Abstract
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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fan, J. B., Chee, M. S., and Gunderson, K. L. (2006) Highly parallel genomic assays. Nat Rev Genet 7 632–44.
Cho, E. K., Tchinda, J., Freeman, J. L., Chung, Y. J., Cai, W. W., et al. (2006) Array-based comparative genomic hybridization and copy number variation in cancer research. Cytogenet Genome Res 115 262–72.
Yuan, D. S., and Irizarry, R. A. (2006) High-resolution spatial normalization for microarrays containing embedded technical replicates. Bioinformatics 22 3054–60.
Weinstein, J. N., Myers, T. G., O’Connor, P. M., Friend, S. H., Fornace, A. J., Jr., et al. (1997) An information-intensive approach to the molecular pharmacology of cancer. Science 275 343–9.
D’Haeseleer, P. (2005) How does gene expression clustering work? Nat Biotechnol 23 1499–501.
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53 325–28.
Pearson, K. (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2 559–72.
Eichler, G. S., Huang, S., and Ingber, D. E. (2003) Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles. Bioinformatics 19 2321–2.
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.
Kanehisa, M. (1997) A database for post-genome analysis. Trends Genet 13 375–6.
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.
Zeeberg, B. R., Feng, W., Wang, G., Wang, M. D., Fojo, A. T., et al. (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4 R28.
Fisher, R. (1922) On the interpretation of X2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society 85 87–94.
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102 15545–50.
Eichler, G. S., Reimers, M., Kane, D., and Weinstein, J. N. (2007) The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data. Genome Biol 8 R187.
de Hoon, M. J., Imoto, S., Nolan, J., and Miyano, S. (2004) Open source clustering software. Bioinformatics 20 1453–4.
Ball, C. A., Awad, I. A., Demeter, J., Gollub, J., Hebert, J. M., et al. (2005) The Stanford Microarray Database accommodates additional microarray platforms and data formats. Nucleic Acids Res 33 D580-2.
Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95 14863–8.
Magni, P., Ferrazzi, F., Sacchi, L., and Bellazzi, R. (2008) TimeClust: a clustering tool for gene expression time series. Bioinformatics 24 430–2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Eichler, G.S. (2012). Bioinformatics/Biostatistics: Microarray Analysis. In: Espina, V., Liotta, L. (eds) Molecular Profiling. Methods in Molecular Biology, vol 823. Humana Press. https://doi.org/10.1007/978-1-60327-216-2_22
Download citation
DOI: https://doi.org/10.1007/978-1-60327-216-2_22
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-60327-215-5
Online ISBN: 978-1-60327-216-2
eBook Packages: Springer Protocols