Multiple testing with the structure‐adaptive Benjamini–Hochberg algorithm

By Ang Li, Rina Foygel Barber. Journal of The Royal Statistical Society Series B-statistical Methodology, 2016

175 citations recorded by the source corpus.

In multiple‐testing problems, where a large number of hypotheses are tested simultaneously, false discovery rate (FDR) control can be achieved with the well‐known Benjamini–Hochberg procedure, which a(0,1]dapts to the amount of signal in the data, under certain distributional assumptions. Many modifications of this procedure have been proposed to improve power in scenarios where the hypotheses are organized into groups or into a hierarchy, as well as other structured settings. Here we introduce the ‘structure‐adaptive Benjamini–Hochberg algorithm’ (SABHA) as a generalization of these adaptive testing methods. The SABHA method incorporates prior information about any predetermined type of structure in the pattern of locations of the signals and nulls within the list of hypotheses, to reweight the p‐values in a data‐adaptive way. This raises the power by making more discoveries in regions where signals appear to be more common. Our main theoretical result proves that the SABHA method controls the FDR at a level that is at most slightly higher than the target FDR level, as long as the adaptive weights are constrained sufficiently so as not to overfit too much to the data—interestingly, the excess FDR can be related to the Rademacher complexity or Gaussian width of the class from which we choose our data‐adaptive weights. We apply this general framework to various structured settings, including ordered, grouped and low total variation structures, and obtain the bounds on the FDR for each specific setting. We also examine the empirical performance of the SABHA method on functional magnetic resonance imaging activity data and on gene–drug response data, as well as on simulated data.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d6e477e034b0edd0ba217f8f83c0703f08cd3871

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Statistics and Probability

Citations in the corpus

Covariate powered cross‐weighted multiple testing (2017, unknown): “For example, the weights in SABHA fulfill Wi = π −1 0 (Xi) (Li and Barber, 2016), while in GBH they fulfill Wi ∝ 1−π0(Xi) π0(Xi) (Hu et al.”
A practical guide to methods controlling false discoveries in computational biology (2018, methodology): “While other modern FDR-controlling methods have also been proposed, methods were excluded if accompanying software was unavailable or if the available software could not be run without substantial work from the user [38].”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, methodology): “In light of Si, the proportion becomes heterogeneous; hence it is desirable to utilize the conditional proportions π1.s/=P.θi =1|Si = s/ to improve the power of existing methods (Zablocki et al., 2014; Scott et al., 2015; Li and Barber, 2016).”