Sequential Monte Carlo multiple testing
Sequential Monte Carlo multiple testing
复制标题
DOI：
10.1093/bioinformatics/btr568
复制
发表时间：
2011-12-01
期刊：
影响因子：
5.8
通讯作者：
Nygard, Stale
中科院分区：
文献类型：
--
作者：
Sandve, Geir Kjetil;Ferkingstad, Egil;Nygard, Stale
动机：与许多其他科学领域一样，在分子生物学中，分析的规模也在不断扩大。通常，需要复杂的蒙特卡罗模拟，有时在大规模的多个测试设置中。由此产生的计算成本可能高得令人望而却步。结果：本文提出了一种简单、新颖的伪发现率（FDR）调制序列蒙特卡罗（MC）多重假设检验算法。该算法在跨测试添加MC样本和计算测试集合的中间FDR值之间迭代。通过顺序MC或基于FDR上的阈值来停止MC采样。该算法的一个基本性质是，无论真实零假设的数量如何，它都限制了MC样本的总数。我们在真实和模拟数据上都表明，所提出的算法在计算效率方面有很大的提高。
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.