JSM 2004 - Toronto

Abstract #300189

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Activity Number: 97
Type: Invited
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #300189
Title: Multi-study Genomic Data Analysis
Author(s): Giovanni Parmigiani*+ and Elizabeth Garrett and Xiaogang Zhong and Edward Gabrielson
Companies: Johns Hopkins University and Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
Address: 550 North Broadway Suite 1103, Baltimore, MD, 21205,
Keywords: genomics ; meta-analysis ; microarrays ; breast cancer ; lung cancer ; validation
Abstract:

Several recent studies sought to refine cancer classification using gene expression microarrays. Uncertainty remains regarding the extent to which these studies agree, and whether results can be integrated. We developed practical tools for cross-study comparison, validation, and integration of cancer molecular classification studies using public data. We show how to evaluate genes for cross-platform consistency of expression patterns, using "integrative correlations," which quantify cross-study reproducibility without relying on direct assimilation of expression measurements across platforms. We then compare associations of gene expression levels to differential diagnosis of squamous cell carcinoma versus adenocarcinoma, via reproducibility of the gene-specific t-ratios, and to survival, via reproducibility of Cox coefficients. We use this comparison as a testbed for developing simple approaches to the normalization of expression measurement across microarray platforms. Finally, we show preliminary progress on the cross-study validation of results of unsupervised cluster analyses.

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