Integrated Statistical Analysis of Genome Scale Data

Genome-scale data sets are a central feature of modern biological research. Experimental and computational biologists who want to get the most from their data sets need to have a firm grasp of 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)
*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.

2010 Speakers To be Announced

Speakers last year included:
Stefan Bekiranov, University of Virginia
Martha Bulyk, Brigham & Womens Hospital
Aedin Culhane, Dana Farber Cancer Institute
Robert Darnell, HHMI/Rockefeller University
Bruce Futcher, Stony Brook University
Rafael Irizarry, Johns Hopkins School of Public Health
Daniel Kliebenstein, University of California, Davis
Shirley Liu, Dana Farber Cancer Institute
Robert Lucito, Cold Spring Harbor Laboratory
Joshua Zhang, Dana Farber Cancer Insitute