Computational analysis of microarray data

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Lockhart, D. J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol. 14, 1675–1680 (1996).

Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 270, 467–470 (1995).

Schena, M. et al. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl Acad. Sci. USA 93, 10614–10619 (1996).

Wen, X. et al. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl Acad. Sci. USA 95, 334–339 (1998).This is one of the first analyses of large-scale gene expression — in this case, RT–PCR data — using clustering and data-mining techniques. It elegantly shows how integrating the results derived using various distance metrics can reveal different but meaningful patterns in the data.

Michaels, G. S. et al. Cluster analysis and data visualization of large-scale gene expression data. Pacific Symp. Biocomput. 1998, 42–53 (1998).

Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).This is an excellent demonstration of the power of hierarchical clustering to the analysis of microarray data. The authors also provide software — Cluster and Treeview — which became the standard for analysing microarray data.

Weinstein, J. N. et al. An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349 (1997).Weinstein and colleagues present one of the first and most elegant applications of hierarchical clustering and other data-mining and visualization techniques to the analysis of large-scale data in molecular biology.

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Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912 (1999).Tamayo and colleagues use self-organizing maps (SOMs) to explore patterns of gene expression generated using Affymetrix arrays, and provide the GENECLUSTER implementation of SOMs.

Eisen, M. B. & Brown, P. O. DNA arrays for analysis of gene expression. Meth. Enzymol. 303, 179–205 (1999).

Hegde, P. et al. A concise guide to microarray analysis. Biotechniques 29, 548–560 (2000).

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Ermolaeva, O. et al. Data management and analysis for gene expression arrays. Nature Genet. 20, 19–23 (1998).

Sherlock, G. et al. The Stanford Microarray Database. Nucleic Acids Res. 29, 152–155 (2001).

Chen, Y., Dougherty, E. R. & Bittner, M. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Opt. 2, 364–374 (1997).

Heyer, L. J., Kruglyak, L. & Yooseph, S. Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9, 1106–1115 (1999).

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Raychaudhuri, S., Stuart, J. M. & Altman, R. B. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 2000, 455–466 (2000).

Brown, M. P. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA 97, 262–267 (2000).This paper shows the power of supervised techniques, in this case support vector machines, to provide additional insight into gene expression and function.

Golub, T. R. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

Furey, T. S. et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000).

Hedenfalk, I. et al. Gene-expression profiles in hereditary breast cancer. N. Engl. J. Med. 344, 539–548 (2001).

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