Evaluation of Nine Strategies for Analyzing a cDNA Toxicology Microarray Data Set

ABSTRACT

Microarray technology with two-color–based cDNA is commonly used for drug development, as well as for a much broader range of biomedical research. Among all the applications, two-group design is probably most commonly used for comparing, e.g., normal and abnormal tissue samples, tissues treated and untreated, or individuals responded and not responded to a drug. Despite the apparent simplicity, there are numerous methods for analyzing such data in a statistically rigorous manner. Here, we discuss nine different analytical strategies, each of which is derived under a set of “reasonable” assumptions. Some of them resemble methods developed for different contexts. In the absence of the truth, investigators should consider underlying assumptions before taking one or more of these strategies for analyzing data from a particular experiment. The issue here is what are the similarities and differences between these analytical strategies. We present these strategies in the context of an actual microarray experiment performed at the U.S. Food and Drug Administration.

Key Words:

• cDNA microarray
• False discovery rate (FDR)
• One- and two-group comparison
• Panel data analysis
• Replicates
• t-Tests,

Mathematics Subject Classification:

• aaa
• bbb

ACKNOWLEDGMENTS

The authors want to thank Dr. Karol Thompson at CDER who made her microarray data available to this work, Dr. Whipple Neely for many helpful discussions in the earlier phase of this work, and the referees for their constructive comments. This work is supported in part by FDA OSHC2003-02, FDA CDER RSR-04-18, and NIH RO1CA106320.