Abstract #301679

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JSM 2003 Abstract #301679
Activity Number: 346
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301679
Title: Generating "Hit Lists" from High-throughput Genomic Assays: Are Hierarchical Models Helping?
Author(s): Giovanni Parmigiani*+ and Dongmei Liu and Brian Scott Caffo
Companies: Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
Address: 550 N Broadway, School of Medicine, Baltimore, MD, 21205-2020,
Keywords:
Abstract:

High-throughput gene expression assays, such as SAGE and a variety of hybridization-based techniques, are often used in small-size comparative experimentS to generate "hit lists" of genes for further investigation. The standard strategy for generating hit lists has been to normalize and preprocess data, and then apply single-gene statistics for screening and ranking. While there is some borrowing of strength across genes in the normalization stage, many have found this insufficient, and several hierarchical methods have been proposed. I survey a selection of representative approaches, present a set of visualization tools for conceptualizing hierarchical models and choosing among them, and finally, present results of head-to head comparisons on spike-in experiments.

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