Motivation: We describe a new approach to the analysis of gene expression d
ata coming from DNA array experiments, using an unsupervised neural network
. DNA array technologies allow monitoring thousands of genes rapidly and ef
ficiently. One of the interests of these studies is the search for correlat
ed gene expression patterns, and this is usually achieved by clustering the
m. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1
997) J. Mel. Evol., 44, 226-233), is a neural network that grows adopting t
he topology of a binary tree. The result of the algorithm is a hierarchical
cluster obtained with the accuracy and robustness of a neural network.
Results: SOTA clustering confers several advantages over classical hierarch
ical clustering methods. SOTA is a divisive method: the clustering process
is performed from top to bottom, i.e. the highest hierarchical levels are r
esolved before going to the details of the lowest levels. The growing can b
e stopped at the desired hierarchical level. Moreover, a criterion to stop
the growing of the tree, based on the approximate distribution of probabili
ty obtained by randomisation of the original data set, is provided. By mean
s of this criterion, a statistical support for the definition of clusters i
s proposed. In addition, obtaining average gene expression patterns is a bu
ilt-in feature of the algorithm. Different neurons defining the different h
ierarchical levels represent the averages of the gene expression patterns c
ontained in the clusters.
Since SOTA runtimes are approximately linear with the number of items to be
classified, it is especially suitable for dealing with huge amounts of dat
a. The method proposed is very general and applies to any data providing th
at they can be coded as a series of numbers and that a computable measure o
f similarity between data items can be used.