Bioinformatics/Biostatistics: Microarray Analysis
The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).
Key wordsBioinformatics Biostatistics Clustering Genomics Microarray
- 5.Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53 325–28.Google Scholar
- 6.Pearson, K. (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2 559–72.Google Scholar
- 8.Ryan, M. C., Zeeberg, B. R., Caplen, N. J., Cleland, J. A., Kahn, A. B., et al. (2008) SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies. BMC Bioinformatics 9 313.PubMedCrossRefGoogle Scholar
- 10.Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25 25–9.Google Scholar