Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, United States.
Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States.
Bioinformatics. 2024 Feb 1;40(2). doi: 10.1093/bioinformatics/btae035.
Emerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss.
We propose a new 2D Group Benjamini-Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure.
2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper.
新兴的组学技术在多变量检验中引入了双向分组结构，如单细胞组学数据中，特征可以按基因或细胞类型进行分组。传统的多变量检验方法在利用这种双向分组结构方面能力有限，导致潜在的功效损失。
我们提出了一种新的二维组 Benjamini-Hochberg（2dGBH）程序，以利用组学数据中的双向分组结构，扩展了传统的单向自适应 GBH 程序。使用模拟和真实数据集，我们表明 2dGBH 在生物学相关的环境中有效地控制了假发现率，它比 BH 或 q 值程序更强大，比单向自适应 GBH 程序更稳健。
2dGBH 可在以下网址作为 R 包使用：https://github.com/chloelulu/tdGBH。分析代码和数据可在以下网址获得：https://github.com/chloelulu/tdGBH-paper。
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