   BMI 209 Home Announcements Syllabus Schedule & Handouts Links |
BMI 209 - Statistical Analysis of Microarray Data Fall
2004, 1 unit Course
coordinator: Ru-Fang Yeh <rufang@biostat.ucsf.edu>
Office Hours:By appointment. Objective: This course will introduce, illustrate and evaluate a variety of statistical methods employed in the context of microarray data analysis. Techniques covered correspond to commonly encountered research questions and study designs and include preprocessing/normalization, linear models, multiple hypothesis testing, clustering, discrimination, prediction and annotation with gene ontology and sequence information. The course will also showcase some relevant computational tools and resources, useful for effecting these analyses. A brief overview of microarray technology is included, as is discussion of recent array-related developments and extensions. Upon completion of the course students will be able to: 1. Perform microarray data analyses. 2. Identify and use relevant resources (genomic data and tools) for their own research. 3. Assess microarray data analyses performed by others. 4. Design studies using microarray technology. Topics: 9/21 Lecture 1: Introduction to genomics and microarray technology [DeRisi] 9/28 Lecture 2: Introduction to microarray bioinformatics [Yeh] - Sequence databases,
gene finding and probe
design, microarray standards (MIAME)
10/5 Lecture 3: Introduction to microarray analysis [Yang] - Exploratory data
analysis for two-color
spotted
arrays and Affymetrix gene chip data; sources of variability,
experimental designs, and normalization issues.
10/12 Lecture 4: Array analysis I: Identify DE genes in two-sample studies [Yeh] - Two-sample statistics
for differential
expression (DE) and multiple testing issues.
10/19 Lecture 5: Array analysis II: Identify DE genes with complex models [Yang] - Introduction to linear
models and its
application in analyzing complex gene expression experiments with two
or more treatment comparisons.
10/26 Lecture 6: Array analysis III: Clustering [Fridlyand] - Introduction to
different clustering
algorithms,
method characteristics and comparison.
11/2 Lecture 7: Array analysis IV: Discrimination [Fridlyand] - Different
classification procedures,
cross-validation and performance assessment.
11/9 Lecture 8: Array Analysis V: Prediction [Segal] - Methods for handling
expression data with
linked
continuous and survival phenotypes.
11/16 Lecture 9: Array Analysis VI: Functional annotation [Yang & Yeh] - Making sense of
co-expression
using Gene Ontology and sequence information.
11/23: Lecture 10: Special Topic: Non-expression arrays [Sen] - Array CGH, SNP array,
eQTL.
Grades: 30% - classroom participation 70% - oral/poster presentation of a student project: case study of your own data, re-analysis of publicly available data, or literature review. Textbook: No textbook is required. References will be provided and posted on the course website for individual lectures at least one week prior to the lecture. Recommended readings: 1. A Primer of Genome Science by G. Gibson & S.V. Muse. 2001. Sinauer Associates 2. Statistical Analysis of Gene Expression Microarray Data edited by T.P. Speed. 2003. Chapman & Hall/CRC. |