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Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters

Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 541)

Abstract

Clustering is a popular technique commonly used to search for groups of similarly expressed genes using mRNA expression data. There are many different clustering algorithms and the application of each one will usually produce different results. Without additional evaluation, it is difficult to determine which solutions are better.

In this chapter we discuss methods to assess algorithms for clustering of gene expression data. In particular, we present a new method that uses two elements: an internal index of validity based on the MDL principle and an external index of validity that measures the consistency with experimental data. Each one is used to suggest an effective set of models, but it is only the combination of both that is capable of pinpointing the best model overall. Our method can be used to compare different clustering algorithms and pick the one that maximizes the correlation with functional links in gene networks while minimizing the error rate. We test our methods on several popular clustering algorithms as well as on clustering algorithms that are specially tailored to deal with noisy data. Finally, we propose methods for assessing the significance of individual clusters and study the correspondence between gene clusters and biochemical pathways.

Key words

Microarrays mRNA expression clustering evaluation 
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Notes

Acknowledgments

This work is supported by the National Science Foundation under Grant No. 0218521, as part of the NSF/NIH Collaborative Research in Computational Neuroscience Program.

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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Biological Statistics and Computational BiologyCornell UniversityIthacaUSA
  2. 2.Department of Computer ScienceTechnion - Israel Institute of TechnologyHaifaIsrael
  3. 3.Center for Integrative GenomicsUniversity of California, BerkeleyBerkeleyUSA
  4. 4.Mathworks Inc.NatickUSA

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