最佳协变量加权增加了高通量生物学的发现
Optimal Covariate Weighting Increases Discoveries in High-throughput Biology
摘要：
高通量生物数据固有的大规模多重检验需要非常高的统计严格性，因此数据中的真实效应难以检测，除非它们具有高效应量。减少多重检验负担的一个有希望的方法是使用独立的信息来优先考虑最有可能是真实效果的特征。然而，有效地使用独立数据是具有挑战性的，并且通常不会导致功率的实质性增益。现有的方法都是根据特征的独立信息对特征进行分组，并计算每个分组的权重。然而，当真实效应较弱且罕见时（高通量生物学研究的典型情况），所有组将包含许多空测试，因此它们的权重被稀释，性能受到影响。我们介绍协变量秩加权（CRW），用于计算近似的最佳权重的条件下，由外部协变量的测试排名的方法。该方法使用协变量排序和检验效应大小之间的概率关系来计算每个检验的个体权重，这些权重比组权重信息量更大，并且不会被零效应稀释。我们展示了如何从理论上计算这种关系的正态分布的协变量。在其他情况下，可以根据经验估计。我们通过模拟和数据应用表明，这种方法在生物数据常见的罕见/低效应大小场景中的性能比现有方法高出10倍，并且在所有场景中至少具有相当的性能。
英文摘要：
The large-scale multiple testing inherent to high throughput biological data necessitates very high statistical stringency and thus true effects in data are difficult to detect unless they have high effect sizes. One promising approach for reducing the multiple testing burden is to use independent information to prioritize the features most likely to be true effects. However, using the independent data effectively is challenging and often does not lead to substantial gains in power. Current state-of-the-art methods sort features into groups by the independent information and calculate weights for each group. However, when true effects are weak and rare (the typical situation for high throughput biological studies), all groups will contain many null tests and thus their weights are diluted, and performance suffers. We introduce Covariate Rank Weighting (CRW), a method for calculating approximate optimal weights conditioned on the ranking of tests by an external covariate. This approach uses the probabilistic relationship between covariate ranking and test effect size to calculate individual weights for each test that are more informative than group weights and are not diluted by null effects. We show how this relationship can be calculated theoretically for normally distributed covariates. It can be estimated empirically in other cases. We show via simulations and applications to data that this method outperforms existing methods by as much as 10-fold in the rare/low effect size scenario common to biological data and has at least comparable performance in all scenarios.
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