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Abstract Details

Activity Number: 437
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #303796
Title: Multivariate Multiple Testing Procedures for High-Throughput Data
Author(s): Debashis Ghosh*+
Companies: Penn State University
Address: , University Park, ,
Keywords: False discovery rate ; multiple comparisons ; multivariate p-values ; genomic data integration ; oncology ; biomarkers

Large-scale datasets such as the Cancer Genome Atlas generate massive amounts of data that require sophisticated tools for statistical interpretation and analysis. In this talk, we use the idea of pathways as a means of unifying information from disparate genomic data types. In particular, we will consider multivariate extensions of classical multiple testing procedures that make no assumptions about the correlation structure between data types. We will also explore the use of wavelet denoising procedures to address the correlation between pathways. The proposed methods maintain the proper error control and have greater power than standard multiple testing procedures. We will use real and simulated data to illustrate the methodology.

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