Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA.
Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA.
Epigenomics. 2024;16(15-16):1067-1080. doi: 10.1080/17501911.2024.2379242. Epub 2024 Aug 2.
DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample -test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.
DNA 甲基化(DNAm)- 基于去卷积的估计包含相对数据,形成一种组成,而标准方法(直接在细胞比例上进行测试)不适合处理。在这项研究中,我们研究了替代方法,即微生物组组成分析(ANCOM),用于分析 DNAm 基于去卷积的估计。我们进行了两项不同的模拟研究,将 ANCOM 与标准方法(直接在细胞比例上进行的双样本检验)进行比较,并分析了妇女健康倡议的真实世界数据,以评估 ANCOM 对 DNAm 基于去卷积的估计的适用性。我们的研究结果表明,ANCOM 可以有效地解释 DNAm 基于去卷积的估计的组成性质。ANCOM 可以充分控制假发现率,同时保持与标准方法相当的统计功效。