Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

Improving the power for detecting overlapping genes from multiple DNA microarray-derived gene lists

Xutao Deng12, Jun Xu1 and Charles Wang12*

Author Affiliations

1 Transcriptional Genomics Core, Burns Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

2 Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, USA

For all author emails, please log on.

BMC Bioinformatics 2008, 9(Suppl 6):S14  doi:10.1186/1471-2105-9-S6-S14

Published: 28 May 2008

Abstract

Background

In DNA microarray gene expression profiling studies, a fundamental task is to extract statistically significant genes that meet certain research hypothesis. Currently, Venn diagram is a frequently used method for identifying overlapping genes that meet the investigator's research hypotheses. However this simple operation of intersecting multiple gene lists, known as the Intersection-Union Tests (IUTs), is performed without knowing the incurred changes in Type 1 error rate and can lead to loss of discovery power.

Results

We developed an IUT adjustment procedure, called Relaxed IUT (RIUT), which is proved to be less conservative and more powerful for intersecting independent tests than the traditional Venn diagram approach. The advantage of the RIUT procedure over traditional IUT is demonstrated by empirical Monte-Carlo simulation and two real toxicogenomic gene expression case studies. Notably, the enhanced power of RIUT enables it to identify overlapping gene sets leading to identification of certain known related pathways which were not detected using the traditional IUT method.

Conclusion

We showed that traditional IUT via a Venn diagram is generally conservative, which may lead to loss discovery power in DNA microarray studies. RIUT is proved to be a more powerful alternative for performing IUTs in identifying overlapping genes from multiple gene lists derived from microarray gene expression profiling.