INTEGRATIVE STATISTICAL ANALYSIS OF
GENOME SCALE DATA
June 8 - 23, 2009
Application Deadline: March 15, 2009

Instructors:
Harmen Bussemaker, Columbia University
Vincent Carey, Harvard University
Christina Leslie, Sloan-Kettering Institute
Partha Mitra, Cold Spring Harbor Laboratory
Mark Reimers, Virginia Commonwealth University

Availability of a variety of genome-scale data sets, and the need to integrate such data sets, is a central feature of modern biological research. Experimental and computational biologists seeking to make sense of such data sets need to have a firm grasp of the relevant statistical and analytical methodology. This course is designed to build competence in quantitative methods for the analysis of high- throughput molecular biology data.

Topics include:
*Introduction to R and Bioconductor
*Review of multivariate statistics (multiple testing, regression, machine learning)
*Survey of key high-throughput technologies (both microarray- and sequencing-based)
*Low-level microarray data analysis (quality control, normalization)
*Analysis based on predefined gene sets (e.g., Gene Ontology) *Classification and prognosis of cancer samples by machine learning
*Cis-regulatory sequence analysis (motif finding, weight matrices)
*Modeling of transcriptional networks through integration of mRNA expression, ChIP, and sequence data
*Integration of genotype (SNP) data and expression data *Integration of epigenetic (DNA methylation) data and expression data

Format:
Detailed lectures and presentations by guest speakers in morning and evening will be combined with hands-on computer tutorials in the afternoon, in which the methods covered in the lectures are applied to actual high-throughput data for yeast and human. Students are assumed to have a basic familiarity with the R programming language at the start of the course.

Speakers last year included:
Stefan Bekiranov, University of Virginia
Martha Bulyk, Brigham & Womens Hospital/Harvard Medical Sch
Bruce Futcher, Stony Brook University
Vishy Iyer, University of Texas at Austin
Robert Lucito, Cold Spring Harbor Laboratory
Trudy Mackay, North Carolina State University
Ewy Mathe, NCI
Adam Olshen, Memorial Sloan Cancer Center
Dana Pe'er, Columbia University
Michael Snyder, Yale University
John Stamatoyannopoulos, University of Washington

This course is supported with funds provided by the National Cancer Institute