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How does gene expression clustering work?

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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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Figure 1: A simple clustering example with 40 genes measured under two different conditions.

Bob Crimi

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References

  1. 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).

    Article  CAS  Google Scholar 

  2. Jain, A.K. & Dubes, R.C. Algorithms for Clustering Data. (Prentice Hall, Englewood Cliffs, New Jersey, 1988).

    Google Scholar 

  3. Aldenderfer, M.S. & Blashfield, R.K. Cluster Analysis. (Sage Publication, Newbury Park, California, 1984).

  4. Jiang, D., Tang, C. & Zhang, A. Cluster analysis for gene expression data: a survey. IEEE Trans. Know. Data Eng. 16, 1370–1386 (2004).

    Article  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. 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).

  7. Handl, J., Knowles, J. & Kell, D.B. Computational cluster validation in post-genomic data analysis. Bioinformatics 21, 3201–3212 (2005).

    Article  CAS  Google Scholar 

  8. Gibbons, F.D. & Roth, F.P. Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res. 12, 1574–1581 (2002).

    Article  CAS  Google Scholar 

  9. 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).

    Article  CAS  Google Scholar 

  10. Datta, S. & Datta, S. Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19, 459–466 (2003).

    Article  CAS  Google Scholar 

  11. Gat-Viks, I., Sharan, R. & Shamir, R. Scoring clustering solutions by their biological relevance. Bioinformatics 19, 2381–2389 (2003).

    Article  CAS  Google Scholar 

Download references

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Authors and Affiliations

  1. Microbial Systems Division, Biosciences Directorate, Lawrence Livermore National Laboratory, PO Box 808, L-448, Livermore, 94551, California, USA

    Patrik D'haeseleer

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  1. Patrik D'haeseleer
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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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