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Biclustering of Time Series Microarray Data

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Next Generation Microarray Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 802))

Abstract

Clustering is a popular data exploration technique widely used in microarray data analysis. In this chapter, we review ideas and algorithms of bicluster and its applications in time series microarray analysis. We introduce first the concept and importance of biclustering and its different variations. We then focus our discussion on the popular iterative signature algorithm (ISA) for searching biclusters in microarray dataset. Next, we discuss in detail the enrichment constraint time-dependent ISA (ECTDISA) for identifying biologically meaningful temporal transcription modules from time series microarray dataset. In the end, we provide an example of ECTDISA application to time series microarray data of Kaposi’s Sarcoma-associated Herpesvirus (KSHV) infection.

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Acknowledgments

This work is supported by an NSF Grant CCF-0546345.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Jia Meng & Yufei Huang

  2. Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Yufei Huang

Authors
  1. Jia Meng
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  2. Yufei Huang
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Corresponding author

Correspondence to Yufei Huang .

Editor information

Editors and Affiliations

  1. Norwegian Radium Hospital, Oslo University Hospital, Montebello, Oslo, 0310, Norway

    Junbai Wang

  2. School of Medicine, University of Colorado Denver, E. 17th Avenue 12801, Aurora, 80010, Colorado, USA

    Aik Choon Tan

  3. School of Mathematics and Statistics, University of Glasgow, Glasgow, 3800, United Kingdom

    Tianhai Tian

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© 2012 Springer Science+Business Media, LLC

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Meng, J., Huang, Y. (2012). Biclustering of Time Series Microarray Data. In: Wang, J., Tan, A., Tian, T. (eds) Next Generation Microarray Bioinformatics. Methods in Molecular Biology, vol 802. Humana Press. https://doi.org/10.1007/978-1-61779-400-1_6

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  • DOI: https://doi.org/10.1007/978-1-61779-400-1_6

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-399-8

  • Online ISBN: 978-1-61779-400-1

  • eBook Packages: Springer Protocols

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