-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathanalyze_abide_siteeffects.m
215 lines (169 loc) · 6.68 KB
/
analyze_abide_siteeffects.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
function results = analyze_abide_siteeffects(varargin)
% Compute and return conditional correlations, nuisance correlations, and NSR on all subjects x sites.
% INPUT
% (optional)
% - nuisance_type: 'within_subject' or 'within_site' nuisance
%
datadir = 'data';
studydata = load(fullfile(datadir,'ABIDE_controlData_7Sites.mat'));
studydata.yeo_order = load(fullfile(datadir,'cc200Map2Communities'));
[~,reorder_communities] = sort(studydata.yeo_order.mapToCommunities,'ascend');
% Save filename
exportfun = @(fname)(print('-dpng','-r300',fname));
fname = ['tmp' filesep datestr(now,'dd-mmm-yyyy-HHMM')];
if(~exist(fname,'dir'))
mkdir(fname)
end
results = {};
results.observed = [];
results.tw_observed = [];
results.denoised = [];
results.nuisance = [];
results.nsr = [];
sitelabels = [];
denoised = [];
nuisance = [];
observed = [];
tw_observed = [];
nsr = [];
% Site Group Options
sitegroup = 3;
switch sitegroup
case 1
sites = [3 5];
case 2
sites= [1 2 4 6 7];
case 3
sites= [1:7];
end
nsites = length(sites);
if(nargin==0)
nuisance_type = 'within_subject';
else
nuisance_type = varargin{1};
end
for studyno=3;
sitelabels = cat(2,sitelabels, ...
ones(1,length(studydata.data{studyno}.subIDs))*studyno);
if(strcmp(nuisance_type,'within_site'))
Y = squeeze(mean( ...
studydata.data{studyno}.signals(:,reorder_communities,:),2))';
end
for ii=1:length(studydata.data{studyno}.subIDs)
if(mod(ii,5)==0)
disp(sprintf('Site %s, Processing Subject %d ...', ...
studydata.data{studyno}.dataName,ii));
end
X = squeeze(studydata.data{studyno}.signals(ii,reorder_communities,:))';
if(strcmp(nuisance_type,'within_subject'))
Y = mean(X,2);
end
%Check subject level conditional correlation
if(mod(ii,10))
demo_conditional_correlation(X,Y);
end
try
% Standard observed correlation
results(studyno).observed(ii,:,:) = standard_correlation(X);
observed = cat(1,observed,results(studyno).observed(ii,:,:));
results(studyno).tw_observed(ii,:,:) = standard_correlation_sn(X);
tw_observed = cat(1,observed,results(studyno).tw_observed(ii,:,:));
% Denoised Correlation
output = conditional_correlation(X,Y);
results(studyno).denoised(ii,:,:) = output.corr;
denoised = cat(1,denoised,results(studyno).denoised(ii,:,:));
% Nuisance Correlation
results(studyno).nuisance(ii,:,:) = output.nuisance;
results(studyno).nsr(ii) = output.NSR;
nuisance = cat(1,nuisance,results(studyno).nuisance(ii,:,:));
nsr = cat(1,nsr,output.NSR);
catch me
disp(me)
disp(me.stack)
end
end
end
if(exist('brewermap'))
colormapfun = @()(flipud(brewermap(length(colormap),'RdYlBu')));
close all;
else
colormapfun = @winter;
end
figure(1);
set(gcf,'Position',[10 450 1350 375],'PaperPosition', [.5 1.5 12.0 7.0]);
subplot(1,3,1);
site_effect{1} = detect_site_effect(observed,sitelabels);
imagesc(real(site_effect{1}.similarity));
colormap(colormapfun()); colorbar; axis image;
title('Similarity Matrix (Observed)','fontsize',24);
xlabel(sprintf('(wit,bet,rat,test) = (%.2f, %.2f, %.2f)', ...
site_effect{1}.within, ...
site_effect{1}.between, ...
site_effect{1}.ratio));
set(gca,'fontsize',16);
subplot(1,3,2);
site_effect{2} = detect_site_effect(nuisance,sitelabels);
imagesc(real(site_effect{2}.similarity));
colormap(colormapfun()); colorbar; axis image;
title('Similarity Matrix (Nuisance)','fontsize',24);
xlabel(sprintf('(wit,bet,rat,test) = (%.2f, %.2f, %.2f)', ...
site_effect{2}.within, ...
site_effect{2}.between, ...
site_effect{2}.ratio));
set(gca,'fontsize',16);
subplot(1,3,3);
site_effect{3} = detect_site_effect(denoised,sitelabels);
imagesc(site_effect{3}.similarity);
colormap(colormapfun()); colorbar; axis image;
title('Similarity Matrix (Denoised)','fontsize',24);
xlabel(sprintf('(wit,bet,rat,test) = (%.2f, %.2f, %.2f)', ...
site_effect{3}.within, ...
site_effect{3}.between, ...
site_effect{3}.ratio));
set(gca,'fontsize',16);
exportfun(fullfile(fname,[mfilename '1']));
end
function output = conditional_correlation(X,Y)
% Only uses usual column standardize (i.e. correlation)
output = struct();
[Sigma results] = covariance.conditional_sample_covariance_separate(X, ...
struct('verbose',false,...
'nuisance',Y) ...
);
output.corr = results.SigmaCov;
output.nuisance = results.nCov;
output.corr2 = covariance.mle_sample_covariance(results.X_perpY, ...
struct('standardize', false));
output.NSR = results.NSR;
end
function Sigma = standard_correlation(X)
% Usual standard correlation matrix
[Sigma results] = covariance.mle_sample_covariance(X, ...
struct('standardize',false));
end
function Sigma = standard_correlation_sn(X)
% Automatically applies row-first successive norm
%standardize.successive_normalize(X');
[Sigma results] = covariance.mle_sample_covariance(X, ...
struct('standardize',true));
end
function site_effect = detect_site_effect(X,sitelabels)
corrfun = @corr;
upper_idx = find(reshape(triu(ones(size(X,2), size(X,3)),1), [1 size(X,2)^2]));
X = reshape(X,[size(X,1) size(X,2)*size(X,3)]);
tmpSimilarity = corrfun(X(:,upper_idx)');
[hom sep mw] = compareWithinAndBetweenGroupsSim(tmpSimilarity,sitelabels);
site_effect.similarity = tmpSimilarity;
site_effect.within = hom;
site_effect.between = sep;
site_effect.stat = mw;
site_effect.ratio = (hom-sep)/(hom+sep);
end
function [nuisance] = get_shared_nuisance(Y);
% Y is time-series x subjects
Yz = zscore(Y')';
[U S V] = svd(Y);
nfactors = min(size(Y,2),3);
nuisance = U(1:nfactors,:);
nuisance = reshape(nuisance,[size(Y,1) nfactors]);
end