filename_exp='expressioninfo.txt'; %input expression profile
filename_net='networkinfo.txt'; %input network profile
filename_sample='sample_group.txt'; %input sample group information
filename_gene='gene_weight.txt'; %input gene weight information
%read expression and network information
expression=importdata(filename_exp);
X=expression.data;
[ng,ns]=size(X);
sample=expression.textdata(1,2:(ns+1));
gene=expression.textdata(2:(ng+1),1);
clear expression;
network=importdata(filename_net);
N=sparse(network);
clear network;
cluster=importdata(filename_sample);
C=cluster.data;
cu=unique(C);
cn=length(cu);
weight=importdata(filename_gene);
W=weight.data;

n=find(ismember(gene,'ABCC8'));
x=find(N(n,:)==1);
y=[];
z=[];
for i=1:length(x)
    y=[y;find(N(:,x(i))==1)];
end

z=find(N(:,n)==1);
u=unique([x,y',z']);

Nn=N(u,u);
Xn=X(u,:);
Wn=W(u);
Wn=Wn/sum(Wn);
En=zeros(length(u),cn);
for i=1:cn
    index=find(C==cu(i));
    for j=1:length(u)
        En(j,i)=median(Xn(j,index'));
    end
end

i=1;
ID=gene(u);
bg=biograph(Nn,ID,'ArrowSize',10,'LayoutType','equilibrium','NodeAutoSize','off');
set(bg.nodes,'Shape','circle','TextColor',[0,0,0.75],'FontSize',16,'LineColor',[0,0,0]);
set(bg.edges,'LineWidth',1.5);
for j=1:length(u)
    set(bg.nodes(j),'Size',[30*Wn(j)^(1/5),30*Wn(j)^(1/5)]);
end
scale=1/(1+exp(-2))-0.5;
for j=1:length(u)
    if En(j,i)>=0
        if En(j,i)<2
            set(bg.nodes(j),'Color',[1,1-(1/(1+exp(-En(j,i)))-0.5)/scale,1-(1/(1+exp(-En(j,i)))-0.5)/scale]);
        else
            set(bg.nodes(j),'Color',[1,0,0]);
        end
    else
        if En(j,i)>-2
            set(bg.nodes(j),'Color',[1-(1/(1+exp(En(j,i)))-0.5)/scale,1,1-(1/(1+exp(En(j,i)))-0.5)/scale]);
        else
            set(bg.nodes(j),'Color',[0,1,0]);
        end
    end
end
view(bg)