Winter Term 2003/2004


Practical Analysis of Gene Expression Data

Jointly taught by:

  • Korbinian Strimmer, Juliane Schäfer, Anne-Laure Boulesteix (Statistics/LMU), and
  • Alexander Yassouridis, Klaus Hechenbichler, Bertram Müller-Myhsok, Markus Panhuysen, Benno Pütz, Daria Salyakina, Klaus Schliep, Shaun Seaman (Max Planck Institute for Psychiatry).


For participation please register (until 31 January 2004) with either Korbinian Strimmer (email) or Alexander Yassouridis (email).


This microarray workshop provides a practical introduction to statistical and computational aspects of gene expression analysis. Emphasis is put both on the theoretical background, such as normalization procedures, differential expression, ANOVA, and clustering and classification procedures, as well as on practical exercises using R packages for gene expression analysis (in particular the Bioconductor packages).

The course runs full four days (10:00-17:00) from 16 to 19 February 2004 and takes place in the computer room of the Department of Statistics.


  • Introduction to microarray technology
  • Normalization procedures
  • Differential expression
  • Clustering gene expression data
  • Classification of tissue samples
  • Inference of genetic networks

On each topic there will be both lectures and practical excercises.

For further details please download the course flyer (pdf) and the timetable and program (pdf).

Course Materials:

Day Talk/Tutorial Speaker/Tutor
16.2.2004 Introduction to microarray technology Marcus Panhuysen
Preprocessing and normalization Benno Pütz
Introduction to R Klaus Schliep
Introduction to R (exercise and solution) Klaus Schliep
Normalization (exercise and solution) Korbinian Strimmer
17.2.2004 t-test and other related methods Shaun Seaman
Multiple testing Daria Salyakina
Differential expression Bertram Müller-Myhsok
Multiple testing and differential expression (exercises) Benno Pütz
18.2.2004 Experimental design/MANOVA Alexander Yassouridis
Clustering and classification methods Klaus Hechenbichler
Clustering and classification (exercises) Klaus Schliep and
Klaus Hechenbichler
19.2.2004 Microarray analysis beyond differential expression Korbinian Strimmer
Identifying periodically expressed genes Korbinian Strimmer
Emerging patterns and gene interactions Anne-Laure Boulesteix
Inferring large graphical models from microarray data Juliane Schäfer
Special topics (exercise and solution) Juliane Schäfer and Anne-Laure Boulesteix

The data sets required for the exercises on clustering and classification (day 3) are also available (compressed with gzip): carci.rda, nci.rda, leukemia.rdata, srbct.rdata, colon.rdata.

The R packages mainly used are the Bioconductor packages and the GeneTS package.

We gratefully acknowledge that parts of the above material is taken from the Bioconductor web site and from the NGFN Microarray data analysis resource.

Last modified:
January 20, 2004