Tree-Dependent Components of Gene Expression Data for Clustering

  • Jong Kyoung Kim
  • Seungjin Choi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metabolic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.


Gene Expression Data Singular Value Decomposition Independent Component Analysis Hide Variable Independent Component Analysis 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jong Kyoung Kim
    • 1
  • Seungjin Choi
    • 1
  1. 1.Department of Computer SciencePohang University of Science and TechnologyPohangKorea

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