论文标题
高通量筛选的无模型标志估计
Model-free Sign Estimation for High-Throughput Screenings
论文作者
论文摘要
在高通量筛选中，通常使用少量独立试验来估计许多处理的影响。由于对这些试验测量的分布特性知之甚少，因此确定可以作为在这种情况下可以作为推理统计基础的合理假设是一项挑战。在本文中，我们开发了一种基于最小假设的方法来推断治疗效果的迹象（正或负面）。提出的方法通过使用同一处理的测量值之间的符号分歧数来控制误估计的符号的数量，作为符号误差数量的代理。在模拟中，提出的方法与应用于无效的$ p $值的Benjamini-Hochberg程序相比，目前被认为是许多高通量筛选的最佳实践。对于来自L1000单元格平台的真实数据，所提出的方法优于现有实践，在某些情况下，这些方法在标称级别无法控制误差，并且在其他情况下是不必要的保守性。
In high-throughput screenings, it is common to estimate the effects of many treatments using a small number of independent trials of each. Because little is known about the distributional properties of the measurements from these trials, it is challenging to identify plausible assumptions that can serve as a basis for inferential statistics in this setting. In this article, we develop a method based on minimal assumptions to infer signs of treatment effects (positive or negative). The proposed method controls the number of misestimated signs by using the number of sign disagreements between measurements of the same treatment as a proxy for the number of sign errors. In simulations, the proposed method compares favorably with the Benjamini-Hochberg procedure applied to invalid $p$-values, which is currently considered best practice for many high-throughput screenings. For real data from the L1000 cell-perturbation platform, the proposed method outperforms existing practices, which fail to control error at the nominal level in some cases and are needlessly conservative in others.
