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Statistical methods for gene expression analysis

Part 4. Bioinformatics

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

Specialist Review

  1. Shibing Deng1,2,
  2. Tzu-Ming Chu1,
  3. Young K. Truong2,
  4. Russ D. Wolfinger1

Published Online: 15 NOV 2005

DOI: 10.1002/047001153X.g405210

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Deng, S., Chu, T.-M., Truong, Y. K. and Wolfinger, R. D. 2005. Statistical methods for gene expression analysis. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:53.

Author Information

  1. 1

    SAS Institute Inc., Cary, NC, USA

  2. 2

    University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Publication History

  1. Published Online: 15 NOV 2005

Abstract

Microarray technology makes it possible to simultaneously study the expression levels from thousands of genes and is widely used in functional genomics research. The analysis of microarray data provides a challenge to researchers because of its high dimensionality and complexity. Extensive research has been devoted recently to address the statistical issues raised from the analysis of the microarray data, and a comprehensive review is not possible here given space limitations and the proliferation of new papers. Instead, this chapter reviews some of the more basic statistical methods used for microarray gene expression data analysis. We divide the methods into three major areas: differential expression, classification, and clustering.

Keywords:

  • microarray;
  • gene expression;
  • differential expression;
  • classification;
  • clustering;
  • transcriptomics