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AAPL: Assessing Association between P-value Lists.
Statistical Analysis and Data Mining ( IF 1.396 ) Pub Date : 2013-04-30 , DOI: 10.1002/sam.11180
Tianwei Yu,Yize Zhao,Shihao Shen

Joint analyses of high-throughput datasets generate the need to assess the association between two long lists of p-values. In such p-value lists, the vast majority of the features are insignificant. Ideally contributions of features that are null in both tests should be minimized. However, by random chance their p-values are uniformly distributed between zero and one, and weak correlations of the p-values may exist due to inherent biases in the high-throughput technology used to generate the multiple datasets. Rank-based agreement test may capture such unwanted effects. Testing contingency tables generated using hard cutoffs may be sensitive to arbitrary threshold choice. We develop a novel method based on feature-level concordance using local false discovery rate. The association score enjoys straight-forward interpretation. The method shows higher statistical power to detect association between p-value lists in simulation. We demonstrate its utility using real data analysis. The R implementation of the method is available at http://userwww.service.emory.edu/~tyu8/AAPL/.
更新日期:2019-11-01

 

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