April 19, 2014

Short Course in Microarray Data Analysis
May 25, 2002

Talk slides available online at

Led by:
Terry Speed, University of California - Berkeley, Department of Statistics and Program in Biostatistics, and The Walter & Eliza Hall Institute of Medical Research, Division of Genetics and Bioinformatics, Australia

Jean (Yee Hwa) Yang, University of California - Berkeley, Department of Statistics

Ben Bolstad, University of California - Berkeley, Department of Statistics

See their web site for microarray data analysis: http://www.stat.Berkeley.EDU/users/terry/zarray/Html/index.html

With assistance from: Iobian Informatics, and The University Health Network Microarray Centre, University of Toronto

Microarray technology, which provides a way to globally measure differential gene expression, promises to be extremely useful for the diagnosis, treatment, and prevention of complex disease as well as for the elucidation of biological mechanisms. These studies yield tens of thousands of simultaneous gene measurements from each biological sample. Issues in measurement and calibration of the microarrays need to be addressed appropriately in order to obtain valid datasets. To gain insight into genes and their function, patterns of expression and expression changes must then be discerned from high-dimensional data in which the number of observations is small relative to the number of variables.

The purpose of the one-day Shortcourse in Statistics for Microarray Data Analysis is to introduce statisticians and other researchers to statistical issues in the design and analysis of microarray studies of current interest to biologists and biomedical researchers. Experience with statistical methods and in data analysis is a pre-requisite, but no previous exposure to microarray data is assumed. The course will include the opportunity for participants to apply statistical methods to several datasets that will be provided.

Register early since space is limited to 90 participants (2 participants for each computer terminal).

Saturday May 25, 2002
9:00 - 9:45 am

Session 1. Biological and technical background
Brief summary of issues relating to DNA, RNA, transcription,
cDNA, hybridization, cDNA microarray construction and use,
including imaging and image analysis.
9:45 - 10:00 Break
10:00 - 10:45

Session 2. Design and preprocessing
Pros and cons of different designs including direct, reference, loop,
factorial, and time series alternatives. Ways of looking at the data,
and normalization to adjust for intensity-dependent and spatial
biases, and other systematic effects.
10:45 - 11:00 Break
11:00 - 12:30 Computer Lab (Room 208 and 210)
12:30 - 2:00
2:00 - 2:45

Session 3. Basic analyses
Estimating and testing for differential expression.
Multiple testing adjustments. Empirical Bayes.
Linear models for designed experiments.
2:45 - 3:00 Break
3:00 - 3:45

Session 4. Advanced analyses
Classification, clustering and other multivariate methods.
Ideas for addressing issues relating to pathways and networks.
3:45 - 4:00 Break
4:00 - 5:30 Computer Lab (Room 210) (Room 208 participants begin at 4:30)