
%
% Load the data
%

% expressions of the genes, a 1475x20 matrix
exp = load('NODE_DATA.dat');

% links, a matrix of 7123x2, where each row tells one interaction
links = load('LINKS.dat');

% convert links to a binary matrix of 1475x1475
linkmat = zeros(size(exp,1));
for i=1:size(links,1);
  linkmat(links(i,1),links(i,2)) = 1;
end;
% we might want to consider symmetric links instead
linkmat_sym = double(or(linkmat,linkmat'));

% Binary matrix of gene memberships in GO categories
classes = load('GOclasses.txt');


%
% Part 1:
%


%
% Cluster the expressions using K-means
% - try at least two distance measures and different numbers of klusters
% - remember to validate the results
% - useful commands: kmeans, imagesc, plot, compareToGO
%





%
% Part 2:
%

%
% Hierarchical clustering
% - useful commands: pdist, linkage, cluster, dendrogram
% - remeber again to validate the results
% - you may also compare the results to part 1
%


%
% Try adding the link information in some way
% - useful commands: squareform, smoothLinks
% - see how the results change from the previous clustering
%


