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Cluster Analysis of Genomic Data

  • K. S. Pollard
  • M. J. van der Laan
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

We provide an overview of existing partitioning and hierarchical clustering algorithms in R. We discuss statistical issues and methods in choosing the number of clusters, the choice of clustering algorithm, and the choice of dissimilarity matrix. We also show how to visualize a clustering result by plotting ordered dissimilarity matrices in R. A new R package hopach, which implements the Hierarchical Ordered Partitioning And Collapsing Hybrid (HOPACH) algorithm, is presented (van der Laan and Pollard, 2003). The methodology is applied to a renal cell cancer gene expression data set.

Keywords

Cluster Algorithm Cluster Result Fuzzy Cluster Dissimilarity Matrix Hierarchical Cluster Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • K. S. Pollard
  • M. J. van der Laan

There are no affiliations available

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