With the recent radical breakthrough in molecular biotechnology and advanced instruments, high throughput genetic and genomic data can now be generated from any organism almost with no limits. For example, thousands of gene expression profiles of an living organism can be obtained routinely in any lab. Millions of Single Nucleotide Polymorphism (SNP) data can be generated with advanced array technology. These massive amount of high-dimensional data bring us unprecedented opportunities to understand the ultimate function of genes in genome or sequence level. Meanwhile, we are facing enormous challenges both statistically and computationally in extracting maximum amount of information out of the "messy" data.
Currently, many investigators are frustrating in choosing appropriate statistical methods in analyzing their data, designing efficient experiments, and drawing meaningful conclusions. The goal of the workshop is to train faculty, postdoc and students from MSU as well as neighboring institutions and bring them the latest developments in design and analysis of genetic and genomic data with cutting edge experimental practices, and to foster long-term collaborative relationship and networking opportunities at Michigan.
The workshop is jointly sponsored by Center for Statistical Training and Consulting (CSTAT), Department of Statistics and Probability, and the Genetics Program at MSU.
Organizer: Yuehua Cui, Ph.D.
A432 Wells Hall
Department of Statistics and Probability
Michigan State University
East Lansing, MI 48824
Email: cui@stt.msu.edu
Connie Page, Ph.D. Marianne Huebner, Ph.D.
Department of Statistics and Probability Department of Statistics and Probability
Michigan State University Michigan State University
East Lansing, MI 48824 East Lansing, MI 48824