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Algorithms for gene expression analysis

Part 4. Bioinformatics

4.5. Computational Methods for High-throughput Genetic Analysis: Expression Profiling

Specialist Review

  1. Alvis Brazma1,
  2. Aedín C. Culhane2

Published Online: 15 NOV 2005

DOI: 10.1002/047001153X.g405202

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Brazma, A. and Culhane, A. C. 2005. Algorithms for gene expression analysis. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:54.

Author Information

  1. 1

    European Bioinformatics Institute, Cambridge, UK

  2. 2

    The Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland

Publication History

  1. Published Online: 15 NOV 2005


A new genre of algorithms, which blends bioinformatics, statistics, and machine learning, is emerging to tackle the challenges of gene expression data analysis. Microarrays enable genome-scale high-throughout measurement of gene expression, and have produced unprecedented amounts of gene expression data. In this chapter, we present an overview of some of the many algorithms that have been employed in the analysis of these data. These include many well-known algorithms and new algorithms developed to address data-specific issues. We describe various clustering algorithms, including hierarchical and k-means, and ordination methods, such as principal component analysis, that can be applied to unsupervised exploration of data. We also provide an introduction to a number of supervised, meta-analysis, and gene selection methods. Where possible, we mention software tools that implement these algorithms. Finally, we stress the importance of a standard data format and data repositories for sharing of microarray gene expression data.


  • microarray;
  • analysis;
  • supervised;
  • clustering;
  • ordination;
  • principal component analysis