Clustering Genes :: Microarray Analysis (Bioinformatics Toolbox)

Bioinformatics Toolbox

Clustering Genes

Now that you have a manageable list of genes, you can look for relationships between the profiles using some different clustering techniques from the Statistics Toolbox.

For hierarchical clustering, the function pdist calculates the pairwise distances between profiles, and the function linkage creates the hierarchical cluster tree.
```
corrDist = pdist(yeastvalues, 'corr');
clusterTree = linkage(corrDist, 'average');
```
The function cluster calculates the clusters based on either a cutoff distance or a maximum number of clusters. In this case, the 'maxclust' option is used to identify 16 distinct clusters.
```
clusters = cluster(clusterTree, 'maxclust', 16);
```

The profiles of the genes in these clusters can be plotted together using a simple loop and the function subplot.

figure
for c = 1:16
    subplot(4,4,c);
    plot(times,yeastvalues((clusters == c),:)');
    axis tight
end
suptitle('Hierarchical Clustering of Profiles');

MATLAB plots the images.

The Statistics Toolbox also has a K-means clustering function. Again, sixteen clusters are found, but because the algorithm is different these are not necessarily the same clusters as those found by hierarchical clustering.

[cidx, ctrs] = kmeans(yeastvalues, 16, 
                      'dist','corr',
                      'rep',5,
                      'disp','final');
figure
for c = 1:16
    subplot(4,4,c);
    plot(times,yeastvalues((cidx == c),:)');
    axis tight
end
suptitle('K-Means Clustering of Profiles');

MATLAB displays

13 iterations, total sum of distances = 11.4042
14 iterations, total sum of distances = 8.62674
26 iterations, total sum of distances = 8.86066
22 iterations, total sum of distances = 9.77676
26 iterations, total sum of distances = 9.01035

Instead of plotting all of the profiles, you can plot just the centroids.

figure
for c = 1:16
    subplot(4,4,c);
    plot(times,ctrs(c,:)');
    axis tight
    axis off    % turn off the axis
end
suptitle('K-Means Clustering of Profiles');

MATLAB plots the figure.

You can use the function clustergram to create a heat map and dendrogram from the output of the hierarchical clustering.
```
figure
clustergram(yeastvalues(:,2:end),'RowLabels',genes,...
                                 'ColumnLabels',times(2:end))
```
MATLAB plots the figure.

Filtering Genes

Principal Component Analysis