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Large error models for microarray intensities

Part 4. Bioinformatics

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

Basic Techniques and Approaches

  1. Wolfgang Huber1,
  2. Anja von Heydebreck2,
  3. Martin Vingron3

Published Online: 15 NOV 2005

DOI: 10.1002/047001153X.g405413

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Huber, W., von Heydebreck, A. and Vingron, M. 2005. Large error models for microarray intensities. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:62.

Author Information

  1. 1

    European Bioinformatics Institute, European Molecular Laboratory, Cambridge, UK

  2. 2

    Merck KGaA, Department of Bio- and Chemoinformatics, Darmstadt, Germany

  3. 3

    Max-Planck Institute for Molecular Genetics, Berlin, Germany

Publication History

  1. Published Online: 15 NOV 2005

Abstract

We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (“normalization”) of data with respect to experimental factors, we describe a method for parameter estimation.

Keywords:

  • error model;
  • microarrays;
  • differential expression;
  • normalization;
  • calibration;
  • variance stabilization;
  • parameter estimation