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<!DOCTYPE html
PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<!--
This HTML was auto-generated from MATLAB code.
To make changes, update the MATLAB code and republish this document.
--><title>demo</title><meta name="generator" content="MATLAB 9.8"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2021-02-17"><meta name="DC.source" content="demo.m"><style type="text/css">
html,body,div,span,applet,object,iframe,h1,h2,h3,h4,h5,h6,p,blockquote,pre,a,abbr,acronym,address,big,cite,code,del,dfn,em,font,img,ins,kbd,q,s,samp,small,strike,strong,sub,sup,tt,var,b,u,i,center,dl,dt,dd,ol,ul,li,fieldset,form,label,legend,table,caption,tbody,tfoot,thead,tr,th,td{margin:0;padding:0;border:0;outline:0;font-size:100%;vertical-align:baseline;background:transparent}body{line-height:1}ol,ul{list-style:none}blockquote,q{quotes:none}blockquote:before,blockquote:after,q:before,q:after{content:'';content:none}:focus{outine:0}ins{text-decoration:none}del{text-decoration:line-through}table{border-collapse:collapse;border-spacing:0}
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html body { height:100%; margin:0px; font-family:Arial, Helvetica, sans-serif; font-size:10px; color:#000; line-height:140%; background:#fff none; overflow-y:scroll; }
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</style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#2">Simulate network data</a></li><li><a href="#3">Shuffle nodes as the input matrix</a></li><li><a href="#4">Edge-wise inference</a></li><li><a href="#5">Detect covariate-related subnetworks by SICERS</a></li><li><a href="#6">Subnetwork-wise tests</a></li></ul></div><pre class="codeinput">clear
warning(<span class="string">'off'</span>,<span class="string">'all'</span>)
</pre><h2 id="2">Simulate network data</h2><pre class="codeinput">rng(1);
n1 = 30;n2 = 30;N=100;m1=15;m2=10;m3=5;
mu1 = 1;mu2 = 0;sigma = 1;
thresh = 0.05;
G = zeros(N);
G(1:m1,1:m1)=ones(m1);
G((m1+1):(m1+m2),(m1+1):(m1+m2))=ones(m2);
G((m1+m2+1):(m1+m2+m3),(m1+m2+1):(m1+m2+m3))=ones(m3);
<span class="keyword">for</span> i=1:N
G(i,i)=0;
<span class="keyword">end</span>
G_vec = squareform(G);
A = zeros(size(G_vec,2),n1);
B = zeros(size(G_vec,2),n2);
p_vec = zeros(size(G_vec));
<span class="keyword">for</span> i=1:n1
A(:,i)=normrnd(mu1*G_vec+mu2,sigma,size(G_vec));
<span class="keyword">end</span>
<span class="keyword">for</span> i=1:n2
B(:,i)=normrnd(mu2,sigma,size(G_vec));
<span class="keyword">end</span>
</pre><h2 id="3">Shuffle nodes as the input matrix</h2><pre class="codeinput">perm_matrix = squareform(1:(N*(N-1)/2));
node_perm_idx = randperm(N);
perm_matrix = perm_matrix(node_perm_idx,node_perm_idx);
perm_vec = squareform(perm_matrix);
A_input = A(perm_vec,:);
B_input = B(perm_vec,:);
</pre><h2 id="4">Edge-wise inference</h2><pre class="codeinput"><span class="keyword">for</span> i=1:size(G_vec,2)
[h,p_vec(i)]=ttest2(A_input(i,:),B_input(i,:));
<span class="keyword">end</span>
P=squareform(p_vec);
<span class="keyword">for</span> i=1:N
P(i,i)=1;
<span class="keyword">end</span>
nlogp=-log(P);
figure;imagesc(nlogp);colormap <span class="string">jet</span>;colorbar;snapnow;
title(<span class="string">'Input matrix'</span>)
</pre><img vspace="5" hspace="5" src="demo_01.png" alt=""> <img vspace="5" hspace="5" src="demo_02.png" alt=""> <h2 id="5">Detect covariate-related subnetworks by SICERS</h2><pre class="codeinput">[Cindx,CID,Clist]=SICERS(squareform(nlogp),thresh,0,10);
figure; imagesc(nlogp(Clist,Clist));colormap <span class="string">jet</span>;colorbar;
title(<span class="string">'Detected covariate-related subnetworks'</span>)
</pre><img vspace="5" hspace="5" src="demo_03.png" alt=""> <h2 id="6">Subnetwork-wise tests</h2><pre class="codeinput">[signodeGEP,GEPstat,P_val,Tmax_5prct,max_T0] = PermTest_pval(A_input,B_input,Cindx,CID,thresh,100);
P_val
</pre><pre class="codeoutput">
P_val =
0 0 0.1100
</pre><p class="footer"><br><a href="https://www.mathworks.com/products/matlab/">Published with MATLAB® R2020a</a><br></p></div><!--
##### SOURCE BEGIN #####
clear
warning('off','all')
%% Simulate network data
rng(1);
n1 = 30;n2 = 30;N=100;m1=15;m2=10;m3=5;
mu1 = 1;mu2 = 0;sigma = 1;
thresh = 0.05;
G = zeros(N);
G(1:m1,1:m1)=ones(m1);
G((m1+1):(m1+m2),(m1+1):(m1+m2))=ones(m2);
G((m1+m2+1):(m1+m2+m3),(m1+m2+1):(m1+m2+m3))=ones(m3);
for i=1:N
G(i,i)=0;
end
G_vec = squareform(G);
A = zeros(size(G_vec,2),n1);
B = zeros(size(G_vec,2),n2);
p_vec = zeros(size(G_vec));
for i=1:n1
A(:,i)=normrnd(mu1*G_vec+mu2,sigma,size(G_vec));
end
for i=1:n2
B(:,i)=normrnd(mu2,sigma,size(G_vec));
end
%% Shuffle nodes as the input matrix
perm_matrix = squareform(1:(N*(N-1)/2));
node_perm_idx = randperm(N);
perm_matrix = perm_matrix(node_perm_idx,node_perm_idx);
perm_vec = squareform(perm_matrix);
A_input = A(perm_vec,:);
B_input = B(perm_vec,:);
%% Edge-wise inference
for i=1:size(G_vec,2)
[h,p_vec(i)]=ttest2(A_input(i,:),B_input(i,:));
end
P=squareform(p_vec);
for i=1:N
P(i,i)=1;
end
nlogp=-log(P);
figure;imagesc(nlogp);colormap jet;colorbar;snapnow;
title('Input matrix')
%% Detect covariate-related subnetworks by SICERS
[Cindx,CID,Clist]=SICERS(squareform(nlogp),thresh,0,10);
figure; imagesc(nlogp(Clist,Clist));colormap jet;colorbar;
title('Detected covariate-related subnetworks')
%% Subnetwork-wise tests
[signodeGEP,GEPstat,P_val,Tmax_5prct,max_T0] = PermTest_pval(A_input,B_input,Cindx,CID,thresh,100);
P_val
##### SOURCE END #####
--></body></html>