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Related Experiment Videos

Kernel hierarchical gene clustering from microarray expression data.

Jie Qin1, Darrin P Lewis, William Stafford Noble

  • 1Columbia Genome Center, Columbia University, 1150 St. Nicholas Avenue, New York, NY 10032, USA. jq22@columbia.edu

Bioinformatics (Oxford, England)
|November 5, 2003
PubMed
Summary
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Kernel hierarchical clustering enhances gene expression analysis by incorporating higher-order features. However, improved clustering performance requires combining this mapping with learning algorithms like support vector machines to avoid the curse of dimensionality.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Gene Expression Analysis

Background:

  • Unsupervised analysis of microarray gene expression data seeks to identify biologically significant patterns.
  • Hierarchical clustering groups genes with similar expression profiles across experiments.
  • Higher-order features (pairwise, tertiary correlations) may improve recognition of co-expressed gene classes.

Purpose of the Study:

  • To generalize hierarchical clustering to efficiently incorporate higher-order gene expression features.
  • To evaluate the utility of kernel hierarchical clustering using internal and external validation.
  • To determine if mapping gene expression data into high-dimensional feature spaces improves clustering.

Main Methods:

  • Developed a generalized hierarchical clustering algorithm using kernel functions.

Related Experiment Videos

  • Mapped gene expression data into a high-dimensional feature space.
  • Evaluated clustering performance using internal and external validation metrics.

    • Main Results:

    • The kernel representation alone did not improve clustering performance.
    • Mapping gene expression data into high-dimensional spaces requires a suitable learning algorithm.
    • Support vector machines, which do not suffer from the curse of dimensionality, are effective when combined with this mapping.

    Conclusions:

    • Kernel hierarchical clustering, while incorporating higher-order features, is insufficient on its own for improved performance.
    • Effective utilization of high-dimensional feature spaces in gene expression analysis necessitates complementary learning algorithms.
    • Support vector machines offer a viable approach to leverage high-dimensional representations for enhanced co-expressed gene class recognition.