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Bioinformatics Advance Access published online on April 3, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl121
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 3, 2006
Revised March 24, 2006
Accepted March 25, 2006

Article

Unbiased pattern detection in microarray data series

S. E. Ahnert 1 *, K. Willbrand 2, F. C. S. Brown 3, and T. M. A. Fink 4

1 Theory of Condensed Matter, Cavendish Laboratory, Cambridge CB3 0HE, UK; Institut Curie, CNRS UMR 144, 75248 Paris Cedex 05, France
2 Laboratoire de Physique Statistique, 75231 Paris Cedex 05, France
3 Départment de mathématiques et applications, Ecole Normale Supérieure, 75231 Paris Cedex 05, France
4 Institut Curie, CNRS UMR 144, 75248 Paris Cedex 05, France

* To whom correspondence should be addressed.
S. E. Ahnert, E-mail: sea31{at}cam.ac.uk


  Abstract

Motivation: Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many casereds the aim is to identify pattern in the data series generated by successive microarray measurements.

Results: Here we introduce a new method of detecting pattern in microarray data series which is independent of the nature of this pattern. Our approach provides a measure of the algorithmic compressibility of each data series. A series which is significantly compressible is much more likely to result from simple underlying mechanisms than series which are incompressible. Accordingly, the gene associated with a compressible series is more likely to be biologically significant. We test our method on microarray time series of yeast cell cycle and show that it blindly selects genes exhibiting the expected cyclic behaviour as well as detecting other forms of pattern. Our results successfully predict two independent non-microarray experimental studies.

Associate Editor: John Quackenbush
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