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Practical Approaches to Analyzing Results of Microarray Experiments

Departments of Functional Genomics and Respiratory Medicine, Sheba Medical Center, Tel-Hashomer; and School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel

Address correspondence to: Naftali Kaminski, M.D., Functional Genomics, Sheba Medical Center, Tel Hashomer, 52621 Israel. E-mail: kamins{at}sheba.health.gov.il

Microarray technology is rapidly becoming a standard laboratory technique. The main challenges related to the successful implementation of the technology are analysis-related. In this article we provide a practically oriented review focusing on methods for analysis of large-scale gene expression data in the research laboratory. We describe the various common clustering methods and outline our approach to using them. We discuss methods for scoring genes for their relevance, focusing on the statistical meaning of microarray results, especially with regard to the problem of multiple testing. We also deal with the problem of adding biologic meaning to the results of microarray experiments and describe advanced tools that represent different but valid directions in providing automated solutions to this problem. The tools and approaches described and discussed here should provide the reader with a preliminary understanding of the analysis of the results of microarray experiments. The practical focus of this review should remove the mystery behind the analysis of microarray experiments, thus leading to more productive and efficient use of the technology.

Abbreviations: false discovery rate, FDR • graph-theoretic clustering, CLICK • probabilistic relational models, PRM • self-organizing maps, SOMs • significance analysis of microarrays, SAM • threshold number of misclassifications, TNOM

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