculate callback
%
figData = get(handles, 'userdata');
nuisance_cov_file = deblank(figData.nuisance_cov_file);
%% Compute median of correlations and save it
cluster_stats_file = fullfile(figData.outputDir, [figData.prefix, '_cluster_stats.mat']);
figData.cluster_stats_file = cluster_stats_file;
threshH = findobj(handles, 'tag', 'threshold_windows');
thresholdWindows = str2num(get(threshH, 'string'));
if (isempty(thresholdWindows))
error('Enter a valid number for threshold');
end
thresholdWindows = ceil(thresholdWindows);
disp(['Atleast ', num2str(thresholdWindows), ' windows must be present in each state to compute median of dFNC correlations']);
disp('Using only median of dFNC correlations for each cluster state...');
icatb_dfnc_cluster_stats(figData.dfncInfo, figData.outputDir, thresholdWindows);
save(figData.cluster_stats_file, 'nuisance_cov_file', '-append');
try
selectedRegressors = figData.dfncInfo.userInput.selectedRegressors;
catch
end
stats_log = fullfile(figData.outputDir, [figData.prefix, '_stats.log']);
diary (stats_log);
try
%% Design criteria
designCriteriaH = findobj(handles, 'tag', 'design_criteria');
designOptions = get(designCriteriaH, 'string');
designCriteriaVal = get(designCriteriaH, 'value');
designCriteria = designOptions{designCriteriaVal};
load (figData.cluster_stats_file);
if (isempty(figData.cov) && strcmpi(designCriteria, 'anova'))
error('Please enter covariates in order to do stats');
end
if (~isempty(figData.nuisance_cov_file))
tmp_nuisance = icatb_load_ascii_or_mat(nuisance_cov_file);
tmp_nuisance = icatb_zscore(tmp_nuisance);
num_states_clusters = size(dfnc_corrs);
num_states_clusters = num_states_clusters(end);
for nSess = 1:size(dfnc_corrs, 1)
for nStates = 1:num_states_clusters
tmp_dfnc_corrs = squeeze(dfnc_corrs(nSess, :, :, nStates));
chkNotNan = isnan(sum(tmp_dfnc_corrs, 2))==0;
chkNotNan = prod(chkNotNan, 2)==1;
tmp_dfnc_corrs = regress_cov(tmp_dfnc_corrs(chkNotNan, :), tmp_nuisance(chkNotNan, :));
dfnc_corrs(nSess, chkNotNan, :, nStates) = tmp_dfnc_corrs;
end
end
if (exist('dfncTaskConnectivity', 'var'))
for nSess = 1:size(dfncTaskConnectivity, 1)
for nR = 1:size(dfncTaskConnectivity, 4)
dfncTaskConnectivity(nSess, :, :, nR) = regress_cov(squeeze(dfncTaskConnectivity(nSess, :, :, nR)), tmp_nuisance);
end
end
end
end
%data = figData.data;
%% Covariates
if (strcmpi(designCriteria, 'anova'))
covH = findobj(handles, 'tag', 'cov');
covVal = get(covH, 'value');
if (isempty(covVal))
error('Please select covariates in order to do stats');
end
covInfo = figData.cov(covVal);
covNames = cellstr(char(covInfo.name));
covTypes = cellstr(char(covInfo.type));
chkRegress = find(strcmpi(covTypes, 'continuous') == 1);
if (~isempty(chkRegress))
error('Please select categorical covariates only if you want to do anova');
end
end
%% Model Type
modelType = 'linear';
if (strcmpi(designCriteria, 'anova'))
if (length(covVal) > 1)
chkModel = icatb_questionDialog('title', 'Model Interactions', 'textbody', 'Do You Want To Model Interactions?');
if (chkModel)
modelType = 'interaction';
end
end
anovaParameters = cell(1, length(covVal));
for nR = 1:length(anovaParameters)
anovaParameters{nR} = covInfo(nR).value;
end
end
set(handles, 'pointer', 'watch');
cluster_stats_directory = figData.outputDir;
% if (~exist(cluster_stats_directory, 'dir'))
% mkdir(cluster_stats_directory);
% end
try
groupNames = figData.ttestOpts.t.name;
catch
error('Select datasets for computing t-tests');
end
try
groupVals = figData.ttestOpts.t.val;
catch
error('Select datasets for computing t-tests');
end
disp('....................... Computing stats on dFNC correlations ................................');
disp('');
disp('');
if (strcmpi(designCriteria, 'one sample t-test'))
disp('Selected design criteria is one sample t-test');
disp('');
%% T-test
if (size(dfnc_corrs, 1) > 1)
dfnc_corrs = squeeze(mean(dfnc_corrs, 1));
else
dfnc_corrs = squeeze(dfnc_corrs);
end
if (exist('dfncTaskConnectivity', 'var'))
if (size(dfncTaskConnectivity, 1) > 1)
dfncTaskConnectivity = squeeze(mean(dfncTaskConnectivity, 1));
else
dfncTaskConnectivity = squeeze(dfncTaskConnectivity);
end
end
subInds = [figData.ttestOpts.t.val{1}];
dfnc_corrs = dfnc_corrs(subInds, :, :);
numClusters = size(dfnc_corrs, 3);
modelX = ones(size(dfnc_corrs, 1), 1);
%% Initialize results
t_u = cell(1, numClusters);
p_u = t_u;
stats_u = t_u;
N = zeros(1, numClusters);
mean_u = cell(1, numClusters);
subject_indices = cell(1, numClusters);
%% Compute and save
for nC = 1:numClusters
disp(['Computing one sample t-test on cluster state# ', num2str(nC), ' ...']);
tmp = squeeze(dfnc_corrs(:, :, nC));
chk = find(isfinite(tmp(:, 1)) == 1);
if (length(chk) > 1)
tmp = tmp(chk, :);
N(nC) = size(tmp, 1);
mean_u{nC} = mean(tmp);
subject_indices{nC} = subInds(chk);
[t_u{nC}, p_u{nC}, stats_u{nC}] = mT(tmp, modelX(chk), [], 0, {'verbose'});
end
end
outFile = fullfile(cluster_stats_directory, [figData.prefix, '_one_sample_ttest_results.mat']);
disp(['One sample t-test results are saved in ', outFile]);
fprintf('\n\n');
save(outFile, 't_u', 'p_u', 'stats_u', 'mean_u', 'N', 'groupNames', 'groupVals', 'subject_indices');
if (exist('dfncTaskConnectivity', 'var'))
mean_task_conn = cell(1, length(selectedRegressors));
t_task_conn = mean_task_conn;
p_task_conn = mean_task_conn;
stats_task_conn = mean_task_conn;
for nRegress = 1:length(selectedRegressors)
disp(['Computing one sample t-test on task connectivity (', selectedRegressors{nRegress}, ' ...']);
tmp = squeeze(dfncTaskConnectivity(:, :, nRegress));
mean_task_conn{nRegress} = mean(tmp);
[t_task_conn{nRegress}, p_task_conn{nRegress}, stats_task_conn{nRegress}] = mT(tmp, modelX, [], 0, {'verbose'});
end
save(outFile, 't_task_conn', 'p_task_conn', 'stats_task_conn', 'mean_task_conn', '-append');
end
drawnow;
%% Plot results
elseif (strcmpi(designCriteria, 'two sample t-test'))
disp('Selected design criteria is two sample t-test');
%% Two sample t-test
if (size(dfnc_corrs, 1) > 1)
dfnc_corrs = squeeze(mean(dfnc_corrs, 1));
else
dfnc_corrs = squeeze(dfnc_corrs);
end
if (exist('dfncTaskConnectivity', 'var'))
if (size(dfncTaskConnectivity, 1) > 1)
dfncTaskConnectivity = squeeze(mean(dfncTaskConnectivity, 1));
else
dfncTaskConnectivity = squeeze(dfncTaskConnectivity);
end
end
numClusters = size(dfnc_corrs, 3);
%% Initialize results
t_u = cell(1, numClusters);
p_u = t_u;
stats_u = t_u;
N = zeros(2, numClusters);
mean_u = cell(2, numClusters);
g1 = [figData.ttestOpts.t.val{1}];
g2 = [figData.ttestOpts.t.val{2}];
subject_indices = cell(2, numClusters);
%% Compute and save
for nC = 1:numClusters
disp(['Computing two sample t-test on cluster state# ', num2str(nC), ' ...']);
%tmp = squeeze(dfnc_corrs(:, :, nC));
tmp1 = squeeze(dfnc_corrs(g1, :, nC));
tmp2 = squeeze(dfnc_corrs(g2, :, nC));
chk1 = find(isfinite(tmp1(:, 1)) == 1);
chk2 = find(isfinite(tmp2(:, 1)) == 1);
if (~isempty(chk1) && ~isempty(chk2))
if ((length(chk1) + length(chk2)) > 2)
tmp1 = tmp1(chk1, :);
tmp2 = tmp2(chk2, :);
N1 = length(chk1);
N2 = length(chk2);
modelX = ones(N1 + N2, 1);
modelX(N1 + 1:end) = 0;
N(1, nC) = N1;
N(2, nC) = N2;
subject_indices{1, nC} = g1(chk1);
subject_indices{2, nC} = g2(chk2);
mean_u{1, nC} = mean(tmp1);
mean_u{2, nC} = mean(tmp2);
[t_u{nC}, p_u{nC}, stats_u{nC}] = mT([tmp1;tmp2], modelX, [], 1, {'verbose'});
end
end
end
outFile = fullfile(cluster_stats_directory, [figData.prefix, '_two_sample_ttest_results.mat']);
disp(['Two sample t-test results are saved in ', outFile]);
fprintf('\n\n');
save(outFile, 't_u', 'p_u', 'stats_u', 'mean_u', 'N', 'groupNames', 'groupVals', 'subject_indices');
if (exist('dfncTaskConnectivity', 'var'))
modelX = ones(length(g1) + length(g2), 1);
modelX(length(g1) + 1:end) = 0;
clear t_task_conn p_task_conn stats_task_conn mean_task_conn
mean_task_conn = cell(2, length(selectedRegressors));
t_task_conn = cell(1, length(selectedRegressors));
p_task_conn = cell(1, length(selectedRegressors));
stats_task_conn = cell(1, length(selectedRegressors));
for nRegress = 1:length(selectedRegressors)
disp(['Computing one sample t-test on task connectivity (', selectedRegressors{nRegress}, ' ...']);
tmp1 = squeeze(dfncTaskConnectivity(g1, :, nRegress));
tmp2 = squeeze(dfncTaskConnectivity(g2, :, nRegress));
mean_task_conn{1, nRegress} = mean(tmp1);
mean_task_conn{2, nRegress} = mean(tmp2);
[t_task_conn{nRegress}, p_task_conn{nRegress}, stats_task_conn{nRegress}] = mT([tmp1;tmp2], modelX, [], 1, {'verbose'});
end
save(outFile, 't_task_conn', 'p_task_conn', 'stats_task_conn', 'mean_task_conn', '-append');
end
drawnow;
%% Plot results
elseif (strcmpi(designCriteria, 'paired t-test'))
%% Paired sample t-test
disp('Selected design criteria is paired sample t-test');
% reshape to (sessions x subjects) x component pairs x cluster
% states
dfnc_corrs = reshape(dfnc_corrs, size(dfnc_corrs, 1)*size(dfnc_corrs, 2), size(dfnc_corrs, 3), size(dfnc_corrs, 4));
if (exist('dfncTaskConnectivity', 'var'))
dfncTaskConnectivity = reshape(dfncTaskConnectivity, size(dfncTaskConnectivity, 1)*size(dfncTaskConnectivity, 2), size(dfncTaskConnectivity, 3), ...
size(dfncTaskConnectivity, 4));
end
numClusters = size(dfnc_corrs, 3);
%% Initialize results
t_u = cell(1, numClusters);
p_u = t_u;
stats_u = t_u;
N = zeros(2, numClusters);
mean_u = cell(2, numClusters);
g1 = [figData.ttestOpts.t.val{1}];
g2 = [figData.ttestOpts.t.val{2}];
subject_indices = cell(2, numClusters);
%% Compute and save
for nC = 1:numClusters
disp(['Computing paired sample t-test on cluster state# ', num2str(nC), ' ...']);
%tmp = squeeze(dfnc_corrs(:, :, nC));
tmp1 = squeeze(dfnc_corrs(g1, :, nC));
tmp2 = squeeze(dfnc_corrs(g2, :, nC));
chk = find(isfinite(tmp1(:, 1).*tmp2(:, 1)) == 1);
if (length(chk) > 1)
tmp1 = tmp1(chk, :);
tmp2 = tmp2(chk, :);
N(1, nC) = length(chk);
N(2, nC) = N(1, nC);
modelX = ones(N(1, nC), 1);
mean_u{1, nC} = mean(tmp1);
mean_u{2, nC} = mean(tmp2);
subject_indices{1, nC} = g1(chk);
subject_indices{2, nC} = g2(chk);
[t_u{nC}, p_u{nC}, stats_u{nC}] = mT(tmp1 - tmp2, modelX, [], 0, {'verbose'});
end
end
outFile = fullfile(cluster_stats_directory, [figData.prefix, '_paired_ttest_results.mat']);
disp(['Paired t-test results are saved in ', outFile]);
fprintf('\n\n');
save(outFile, 't_u', 'p_u', 'stats_u', 'mean_u', 'N', 'groupNames', 'groupVals', 'subject_indices');
if (exist('dfncTaskConnectivity', 'var'))
modelX = ones(length(g1), 1);
clear t_task_conn p_task_conn stats_task_conn mean_task_conn
mean_task_conn = cell(2, length(selectedRegressors));
t_task_conn = cell(1, length(selectedRegressors));
p_task_conn = cell(1, length(selectedRegressors));
stats_task_conn = cell(1, length(selectedRegressors));
for nRegress = 1:length(selectedRegressors)
disp(['Computing paired t-test on task connectivity (', selectedRegressors{nRegress}, ' ...']);
tmp1 = squeeze(dfncTaskConnectivity(g1, :, nRegress));
tmp2 = squeeze(dfncTaskConnectivity(g2, :, nRegress));
mean_task_conn{1, nRegress} = mean(tmp1);
mean_task_conn{2, nRegress} = mean(tmp2);
[t_task_conn{nRegress}, p_task_conn{nRegress}, stats_task_conn{nRegress}] = mT(tmp1 - tmp2, modelX, [], 0, {'verbose'});
end
save(outFile, 't_task_conn', 'p_task_conn', 'stats_task_conn', 'mean_task_conn', '-append');
end
drawnow;
% elseif (strcmpi(designCriteria, 'anova'))
% %% Anova
%
%
% anovaR = icatb_anova(tmpDat, anovaP, 'model', modelType, 'var_names', covNames);
% tbl = icatb_anova_table(anovaR);
%
% clear anovaP tmpDat;
%
% icatb_print_table(tbl, fid, 'a+', 0);
% fprintf(fid, '\n');
end
disp('');
disp(['Stats completed. Please see results file ', outFile]);
disp('');
disp('');
diary('off');
set(handles, 'pointer', 'arrow');
catch
icatb_errorDialog(lasterr, 'dFNC Stats');
msg = lasterror;
set(handles, 'pointer', 'arrow');
rethrow(msg);
diary('off');
end
function designCallback(hObject, ed, handles)
%% Design callback
%
%% Design criteria
designCriteriaH = findobj(handles, 'tag', 'design_criteria');
designOptions = get(designCriteriaH, 'string');
designCriteriaVal = get(designCriteriaH, 'value');
designCriteria = designOptions{designCriteriaVal};
covH = findobj(handles, 'tag', 'cov');
addCovH = findobj(handles, 'tag', 'add_cov_button');
removeCovH = findobj(handles, 'tag', 'remove_cov_button');
if (~strcmpi(designCriteria, 'anova'))
set(covH, 'enable', 'off');
set(addCovH, 'enable', 'off');
set(removeCovH, 'enable', 'off');
else
set(covH, 'enable', 'on');
set(addCovH, 'enable', 'on');
set(removeCovH, 'enable', 'on');
return;
end
figData = get(handles, 'userdata');
avgRuns = 1;
if (figData.numOfSess ~= 1)
if (strcmpi(designCriteria, 'paired t-test'))
avgRuns = 0;
end
end
if (~avgRuns)
subjectStr = cell( figData.numOfSub* figData.numOfSess, 1);
count = 0;
for nSub = 1: figData.numOfSub
for nSess = 1: figData.numOfSess
count = count + 1;
subjectStr{count} = ['Subject ', num2str(nSub), ' Session ', num2str(nSess)];
end
end
else
subjectStr = cell(figData.numOfSub, 1);
count = 0;
for nSub = 1:figData.numOfSub
count = count + 1;
subjectStr{count} = ['Subject ', num2str(nSub)];
end
end
ttestOpts.des = designCriteria;
if (strcmpi(designCriteria, 'one sample t-test'))
groupName = '';
groupVal = [];
try
groupName = figData.ttestOpts.t.name{1};
catch
end
try
groupVal = figData.ttestOpts.t.val{1};
catch
end
[groupName, groupVal] = icatb_select_groups_gui(subjectStr, 'Group', 'selGrp', groupName, groupVal);
if (isempty(groupName))
error('Data-sets are not selected');
end
ttestOpts.t.name = {groupName};
ttestOpts.t.val = {groupVal};
elseif (strcmpi(designCriteria, 'two sample t-test'))
group1Name = '';
groupVal1 = [];
try
group1Name = figData.ttestOpts.t.name{1};
catch
end
try
groupVal1 = figData.ttestOpts.t.val{1};
catch
end