Apparently low reproducibility of true differential expression discoveries in microarray studies.

Abstract

MOTIVATION:

Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries.

RESULTS:

Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes. Supplementaty information: Supplementary data are available at Bioinformatics online.

• Authors: Min Zhang, Chen Yao, Zheng Guo, Jinfeng Zou, Lin Zhang, Hui Xiao, Dong Wang, Da Yang, Xue Gong, Jing Zhu, Yanhui Li, Xia Li
• Journal: Bioinformatics (Oxford, England) (Bioinformatics) Vol. 24 Issue 18 Pg. 2057-63 (Sep 15 2008) ISSN: 1367-4811 [Electronic] England
• PMID: 18632747 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Algorithms
• Gene Expression Profiling
• Models, Statistical
• Oligonucleotide Array Sequence Analysis (methods)
• Reproducibility of Results