BMI 209 -

Statistical  Methods in Bioinformatics:   Case Studies

Fall 2006, 1 unit


Course website: http://www.biostat.ucsf.edu/cbmb/bmi209

Lectures: Thursdays 2-3.30pm, Genentech Hall S201(Mission Bay campus) (S202 on 9/21)

Instructors: CBMB faculty <cbmb@biostat.ucsf.edu>
(Jane Fridlyand, Ru-Fang Yeh, Yuan-Yuan Xiao, Mark Segal and guest speakers) 

Course coordinators:  Jane Fridlyand <jfridlyand@cc.ucsf.edu>
                                           Ru-Fang Yeh <rufang@biostat.ucsf.edu>

Office Hours: By appointment.

Target audience: BMI, PQB, BMS students; Interested auditors

Description 

This course offers students a series of weekly seminar-style lectures detailing methods for the analysis of high dimensional, molecular biological data through case studies. A range of statistical techniques, corresponding to frequently encountered research questions and study designs in genomics and bioinformatics, are illustrated and evaluated. Tools for performing such analyses are also described.

Objective:

This course is intended to expose students to approaches to formulating and tackling important data analytic problems that arise in the context of contemporary, high-throughput technologies.  These include DNA microarrays, ChIP-chip studies, SNP arrays, whole-genome sequence, and proteomic data. It is expected that students will acquire the ability to frame statistical hypotheses in such settings and be able to identify corresponding data analytic techniques. While such techniques will be introduced here via case studies, they pertain to more broadly encountered research questions and study domains. Examples (and the settings where they arise) include data preprocessing (expression, tiling and SNP arrays; mass spectrometry), multiple hypothesis testing (evolution, CpG island methylation), sequence analysis (motif finding), clustering (SNP arrays), and classification methods (CpG island methylation, copy number data).

Schedule:

Sep 14 (Lecture 1):  Dr. Ru Fang Yeh:
Overview of statistical issues in bioinformatics. Introduction to  microarray analysis;

Sep 21 (Lecture 2): Dr. Ru Fang Yeh:
Statistical approaches to the analysis of tiling arrays and ChIP-chip data:
Application to brain tumor data;

Sep 28 (Lecture 3): Dr. Yuan-Yuan Xiao
 Low –level analysis of SNP Chip data: Genotyping HapMap data;

 Oct 5 (Lecture 4): Dr. Jane Fridlyand
 Copy number arrays and example of  meta-analysis of genomic data:
 Demonstration of the methodology on the breast cancer data;

 Oct 12 (Lecture 5): Dr. Jane Fridlyand
 Classification in genomics and metabolomics: Application to tumor data; 

 Oct 19 (Lecture 6): Dr. Mark Segal
 Validation in genomics: CpG island methylation revisited;

Oct 26 (Lecture 7): Drs. Mark Segal & Yuan-Yuan Xiao
E-values for protein database searches based on tandem mass spectrometry scoring;
Case study in proteomic mass-spectrometry: Coronary artery disease data;

Nov 2 (Lecture 8): Dr. Haiyan Huang, UC Berkeley
A statistical framework to infer functional gene associations from multiple biologically
interrelated microarray experiments: application to yeast and human data;

Nov 9 (Lecture 9): Dr. Imola Fodor, LLNL
Phenotypic characterization of Yersinia pestis;

Nov 16 (Lecture 10): Dr. Katie Pollard, UC Davis
Detecting lineage-specific evolution of DNA.

 

Grades: 
  100% - classroom participation

Textbook:
    The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman. 2001. Springer.

Recommended readings:

1. Bioinformatics and Computational Biology Solutions using R and Bioconductor, edited by R. Gentleman et al. 2005. Springer.
2. Statistical Analysis of Gene Expression Microarray Data, edited by T.P. Speed. 2003. Chapman & Hall/CRC.
3. A Primer of Genome Science by G. Gibson & S.V. Muse. 2001. Sinauer Associates.