Unit

UNIT 22.5 Pattern Discovery in Expression Profiling Data

  1. Fumiaki Katagiri,
  2. Jane Glazebrook

Published Online: 1 FEB 2005

DOI: 10.1002/0471142727.mb2205s69

Current Protocols in Molecular Biology

Current Protocols in Molecular Biology

How to Cite

Katagiri, F. and Glazebrook, J. 2005. Pattern Discovery in Expression Profiling Data. Current Protocols in Molecular Biology. 69:22.5:22.5.1–22.5.11.

Author Information

  1. University of Minnesota, St. Paul, Minnesota

Publication History

  1. Published Online: 1 FEB 2005
  2. Published Print: JAN 2005

This is not the most recent version of the article. View current version (1 JAN 2009)

Abstract

In expression profiling studies, it is often necessary to identify groups of genes with similar expression profiles in a variety of samples, and/or groups of samples with similar expression profiles. Each profile can be expressed as a single data point in a space with the same number of dimensions as there are parameters in the profiles. In this way, pattern discovery among expression profiles is translated into pattern discovery in the spatial distribution of data points. Hierarchical clustering is useful for clustering similarly behaving genes or samples at local levels and for displaying the results in a simple color-coded manner. K-means clustering can be used for discovery of well-defined clusters. Principal component analysis and self-organizing maps can be used for dimensionality reduction, thereby facilitating visualization of major trends in data sets.

Keywords:

  • Hierarchical clustering;
  • K-means;
  • dimensionality reduction;
  • Principal component analysis;
  • Self-organizing maps;
  • Pearson correlation coefficient