Abstract #300557

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JSM 2003 Abstract #300557
Activity Number: 283
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #300557
Title: Statistical Outlier Detection for Microarray Data
Author(s): Tzu-Ming Chu*+ and Russell D. Wolfinger
Companies: SAS Institute, Inc. and SAS Institute, Inc.
Address: R3214, Cary, NC, 27513,
Keywords: microarray ; GeneChip ; outlier ; probe ; mixed model
Abstract:

Outliers are common in microarray data and determining what to do with them is often a critical step towards obtaining optimal inferences. We discuss both graphical and statistical modeling approaches to the problem. For the latter, we employ linear mixed models in two ways. The first is to consider various types of large residuals. The second we call Forward Outlier Detection (FOD), in which we use covariates to adjust for the largest residual at each step. FOD includes formal statistical tests for outliers and provides a more rigorous way to evaluate their significance. Data from an ionizing radiation experiment on human cell lines illustrates the key concepts.

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