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Application of Bayesian decomposition for analysing microarray data.

TitleApplication of Bayesian decomposition for analysing microarray data.
Publication TypeJournal Article
Year of Publication2002
AuthorsMoloshok, TD, Klevecz, RR, Grant, JD, Manion, FJ, Speier, WF, Ochs, MF
JournalBioinformatics
Volume18
Issue4
Pagination566-75
Date Published2002 Apr
ISSN1367-4803
KeywordsAlgorithms, Bayes Theorem, Cell Cycle, Databases, Genetic, Gene Expression Regulation, Genome, Fungal, Markov Chains, Models, Genetic, Models, Statistical, Monte Carlo Method, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Periodicity, Reproducibility of Results, Saccharomyces cerevisiae, Sensitivity and Specificity
Abstract

MOTIVATION: Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments.RESULTS: Originally developed for spectroscopic analysis, Bayesian Decomposition (BD) includes two features which make it useful for microarray data analysis: the ability to assign genes to multiple coexpression groups and the ability to encode biological knowledge into the system. Here we demonstrate the ability of the algorithm to provide insight into the yeast cell cycle, including identification of five temporal patterns tied to cell cycle phases as well as the identification of a pattern tied to an approximately 40 min cell cycle oscillator. The genes are simultaneously assigned to the patterns, including partial assignment to multiple patterns when this is required to explain the expression profile.AVAILABILITY: The application is available free to academic users under a material transfer agreement. Go to http://bioinformatics.fccc.edu/ for more details.

Alternate JournalBioinformatics
PubMed ID12016054
Grant ListCA06927 / CA / NCI NIH HHS / United States
People: 
Frank Manion
University of Michigan Comprehensive Cancer Center at North Campus Reserach Complex
1600 Huron Parkway, Bldg 100, Rm 100 
Mailing Address: 2800 Plymouth Rd, NCRC 100-1004
Ann Arbor, MI 48109-2800 
Ph. (734) 764-8848 Fax. (734) 615-0517
Please acknowledge the Cancer Center Support Grant (P30 CA046592) when publishing manuscripts or abstracts that utilized the services of the University of Michigan's Comprehensive Cancer Center's Shared Resource: Cancer Informatics.
Suggested language: "Research reported in this [publication/press release] was supported by the National Cancer Institute of the National Institutes of Health under award number P30CA046592."

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