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Application and Analysis of
Microarrays |
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Course Information |
Lecture: Tu
Information and Handouts
Course Description & Objectives
Prepare biology, chemistry,
and bioinformatics graduate students for research projects and collaborations
using microarrays that measure biological processes (i.e. DNA microarrays); course assignments
emphasize interdisciplinary collaboration.
All students, regardless of initial
background, will learn:
- the basics of microarray design and construction
- to design simple experiments with hypotheses testable by microarray
experiments, as well as to develop hypotheses from available microarray
data that are testable by further experimentation
- to use GenePix software to collect data,
and Bioconductor software to perform basic data
analysis to assess data quality, filter/interpolate for poor or missing
data, and group correlated signals
- to understand and be able to apply advanced data analysis
techniques in appropriate situations
- to objectively judge the quality and rigor of published array
experiments from the primary literature
- to appreciate the breadth of applications and limitations of using
microarray technology
Prerequisites
- Any?
Grading
Course grades will be based 50% on homework assigments and 50% on the Final Project. Some extra credit
may be awarded for class participation.
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Communication |
We strongly encourage students to come to office
hours.
Please do not e-mail the instructors with
grading questions. If you want us to explain why we took points off, you can
talk to us after class or during office hours. If you want a re-grade, please
write an explanation and hand the homework and the explanation to one of the
instructors during office hours or after class.
Occasionally we may need to broadcast a
message to entire class. To make sure we can reach you, please subscribe to
the BME210 mailing list by sending an e-mail to majordomo@lists.ucsc.edu with a
blank subject line and "subscribe bme210" as your message.
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Textbook Information |
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The primary reading material will be the book
“Microarray: Gene Expression Data Analysis” by Causton,
Quackenbush, and Brazma.
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An optional
text we recommend for slightly more advanced statistical topics is the book “Statistcal Analysis of Gene Expression Microarray Data”
edited by Terry Speed.
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Comments to Josh Stuart |