<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>Gene expression datasets are large and complex, having many variables and unknown
internal structure. We apply independent component analysis (ICA) to derive a
less redundant representation of the expression data. The decomposition produces
components with minimal statistical dependence and reveals biologically relevant
information. Consequently, to the transformed data, we apply cluster analysis (an
important and popular analysis tool for obtaining an initial understanding of the
data, usually employed for class discovery). The proposed self-organizing map
(SOM)-based clustering algorithm automatically determines the number of ‘natural’
subgroups of the data, being aided at this task by the available prior knowledge of the
functional categories of genes. An entropy criterion allows each gene to be assigned
to multiple classes, which is closer to the biological representation. These features,
however, are not achieved at the cost of the simplicity of the algorithm, since the
map grows on a simple grid structure and the learning algorithm remains equal to
Kohonen’s one.</p>
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Comp Funct Genomics. 2004 Dec; 5(8):596-616