Large error models for microarray intensities
Part 4. Bioinformatics
4.5. Computational Methods for High-throughput Genetic Analysis: Expression Profiling
Basic Techniques and Approaches
Published Online: 15 NOV 2005
Copyright © 2005 John Wiley & Sons, Ltd
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.
- Published Online: 15 NOV 2005
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.
- error model;
- differential expression;
- variance stabilization;
- parameter estimation