Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
IMPACT FACTOR increased in 2015: 1.265
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Rank 42 out of 123 in category Statistics & Probability in the 2015 Thomson Reuters Journal Citation Report/Science Edition
SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554
Impact per Publication (IPP) 2015: 1.061
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The Joint Null Criterion for Multiple Hypothesis Tests
- Johns Hopkins Bloomberg School of Public Health
- Princeton University
Simultaneously performing many hypothesis tests is a problem commonly encountered in high-dimensional biology. In this setting, a large set of p-values is calculated from many related features measured simultaneously. Classical statistics provides a criterion for defining what a “correct” p-value is when performing a single hypothesis test. We show here that even when each p-value is marginally correct under this single hypothesis criterion, it may be the case that the joint behavior of the entire set of p-values is problematic. On the other hand, there are cases where each p-value is marginally incorrect, yet the joint distribution of the set of p-values is satisfactory. Here, we propose a criterion defining a well behaved set of simultaneously calculated p-values that provides precise control of common error rates and we introduce diagnostic procedures for assessing whether the criterion is satisfied with simulations. Multiple testing p-values that satisfy our new criterion avoid potentially large study specific errors, but also satisfy the usual assumptions for strong control of false discovery rates and family-wise error rates. We utilize the new criterion and proposed diagnostics to investigate two common issues in high-dimensional multiple testing for genomics: dependent multiple hypothesis tests and pooled versus test-specific null distributions.