Sequential Monte Carlo multiple testing. Motivation: In molecular biology, as in many other scientific fields, the scale of analyses is ever increasing. Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting. The resulting computational costs may be prohibitively high. Results: We here present MCFDR, a simple, novel algorithm for false discovery rate (FDR) modulated sequential Monte Carlo (MC) multiple hypothesis testing. The algorithm iterates between adding MC samples across tests and calculating intermediate FDR values for the collection of tests. MC sampling is stopped either by sequential MC or based on a threshold on FDR. An essential property of the algorithm is that it limits the total number of MC samples whatever the number of true null hypotheses. We show on both real and simulated data that the proposed algorithm provides large gains in computational efficiency. Availability: MCFDR is implemented in the Genomic HyperBrowser (http://hyperbrowser.uio.no/mcfdr), a web-based system for genome analysis. All input data and results are available and can be reproduced through a Galaxy Pages document at: http://hyperbrowser.uio.no/mcfdr/u/sandve/p/mcfdr.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Hahn, Georg: On the expected runtime of multiple testing algorithms with bounded error (2020)
- Hahn, Georg: Optimal allocation of Monte Carlo simulations to multiple hypothesis tests (2020)
- Hahn, Georg: Closure properties of classes of multiple testing procedures (2018)
- Gandy, Axel; Hahn, Georg: QuickMMCTest: quick multiple Monte Carlo testing (2017)
- Gandy, Axel; Hahn, Georg: A framework for Monte Carlo based multiple testing (2016)
- Ferkingstad, Egil; Holden, Lars; Sandve, Geir Kjetil: Monte Carlo null models for genomic data (2015)
- Gandy, Axel; Hahn, Georg: MMCTest -- a safe algorithm for implementing multiple Monte Carlo tests (2014)