Announcements

Lectures

Homework

Final project

References

Datasets

Time and place

TuTh 12:30-2:00pm, 215 Dwinelle Hall.
Please note room change.

Sandrine Dudoit
Website: www.stat.berkeley.edu/~sandrine
E-mail: sandrine@stat.berkeley.edu
Office hours: TuTh 2:00-3:00pm, 109 Haviland Hall.

This course surveys statistical and computational methods for the analysis of biomedical and genomic data, from the early Mendelian experiments to modern day genomic research.

Biological questions of interest include, but are not limited to: modeling meiosis; genetic mapping; nucleotide and protein sequence analysis; DNA microarray data analysis; biological metadata analysis.

Related statistical topics include: numerical and graphical summaries of data; stochastic processes (Markov processes, hidden Markov models, Markov chain Monte Carlo); experimental design; loss-based estimation (e.g., least-squares regression, classification, maximum likelihood estimation, density estimation, variable/model selection); multiple hypothesis testing; resampling (bootstrap, cross-validation); simulation studies.

The course also introduces statistical computing resources for the analysis of biological data, with emphasis on the R language and environment (www.r-project.org) and Bioconductor software packages (www.bioconductor.org).

In addition to discussing specific statistical and computational methods, the course provides an introduction to basic notions in genetics and molecular biology and involves the critical reading of articles related to statistical analyses in the biological and medical sciences.

I encourage you to attend PH 296, the Statistics and Genomics Seminar (schedule and abstracts)  

Public Health 240D, Section 001
Course control number: 76302
Units: 4

Online schedule of classes
Academic calendar

STAT 200A and STAT 200B (may be taken concurrently) or consent of instructor. 

Some familiarity with the S language (R or S-Plus). Tutorials are available on the R and Bioconductor websites.

No formal training in biology is required; basic notions will be presented in class and references will be provided for further reading.

50% homework and 50% final project. 

Assignments will involve both theory and biological data analysis using R.

The final project will consist of a written report and poster presentation on a topic that involves the application of statistical and computational methods to address a particular biological question. I will provide a list of suggested topics.

There is no required textbook. Lecture notes and references will be provided on the class website.