We have developed a platform independent Java package of
tools to simultaneously visualize and analyze a whole set
of gene expression experiments. After reading the data
from flat files several graphical representations of
hybridizations can be generated, showing a matrix of
experiments and genes, where multiple experiments and
genes can be easily compared with each other. Fluorescence
ratios can be normalized in several ways to gain a best
possible representation of the data for further
statistical analysis. We have implemented hierarchical and
non hierarchical algorithms to identify similar expressed
genes and expression patterns, including: 1) hierarchical
clustering, 2) k-means, 3) self organizing maps, 4)
principal component analysis, and 5) support vector
machines. More than 10 different kinds of similarity
distance measurements have been implemented, ranging from
simple Pearson correlation to more sophisticated
approaches like mutual information. Moreover, it is
possible to map gene expression data onto chromosomal
sequences. The flexibility, variety of analysis tools and
data visualizations, as well as the free availability to
the research community makes this software suite a
valuable tool in future functional genomic studies.

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