Rahmani Elior, Jew Brandon, Halperin Eran
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States.
Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, United States.
Front Bioinform. 2022 Jan 18;1:792605. doi: 10.3389/fbinf.2021.792605. eCollection 2021.
Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives. We demonstrate that misspecification of the model directionality can lead to a drastic decrease in performance and increase in risk of spurious findings in cell-type-specific differential methylation analysis, and we discuss the need to carefully consider model directionality before choosing a statistical method for analysis.
从组织水平的大量数据中在细胞类型水平上识别差异甲基化是基因组学中的一项基本挑战,该挑战最近受到了更多关注。这些研究通常旨在识别统计关联而非因果效应。然而,现有方法通常对效应方向做出隐含假设,并且到目前为止,几乎没有关注到这一方向性假设可能不成立,进而可能影响统计效力以及对假阳性的控制这一事实。我们证明,在细胞类型特异性差异甲基化分析中,模型方向性的错误设定会导致性能急剧下降以及出现虚假结果的风险增加,并且我们讨论了在选择统计分析方法之前仔细考虑模型方向性的必要性。