Syllabus for Biostatistics 278
“Statistical Analysis of DNA Microarray Data”
4 units
Objective: This course will give an introduction to DNA microarray technology and
present statistical methods for handling the complex data produced by experiments using
the technology.
Outline: The course seeks to provide instruction in the use of statistical tools used to
analyze microarray data. The structure will correspond to the analytical protocol an
investigator might follow when working with microarray data. The final project will give
the students an opportunity to analyze a microarray data set.
1. Introduction to Genetics and Molecular Biology
2. Microarray Technologies: cDNA, Affymetrix
3. Data Acquisition, Normalization and Outlier Detection; dChip, Affymetrix
Microarray Suite.
4. Significance testing and Gene filtering. SAM
5. Cluster Analysis and Graphical tools: hierarchical clustering with various
modification, Treeview.
6. Prediction methods: class prediction, SOM, CART.
7. Multivariate statistical techniques: Principal components, multidimensional
scaling.
8. Tissue arrays
Format: Two lectures, Tuesdays and Thursdays, 90 minutes each.
Instructors:
Professor David Elashoff
Rm: 21-254C
Ph# 794-7835
dae@ucla.edu
Professor Steve Horvath
Rm: 51-236
Ph# 825-9299
SHorvath@mednet.ucla.edu
Assignments: Weekly reading assignments, one in-class presentation, four computer
assignments and a group final project.
Grading: The final grade will be determined as follows:
Computer Assignments
20%
There will be four computer assignments that will introduce students to a number of the
main microarray analysis software packages.
In class presentations
30%
There will be one class presentations (30 minutes). For the presentation the student will
chose a paper covering some aspect of the statistical analysis of microarray data and
present a summary and review of the paper.
Final project
50%
The final project will be a complete analysis of a microarray dataset. Students can use a
dataset that the instructors will provide or one of their choosing. The project must include
the specification and justification of an analysis plan and a complete presentation of
results from the analysis along with any conclusions that can be drawn. Final projects
will be done in groups of 3-4 students and will include a class presentation of the results.
Weekly reading assignments will be available on JSTOR or the course website. We will
demonstrate a number software packages for microarray data analysis that can be
downloaded for free.
Prerequisites: An understanding of generally used statistical methods. Intended for 2nd
year masters students and doctoral students in biostatistics. Consent of the instructor will
be required.
Course Readings by Section
1,2: Intro to Microarrays
1. M Schena, D Shalon, R Heller, A Chai, PO Brown, RW Davis (1996)
"Parallel human genome analysis: microarray-based expression monitoring of 1000
genes",
Proceedings of the National Academy of Sciences, 93(20):10614-10619.
2. JL DeRisi, VR Iver, PO Brown (1997),
"Exploring the metabolic and genetic control of gene expression on a genomic scale",
Science, 278(5338):680-686.
3. DA Lashkari, JL DeRisi, JH McCusker, AF Namath, C Gentile, SY Hwang, PO
Brown, RW Davis (1997),
"Yeast microarrays for genome wide parallel genetic and gene expression analysis",
Proceedings of the National Academy of Sciences, 94(24):13057-13062.
4. R Lipshutz, S Fodor, T Gingeras, D Lockhart (1999), “High density synthetic
oligonucleotide arrays”, Nature Genetics, 21(1 suppl):20-24
5 . P Brown, D Bostein (1999),
"Exploring the new world of the genome with DNA microarrays", Nature Genetics, 21(1
suppl):33-37.