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Bioinformatics Advance Access published online on October 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti096
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received May 12, 2004
Revised September 27, 2004
Accepted October 11, 2004

Article

Finding regulatory modules through large-scale gene-expression data analysis

M. Kloster 1, C. Tang 2*, and N. S. Wingreen 3

1 Department of Physics, Princeton University, Princeton, New Jersey 08544; NEC Laboratories America, Inc., 4 Independence Way, Princeton, New Jersey 08540
2 NEC Laboratories America, Inc., 4 Independence Way, Princeton, New Jersey 08540; Center for Theoretical Biology, Peking University, Beijing 100871, China
3 NEC Laboratories America, Inc., 4 Independence Way, Princeton, New Jersey 08540; Department of Molecular Biology, Princeton University, Princeton, New Jersey 8544

* To whom correspondence should be addressed.
C. Tang, E-mail: tang{at}nec-labs.com


  Abstract

Motivation: The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms.

Results: Based on the Iterative Signature Algorithm [Bergmann, S., Ihmels, J. and Barkai, N. (2002) Phys. Rev. E 67, 031902], we present an algorithm--the Progressive Iterative Signature Algorithm (PISA)--that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.

Supporting material: Sections S.1-S.5, figures S1-S10 and table S1 are available at ??.


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