Application of Bayesian Decomposition for analysing microarray data

• Published in: Bioinformatics 2002-04, Vol.18 (4), p.566-575
• Main Authors: Moloshok, T. D., Klevecz, R. R., Grant, J. D., Manion, F. J., Speier, W. F., Ochs, M. F.
• Format: Article
• Language: English
• Subjects: Agronomy. Soil science and plant productions; Algorithms; Bayes Theorem; Biochemistry and biology; Biological and medical sciences; Cell Cycle - genetics; Chemical, physicochemical, biochemical and biological properties; Databases, Genetic; Fundamental and applied biological sciences. Psychology; Gene Expression Regulation; General aspects; Genome, Fungal; Markov Chains; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Microbiology; Models, Genetic; Models, Statistical; Monte Carlo Method; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Periodicity; Physics, chemistry, biochemistry and biology of agricultural and forest soils; Reproducibility of Results; Saccharomyces cerevisiae - genetics; Sensitivity and Specificity; Soil science; Techniques used in virology; Virology
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/18.4.566

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 ∼ 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. Contact: [e-mail]; [e-mail]; jd_ [e-mail]; fj_ [e-mail]; [e-mail]; m_ [e-mail] * To whom correspondence should be addressed.