limma: Linear Models for Microarray Data

A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

Editors and Affiliations

1. Program in Computational Biology Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, M2-B876, PO Box 19024, Seattle, Washington, 98109-1024, USA

   Robert Gentleman

2. Channing Laboratory Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA

   Vincent J. Carey

3. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK

   Wolfgang Huber

4. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA

   Rafael A. Irizarry

5. Division of Biostatistics School of Public Health, University of California Berkeley, 140 Earl Warren Hall, #7360, Berkeley, CA, 94720-7360, USA

   Sandrine Dudoit

Reprints and permissions

© 2005 Springer Science+Business Media, Inc.

Policies and ethics