A Bayesian method for analysing spotted microarray data
NIH NRSA postdoctoral fellowship at Brown University in February 2005. His research interests encompass a wide range of topics in evolutionary genetics, including the role of interactions between the two sexes (intersexual conflict), between loci (epistasis) and between different genomes (nuclear and mitochondrial) in driving and constraining evolution. He is currently using the model system Drosophila and spotted microarray technologies to study these interactions.
Assistant Professor in Molecular and Cell Biology at the University of Connecticut, and is a member of the Program in Genetics. He has performed experimental work to characterise genomic variation in gene expression at a population level and in relation to phenotypic variation, and developed mathematical and statistical models for the analysis of genome-wide data sets. He has constructed theoretical tools for understanding microbial population genetics and molecular evolution in a way that accommodates population variation in gene expression, gene regulation and horizontal gene transfer.
Jeffrey P. Townsend, Department of Molecular and Cellular Biology, University of Connecticut, 354 Mansfield Road, Storrs, CT 06269, USA Tel: +1 (860) 486 189 Fax: +1 (860) 486 4331 E-mail: Jeffrey.Townsend{at}UConn.edu
In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.
Keywords: cDNA microarray, gene expression, Bayesian analysis, Markov chain, Monte Carlo, statistical power, experimental design
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