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Differential expression with the Bioconductor Project

Part 4. Bioinformatics

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

Specialist Review

  1. Anja von Heydebreck1,
  2. Wolfgang Huber2,
  3. Robert Gentleman3

Published Online: 15 NOV 2005

DOI: 10.1002/047001153X.g405208

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

von Heydebreck, A., Huber, W. and Gentleman, R. 2005. Differential expression with the Bioconductor Project. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:55.

Author Information

  1. 1

    Max-Planck-Institute for Molecular Genetics, Berlin, Germany

  2. 2

    German Cancer Research Center, Heidelberg, Germany

  3. 3

    Dana-Farber Cancer Institute, Boston, MA, USA

Publication History

  1. Published Online: 15 NOV 2005

Abstract

A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes' expression levels. Possible strategies are to do univariate gene-by-gene analysis, and to perform data-driven nonspecific filtering of genes before the actual statistical analysis. However, more focused strategies that make use of biologically relevant knowledge are more likely to increase our understanding of the data.

Keywords:

  • differential gene expression;
  • microarrays;
  • multiple testing;
  • statistical software;
  • biological metadata