  Motivation: ``Molecular signatures'' or ``gene-expression signatures'' are
used to predict patients' characteristics using data from coexpressed genes.
Signatures can enhance understanding about biological mechanisms and have
diagnostic use. However, available methods to search for signatures fail to
address key requirements of signatures, especially the discovery of sets of
tightly coexpressed genes. Results: After suggesting an operational definition
of signature, we develop a method that fulfills these requirements, returning
sets of tightly coexpressed genes with good predictive performance. This method
can also identify when the data are inconsistent with the hypothesis of a few,
stable, easily interpretable sets of coexpressed genes. Identification of
molecular signatures in some widely used data sets is questionable under this
simple model, which emphasizes the needed for further work on the
operationalization of the biological model and the assessment of the stability
of putative signatures. Availability: The code (R with C++) is available from
http://www.ligarto.org/rdiaz/Software/Software.html under the GNU GPL.
