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Issue No.05 - September/October (2011 vol.8)
pp: 1431-1437
Ehud Aharoni , IBM Research Laboratory, Haifa
Hani Neuvirth , IBM Research Laboratory, Haifa
Saharon Rosset , Tel Aviv University, Tel Aviv
The common scenario in computational biology in which a community of researchers conduct multiple statistical tests on one shared database gives rise to the multiple hypothesis testing problem. Conventional procedures for solving this problem control the probability of false discovery by sacrificing some of the power of the tests. We suggest a scheme for controlling false discovery without any power loss by adding new samples for each use of the database and charging the user with the expenses. The crux of the scheme is a carefully crafted pricing system that fairly prices different user requests based on their demands while keeping the probability of false discovery bounded. We demonstrate this idea in the context of HIV treatment research, where multiple researchers conduct tests on a repository of HIV samples.
Family-wise error rate, multiple comparisons, Bonferroni method.
Ehud Aharoni, Hani Neuvirth, Saharon Rosset, "The Quality Preserving Database: A Computational Framework for Encouraging Collaboration, Enhancing Power and Controlling False Discovery", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 5, pp. 1431-1437, September/October 2011, doi:10.1109/TCBB.2010.105
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