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Partitioning biological data with transitivity clustering

Nature Methods volume 7, pages 419–420 (2010)Cite this article

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To the Editor:

Clustering is a common computational technique for data analysis in the life sciences. Essentially one defines clustering as a partitioning of arbitrary data objects into groups, such that the objects in each group, or cluster, have common traits, with respect to a similarity function. Ideally, objects from the same cluster are more similar to each other than to objects from different clusters. A density parameter controls the size and the number of resulting clusters.

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Figure 1: Data analysis and clustering workflow.

References

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Acknowledgements

This work was supported by the German Academic Exchange Service (DAAD), the Boehringer Ingelheim Fonds, US National Institutes of Health grant P41-RR01081, the German Research Foundation (DFG) Graduate School (GK) 635, the German National Genome Research Network (NGFN) and the DFG-funded Cluster of Excellence for Multimodal Computing and Interaction.

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Authors and Affiliations

  1. International Computer Science Institute, University of California at Berkeley, Berkeley, California, USA

    Tobias Wittkop, Dorothea Emig & Jan Baumbach

  2. Center for Biotechnology, Bielefeld University, Bielefeld, Germany

    Tobias Wittkop & Jens Stoye

  3. Buck Institute for Age Research, Novato, California, USA

    Tobias Wittkop

  4. Max Planck Institute for Informatics, Saarbrücken, Germany

    Dorothea Emig, Mario Albrecht & Jan Baumbach

  5. Department of Computer Science, Albert Ludwigs University, Freiburg, Germany

    Sita Lange

  6. Technische Universität Dortmund, Dortmund, Germany

    Sven Rahmann

  7. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA

    John H Morris

  8. Institute for Informatics, Friedrich Schiller Universität Jena, Jena, Germany

    Sebastian Böcker

  9. Faculty of Technology, Bielefeld University, Bielefeld, Germany

    Jens Stoye

  10. Saarland University, Cluster of Excellence for Multimodal Computing and Interaction, Saarbrücken, Germany

    Jan Baumbach

Authors
  1. Tobias Wittkop
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  2. Dorothea Emig
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  3. Sita Lange
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  4. Sven Rahmann
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  5. Mario Albrecht
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  6. John H Morris
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  7. Sebastian Böcker
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  8. Jens Stoye
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  9. Jan Baumbach
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Correspondence to Jan Baumbach.

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Supplementary Figure 1, Supplementary Table 1, Supplementary Methods (PDF 667 kb)

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Wittkop, T., Emig, D., Lange, S. et al. Partitioning biological data with transitivity clustering. Nat Methods 7, 419–420 (2010). https://doi.org/10.1038/nmeth0610-419

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