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文章以简单的篇幅介绍了multiple test中的校正策略：1）Bonferroni, P-values； 2）FDR；3）q-values。 计算p-value的策略：（1）通过随机模拟得到Empirical distribution，基于此计算p-value；（2）估计参数获得理论分布，然后计算对应值的p-value。 由于“This is because the P-value is only statistically valid when a single score is computed.”，为此对于mutliple test，需要进行校正。校正的策略有： （1）Bonferroni adjustment：其作用在于“Practically speaking, this means that, given a set of CTCF sites with a Bonferroni adjusted significance threshold of a=0.01, we can be 99% sure that none of the scores would be observed by chance when drawn according to the null hypothesis.” （2）Rather than saying that we want to be 99% sure that none of the observed scores is drawn accoding to the null, it is frequently sufficient to identify a set of scores for which a specified percentage of scores are drawn according to the null.This is the basis of multiple testing correction using FDR estimation.FDR的产生，FDR的计算有多种不同的方式。如果是empirical null distribution，则利用Snul/Sobs计算；如果是analytical null model，利用Benjamini-Hochberg procedure计算P-value，还有其他的计算方法。 （3）q-value的出现是为了解决FDR本质的问题：The q-value is motivated, in part, by a somewhat unfortunate mathematical property of the FDR: when considering a ranked list of scores, it is possible for the FDR associated with the first m scores to be higher than the FDR associated with the first m+1 scores. |
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