This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 125
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #308324
Title: Massively Parallel Linear Modeling of Gene Expression Data
Author(s): Walter Liggett*+
Companies: National Institute of Standards and Technology
Address: 100 Bureau Drive, Stop 8980, Gaithersburg, MD, 20899-8980,
Keywords: microarray ; F test ; preprocessing ; probe set summarization ; local false discovery rate ; gene list
Abstract:

Gene-by-gene fitting of a common regression model to microarray responses requires choice of methods for preprocessing (normalization, background correction, and probe set summarization) and for computing local false discovery rates. Our normalization retains the common applicability of the model. Preprocessing alternatives include weighted mean summarization and RMA-type summarization. Lack of linearity with mRNA concentration can be tested gene-by-gene resulting in parallel F tests interpretable through local false discovery rates. Our data consist of 12 microarray measurements on each of six animals. Animal to animal, local false discovery rates based on the empirical null distribution chosen give gene lists with minor disagreements. The different preprocessing methods give gene lists with disagreements that seem understandable in terms of the treatment of small probe responses.


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