Gordon Smyth
August 16, 2005

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.

Lab exercises


Exercise Platform Design Topics covered
apoAI data cDNA Two group comparison with common reference Introduction to linear models. Obtaining empirical Bayes statistics. Getting lists of differentially expressed genes.
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 genewise dye effects.
Estrogen data Affymetrix 2x2 Factorial More on linear models. Use of contrasts. Venn diagrams. Linking gene lists to annotation information on the internet. Gene set tests.
Drosophila embryogenesis dataset Affymetrix Time course with series-level replication Time course analysis using linear models and moderated F-statistics.

Datasets used in the exercises

Please check whether you already have the "Drosophila Embryo" and "Estrogen" packages from the Required Software. (These data sets are stored within R packages.)

Lab Handouts

The handouts in pdf can be accessed here: lab handouts

Required R packages

Please install these packages before attempting to repeat the lab exercises. Note: if you don't have write permission to the system library directory of your R installation, you can use the .libPaths() function with something like .libPaths("C:/mylibdir") before you run install.packages() (or equivalent) to install the packages in a customized directory location.

A local cache of packages is here: Local Package Cache

Package

Windows

MacOS X

Source

limma_2.0.4

limma_2.0.4.zip

limma_2.0.4.tar.gz

limma_2.0.4.tar.gz

statmod_1.2.0

statmod_1.2.0.zip

statmod_1.2.0.tar.gz

statmod_1.2.0.tar.gz

affy_1.6.7

affy_1.6.7.zip

affy_1.6.7.tgz

affy_1.6.7.tar.gz

Biobase_1.5.12

Biobase_1.5.12.zip

Biobase_1.5.12.tgz

Biobase_1.5.12.tar.gz

hgu95av2_1.8.4

hgu95av2_1.8.4.zip

hgu95av2_1.8.4.tar.gz

hgu95av2_1.8.4.tar.gz

hgu95av2cdf_1.5.1

hgu95av2cdf_1.5.1.zip

hgu95av2cdf_1.5.1.tar.gz

hgu95av2cdf_1.5.1.tar.gz

xtable_1.2-5

xtable_1.2-5.zip

xtable_1.2-5.tgz

xtable_1.2-5.tar.gz

Getting started

You should be running R 2.1.0 or 2.1.1 and limma 2.0.4. 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()

References

  1. Smyth, G. K., Thorne, N. P. and Wettenhall J. (2005) limma: Linear Models for Microarray Data User's Guide. http://bioinf.wehi.edu.au/limma (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/
Fred Hutchinson Cancer Research Center