Abstract #300033

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JSM 2003 Abstract #300033
Activity Number: 95
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300033
Title: Identifying Genes Altered by a Drug in Temporal Microarray Data: A Case Study
Author(s): Nicoleta Serban*+ and Larry A. Wasserman
Companies: Carnegie Mellon University and Carnegie Mellon University
Address: 815 Mifflin Ave., Pittsburgh, PA, 15221,
Keywords: Risk estimation and adaptation after coordinate transformation ; runs test ; microarray ; poly(dT) ; Expressed sequence tag ; False Discovery Rate
Abstract:

An important application of microarray techniques is identifying genes altered by a particular drug of interest. This process allows biologists to target drug therapies to particular diseases, and, eventually, to gain more knowledge about the biological processes responsible for diseases. We analyze data from a microarray experiment focusing on the effect of a drug, once prescribed for diabetes, which is a genetically heterogeneous diseases. Our case study moves beyond the simple rules" of previously published studies by tailoring our analysis to the data in hand. We identify significant systematic sources of variability which are potential issues for other microarray datasets. Subsequently, we apply two novel nonparametric multiple hypothesis tests to identify differentially expressed genes and we find a set of genes which appear to change in expression level over time in response to the drug treatment. Finally, we address the problem of identification of co-expressed genes among the ones obtained from multiple hypothesis testing using a novel cluster analysis based on nonparametric regression procedure, REACT. We evaluate our methodologies on a complex synthetic dataset.

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