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AAPL: Assessing Association between P-value Lists.


ABSTRACT: 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/.

SUBMITTER: Yu T 

PROVIDER: S-EPMC3634673 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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AAPL: Assessing Association between P-value Lists.

Yu Tianwei T   Zhao Yize Y   Shen Shihao S  

Statistical analysis and data mining 20130401 2


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 mul  ...[more]

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