False discovery proportion estimation by permutations: confidence for significance analysis of microarrays
False discovery proportion estimation by permutations: confidence for significance analysis of microarrays
复制标题
DOI：
10.1111/rssb.12238
复制
发表时间：
2018-01-01
影响因子：
5.8
通讯作者：
Goeman, Jelle J.
中科院分区：
文献类型：
--
作者：
Hemerik, Jesse;Goeman, Jelle J.
微阵列显著性分析（SAM）是一种非常流行的基于排列的多重检验方法，用于估计假发现比例（FDP）：所有被拒绝的假设中假阳性结果的比例。也许令人惊讶的是，到目前为止，这种方法还没有已知的特性。本文扩展SAM提供1-上置信界的FDP，使确切的信心声明可以。作为一种特殊情况，得到的估计FDP低估FDP的概率最多为0.5。此外，使用一个封闭的测试过程中，本文降低了上限和估计，在这样的方式，保持了置信水平。我们的方法基于随机排列的精确测试的一般结果。
Significance analysis of microarrays (SAM) is a highly popular permutation-based multiple-testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1- upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates the FDP with probability at most 0.5. Moreover, using a closed testing procedure, this paper decreases the upper bounds and estimates in such a way that the confidence level is maintained. We base our methods on a general result on exact testing with random permutations.