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Bioinformatics Advance Access first published online on November 18, 2004
This version published online on January 19, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti149
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received August 22, 2004
Revised November 9, 2004
Accepted November 10, 2004

Article

Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression

Yoram Ben-Shaul 1, Hagai Bergman 2, and Hermona Soreq 3*

1 Department of Biological Chemistry, The Life Sciences Institute, The Hebrew University of Jerusalem, Israel 91904; Center for Computational Neuroscience and the Eric Roland Center of Neurodegenerative Diseases, The Hebrew University of Jerusalem, Israel 91904; The Department of Physiology of Hadassah Medical School, The Hebrew University of Jerusalem, Israel 91904
2 Center for Computational Neuroscience and the Eric Roland Center of Neurodegenerative Diseases, The Hebrew University of Jerusalem, Israel 91904; The Department of Physiology of Hadassah Medical School, The Hebrew University of Jerusalem, Israel 91904
3 Department of Biological Chemistry, The Life Sciences Institute, The Hebrew University of Jerusalem, Israel 91904; Center for Computational Neuroscience and the Eric Roland Center of Neurodegenerative Diseases, The Hebrew University of Jerusalem, Israel 91904

* To whom correspondence should be addressed.
Hermona Soreq, E-mail: soreq{at}cc.huji.ac.il


  Abstract

Motivation: Analysis of large scale expression data is greatly facilitated by availability of gene ontologies (GO). Many current methods test whether sets of transcripts annotated with specific ontology terms contain an excess of "changed" transcripts. This approach suffers from two main limitations. First, since gene expression is continuous rather than discrete, designating a gene as changed or unchanged is arbitrary and oblivious to the actual magnitude of the change. Second, by considering only the number of changed genes, finer changes in expression patterns associated with the category may be ignored. Since genes generally participate in multiple networks, widespread and subtle modifications in expression patterns are at least as important as extreme increases/decreases of a few genes.

Results: Numerical simulations confirm that incorporating continuous measures of gene expression for all measured transcripts yields detection of considerably more subtle changes. Applying continuous measures to microarray data from brains of mice injected with the Parkinsonian neurotoxin MPTP enables detection of changes in various biologically relevant gene-ontology terms, many of which are overlooked by discrete approaches.

Availability: Software (MATLAB) is available upon request from the Authors.


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