Simultaneously segmenting multiple gene expression time courses by analyzing cluster dynamics

Satish Tadepalli, Naren Ramakrishnan, Layne T. Watson, Bhubaneshwar Mishra, Richard F. Helm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a new approach to segmenting multiple time series by analyzing the dynamics of cluster rearrangement around putative segment boundaries. By directly minimizing information-theoretic measures of segmentation quality derived from Kullback-Leibler (KL) divergences, our formulation reveals clusters of genes along with a segmentation such that clusters show concerted behavior within segments but exhibit significant re- grouping across segmentation boundaries. This approach finds application in distilling large numbers of gene expression profiles into temporal relationships underlying biolog- ical processes. The results of the segmentation algorithm can be summarized as Gantt charts revealing temporal dependencies in the ordering of key biological processes. Ap- plications to the yeast metabolic cycle and the yeast cell cycle are described.

Original languageEnglish (US)
Title of host publicationProceedings of 6th Asia-Pacific Bioinformatics Conference, APBC 2008
Pages297-306
Number of pages10
StatePublished - 2008
Event6th Asia-Pacific Bioinformatics Conference, APBC 2008 - Kyoto, Japan
Duration: Jan 14 2008Jan 17 2008

Publication series

NameSeries on Advances in Bioinformatics and Computational Biology
Volume6
ISSN (Print)1751-6404

Other

Other6th Asia-Pacific Bioinformatics Conference, APBC 2008
CountryJapan
CityKyoto
Period1/14/081/17/08

Keywords

  • Clustering
  • KL-divergence
  • Temporal regulation
  • Time series segmentation

ASJC Scopus subject areas

  • Bioengineering
  • Information Systems

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  • Cite this

    Tadepalli, S., Ramakrishnan, N., Watson, L. T., Mishra, B., & Helm, R. F. (2008). Simultaneously segmenting multiple gene expression time courses by analyzing cluster dynamics. In Proceedings of 6th Asia-Pacific Bioinformatics Conference, APBC 2008 (pp. 297-306). (Series on Advances in Bioinformatics and Computational Biology; Vol. 6).