Effect of adding fold-change criteria to significance testing of microarray data

Abstract

Microarray experiments involve simultaneous testing of thousands of genes, thus leading to a large number of false-significant results. To combat this, a wide range of multiple-testing procedures have been developed to reduce false significance rates and related errors. In contrast, clinical investigators may choose alternative routes having intuitive appeal but less optimal statistical properties, such as requiring a 2-fold change. While the corresponding limitations are commonly recognized, the impact of such approaches has not been specifically characterized for practical scenarios. This study investigates the effect of combining the t-test (with the Benjamini–Hochberg adjustment) and 2-fold-change cut-off on the resulting levels of significance and power. Findings illustrate that both significance and power are often dominated by the 2-fold criteria, essentially negating the properties of the multiple comparisons adjustment. Other cases lead to more complex and potentially counter-intuitive results.

Keywords:

• microarray
• multiple comparisons
• fold change

AMS Classification :

• 62-07
• 62F03
• 62P10

Acknowledgements

The authors would like to thank the reviewers of this manuscript for their important corrections and additions. We would also like to acknowledge the McAnulty College of Arts and Sciences at Duquesne University for funding the undergraduate research small grant (for the period of January 2007 to April 2007) used to generate the simulation results.