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Clustering of Multiple Microarray Experiments Using Information Integration

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Information Technology in Bio- and Medical Informatics (ITBAM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6865))

Abstract

In this article, we study two microarray data integration techniques and describe how they can be applied and validated on a set of independent, but biologically related, microarray data sets in order to derive consistent and relevant clustering results. First, we present a cluster integration approach, which combines the information containing in multiple data sets at the level of expression or similarity matrices, and then applies a clustering algorithm on the combined matrix for subsequent analysis. Second, we propose a technique for the integration of multiple partitioning results. The performance of the proposed cluster integration algorithms is evaluated on time series expression data using two clustering algorithms and three cluster validation measures. We also propose a modified version of the Figure of Merit (FOM) algorithm, which is suitable for estimating the predictive power of clustering algorithms when they are applied to multiple expression data sets. In addition, an improved version of the well-known connectivity measure is introduced to achieve a more objective evaluation of the connectivity performance of clustering algorithms.

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Author information

Authors and Affiliations

  1. Branch Plovdiv, Computer Systems and Technologies Department, Technical University of Sofia, 4400, Plovdiv, Bulgaria

    Elena Kostadinova & Veselka Boeva

  2. School of Computing, Blekinge Institute of Technology, SE-371 79, Karlskrona, Sweden

    Niklas Lavesson

Authors
  1. Elena Kostadinova
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  2. Veselka Boeva
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  3. Niklas Lavesson
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Ludwig-Maximilians-Universität, Oettingenstrasse 67, 80538, München, Germany

    Christian Böhm

  2. Department of Computer Science, San José State University, One Washington Square, 95192-0249, San José, CA, U.S.A.

    Sami Khuri

  3. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University, Technicka 2, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  4. Dipartimento di Informatica, Università di Pisa, Italy

    Nadia Pisanti

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© 2011 Springer-Verlag Berlin Heidelberg

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Kostadinova, E., Boeva, V., Lavesson, N. (2011). Clustering of Multiple Microarray Experiments Using Information Integration. In: Böhm, C., Khuri, S., Lhotská, L., Pisanti, N. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2011. Lecture Notes in Computer Science, vol 6865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23208-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-23208-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23207-7

  • Online ISBN: 978-3-642-23208-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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