University of Copenhagen

Differential Expression Analysis of Microarray Experiments

Gordon Smyth
6 November 2007

Walter and Eliza Hall Institute of Medical Research

Aims

This laboratory explores some of the features of the limma package for assessing differential expression in microarray experiments. Examples are included of cDNA two-color microarrays and Affymetrix one-channel microarrays. Some pre-processing issues are also discussed for two-color arrays.

Getting started

The materials for this laboratory will be supplied as a directory ku2007limma from a network location or on a removable disk. You should copy the directory ku2007limma to a convenient location on your computer, for example c:\ku2007limma. Point your browser at c:\ku2007limma\html\index.html. Start R and make c:\cpn2007limma the working directory:

> setwd("c:/ku2007limma/data")

You should be running R 2.6.0 and limma 2.12.0. A good way to get started is to open up the Limma User's Guide:

> library(limma)

If you're using Windows, just use the drop-down menu "Vignettes". Otherwise, type

> limmaUsersGuide()

If you are using Windows, you can cut and paste the code from the tutorials into your R session using "paste commands only".

Lab exercises

Exercise Platform Design Topics covered
integrin beta7 data cDNA Direct comparisons with dye-swaps Data entry for two color data. Highlighting control probes. Exploring different background correction methods. Allowing for probe-specific dye effects.
Estrogen data Affymetrix 2x2 Factorial More on linear models. Use of contrasts. Venn diagrams.
Estrogen data II Affymetrix 2x2 Factorial Gene set tests.
SAHA/depsipeptide data cDNA Treatments x time course Time course analysis using linear models and moderated F-statistics.

References

  1. Smyth, G. K., Thorne, N. P. and Wettenhall J. (2007). limma: Linear Models for Microarray Data User's Guide. (Included as part of the limma package.)
  2. Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York.
  3. Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3/