The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Leming Shi, Gregory Campbell, Wendell D. Jones, Fabien Campagne, Zhining Wen, Stephen J. Walker, Zhenqiang Su, Tzu Ming Chu, Federico M. Goodsaid, Lajos Pusztai, John D. Shaughnessy, André Oberthuer, Russell S. Thomas, Richard S. Paules, Mark Fielden, Bart Barlogie, Weijie Chen, Pan Du, Matthias Fischer, Cesare FurlanelloBrandon D. Gallas, Xijin Ge, Dalila B. Megherbi, W. Fraser Symmans, May D. Wang, John Zhang, Hans Bitter, Benedikt Brors, Pierre R. Bushel, Max Bylesjo, Minjun Chen, Jie Cheng, Jing Cheng, Jeff Chou, Timothy S. Davison, Mauro Delorenzi, Youping Deng, Viswanath Devanarayan, David J. Dix, Joaquin Dopazo, Kevin C. Dorff, Fathi Elloumi, Jianqing Fan, Shicai Fan, Xiaohui Fan, Hong Fang, Nina Gonzaludo, Kenneth R. Hess, Huixiao Hong, Jun Huan, Rafael A. Irizarry, Richard Judson, Dilafruz Juraeva, Samir Lababidi, Christophe G. Lambert, Li Li, Yanen Li, Zhen Li, Simon M. Lin, Guozhen Liu, Edward K. Lobenhofer, Jun Luo, Wen Luo, Matthew N. McCall, Yuri Nikolsky, Gene A. Pennello, Roger G. Perkins, Reena Philip, Vlad Popovici, Nathan D. Price, Feng Qian, Andreas Scherer, Tieliu Shi, Weiwei Shi, Jaeyun Sung, Danielle Thierry-Mieg, Jean Thierry-Mieg, Venkata Thodima, Johan Trygg, Lakshmi Vishnuvajjala, Sue Jane Wang, Jianping Wu, Yichao Wu, Qian Xie, Waleed A. Yousef, Liang Zhang, Xuegong Zhang, Sheng Zhong, Yiming Zhou, Sheng Zhu, Dhivya Arasappan, Wenjun Bao, Anne Bergstrom Lucas, Frank Berthold, Richard J. Brennan, Andreas Buness, Jennifer G. Catalano, Chang Chang, Rong Chen, Yiyu Cheng, Jian Cui, Wendy Czika, Francesca Demichelis, Xutao Deng, Damir Dosymbekov, Roland Eils, Yang Feng, Jennifer Fostel, Stephanie Fulmer-Smentek, James C. Fuscoe, Laurent Gatto, Weigong Ge, Darlene R. Goldstein, Li Guo, Donald N. Halbert, Jing Han, Stephen C. Harris, Christos Hatzis, Damir Herman, Jianping Huang, Roderick V. Jensen, Rui Jiang, Charles D. Johnson, Giuseppe Jurman, Yvonne Kahlert, Sadik A. Khuder, Matthias Kohl, Jianying Li, Li Lee, Menglong Li, Quan Zhen Li, Shao Li, Zhiguang Li, Jie Liu, Ying Liu, Zhichao Liu, Lu Meng, Manuel Madera, Francisco Martinez-Murillo, Ignacio Medina, Joseph Meehan, Kelci Miclaus, Richard A. Moffitt, David Montaner, Piali Mukherjee, George J. Mulligan, Padraic Neville, Tatiana Nikolskaya, Baitang Ning, Grier P. Page, Joel Parker, R. Mitchell Parry, Xuejun Peng, Ron L. Peterson, John H. Phan, Brian Quanz, Yi Ren, Samantha Riccadonna, Alan H. Roter, Frank W. Samuelson, Martin M. Schumacher, Joseph D. Shambaugh, Qiang Shi, Richard Shippy, Shengzhu Si, Aaron Smalter, Christos Sotiriou, Mat Soukup, Frank Staedtler, Guido Steiner, Todd H. Stokes, Qinglan Sun, Pei Yi Tan, Rong Tang, Zivana Tezak, Brett Thorn, Marina Tsyganova, Yaron Turpaz, Silvia C. Vega, Roberto Visintainer, Juergen Von Frese, Charles Wang, Eric Wang, Junwei Wang, Wei Wang, Frank Westermann, James C. Willey, Matthew Woods, Shujian Wu, Nianqing Xiao, Joshua Xu, Lei Xu, Lun Yang, Xiao Zeng, Jialu Zhang, Li Zheng, Min Zhang, Chen Zhao, Raj K. Puri, Uwe Scherf, Weida Tong, Russell D. Wolfinger

Research output: Contribution to journalArticle

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Original languageEnglish (US)
Pages (from-to)827-838
Number of pages12
JournalNature Biotechnology
Volume28
Issue number8
DOIs
StatePublished - Aug 2010

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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  • Cite this

    Shi, L., Campbell, G., Jones, W. D., Campagne, F., Wen, Z., Walker, S. J., Su, Z., Chu, T. M., Goodsaid, F. M., Pusztai, L., Shaughnessy, J. D., Oberthuer, A., Thomas, R. S., Paules, R. S., Fielden, M., Barlogie, B., Chen, W., Du, P., Fischer, M., ... Wolfinger, R. D. (2010). The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28(8), 827-838. https://doi.org/10.1038/nbt.1665