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Short Course in Microarray Data Analysis
May 25, 2002
Talk slides available online at http://www.stat.berkeley.edu/users/terry/zarray/Course/ |
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
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Overview
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).
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Schedule |
9:00 - 9:45 am
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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
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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
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Lunch |
2:00 - 2:45
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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
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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) |
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