Clustering is often one of the first steps in gene expression analysis. How do clustering algorithms work, which ones should we use and what can we expect from them?
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References
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. & Church, G.M. Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999).
Jain, A.K. & Dubes, R.C. Algorithms for Clustering Data. (Prentice Hall, Englewood Cliffs, New Jersey, 1988).
Aldenderfer, M.S. & Blashfield, R.K. Cluster Analysis. (Sage Publication, Newbury Park, California, 1984).
Jiang, D., Tang, C. & Zhang, A. Cluster analysis for gene expression data: a survey. IEEE Trans. Know. Data Eng. 16, 1370–1386 (2004).
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).
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).
Handl, J., Knowles, J. & Kell, D.B. Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201–3212 (2005).
Gibbons, F.D. & Roth, F.P. Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res. 12, 1574–1581 (2002).
Costa, I.G., de Carvalho, F.A. & de Souto, M.C. Comparative analysis of clustering methods for gene expression time course data. Genet. Mol. Biol. 27, 623–631 (2004).
Datta, S. & Datta, S. Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19, 459–466 (2003).
Gat-Viks, I., Sharan, R. & Shamir, R. Scoring clustering solutions by their biological relevance. Bioinformatics 19, 2381–2389 (2003).
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D'haeseleer, P. How does gene expression clustering work?. Nat Biotechnol 23, 1499–1501 (2005). https://doi.org/10.1038/nbt1205-1499
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DOI: https://doi.org/10.1038/nbt1205-1499
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