title: Sequential Monte Carlo multiple testing.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  geirksa@ifi.uio.no.
authors: Department of Informatics, University of Oslo, Oslo, Norway. geirksa@ifi.uio.no
authors: Geir Kjetil
authors: GK
authors: Sandve
authors: Egil
authors: E
authors: Ferkingstad
authors: Ståle
authors: S
authors: Nygård
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D042421:Histone Code; D009010:Monte Carlo Method