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Integrating statistical approaches in experimental design and data analysis

Part 4. Bioinformatics

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

Introductory Review

  1. Ernst Wit,
  2. Raya Khanin

Published Online: 15 NOV 2005

DOI: 10.1002/047001153X.g405115

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Wit, E. and Khanin, R. 2005. Integrating statistical approaches in experimental design and data analysis. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:50.

Author Information

  1. University of Glasgow, Glasgow, UK

Publication History

  1. Published Online: 15 NOV 2005

Abstract

In complex experiments such as microarray studies, the quantity of interest is measured via a long path of intermediate technologies. It is not surprising that the final signal is partially corrupted by the measurement process itself. Statistical design is aimed at assigning the replicates in an optimal way to the conditions of interest. It should be seamlessly integrated with the statistical methods to recover the signal, so that it is possible to evaluate the scientific question of interest most efficiently.

Keywords:

  • statistical design;
  • ANOVA;
  • blocking;
  • optimal design;
  • interwoven loop design