JSM 2004 - Toronto

Abstract #300534

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Activity Number: 19
Type: Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300534
Title: Stochastic Linear Hypotheses for Nonparametric Analysis of Microarrays
Author(s): Jeanne Kowalski*+
Companies: Johns Hopkins University
Address: 550 North Broadway, Baltimore, MD, 21205,
Keywords: genomics ; microarrays ; nonparametric ; stochastic
Abstract:

I introduce a class of stochastic linear hypotheses to facilitate high-dimensional comparisons among several groups, based on as few as a single sample per group. This class in particular includes the Mann-Whitney Wilcoxon rank sum test as a special case. I discuss the analytic approach in two stages, within the context of a microarray experiment. In the first part, I estimate a number of candidate genes that characterizes a general comparative criteria by formulating comparisons in terms of stochastic linear hypotheses, which are tested based on developed U-statistic theory. In the second part, I discuss a Bioinformatics algorithm for selecting candidate genes by comparing intensity functionals, using inner product and singular value decomposition concepts, in combination. As motivation, I compare intensities among T-cell clones singly exposed to conditions hypothesized as pathways leading to T-cell clonal anergy, followed by the genomic characterization of such conditions.

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Revised March 2004