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

@article{citeulike:3035921, 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. CONTACT: guoz@ems.hrbmu.edu.cn, lixia@ems.hrbmu.edu.cn.}, address = {1 School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.}, author = {Zhang, Min and Yao, Chen and Guo, Zheng and Zou, Jinfeng and Zhang, Lin and Xiao, Hui and Wang, Dong and Yang, Da and Gong, Xue and Zhu, Jing and Li, Yanhui and Li, Xia }, citeulike-article-id = {3035921}, doi = {http://dx.doi.org/10.1093/bioinformatics/btn365}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {fdr}, month = {July}, posted-at = {2008-07-23 07:13:55}, priority = {5}, title = {Apparently low reproducibility of true differential expression discoveries in microarray studies.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn365}, year = {2008} }

TY - JOUR ID - citeulike:3035921 L3 - citeulike-article-id:3035921 TI - Apparently low reproducibility of true differential expression discoveries in microarray studies. JF - Bioinformatics (Oxford, England) AD - 1 School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China. SN - 1460-2059 N2 - 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. CONTACT: guoz@ems.hrbmu.edu.cn, lixia@ems.hrbmu.edu.cn. KW - fdr AU - Zhang, Min AU - Yao, Chen AU - Guo, Zheng AU - Zou, Jinfeng AU - Zhang, Lin AU - Xiao, Hui AU - Wang, Dong AU - Yang, Da AU - Gong, Xue AU - Zhu, Jing AU - Li, Yanhui AU - Li, Xia PY - 2008/07/16/ UR - http://dx.doi.org/10.1093/bioinformatics/btn365 ER -