考克斯MDS:在全表观基因组研究中对生存结果进行高维介导分析的多重数据分割
Minhao Yao1, Peixin Tian2, Xihao Li3,4
1Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore.
Briefings in bioinformatics
|January 15, 2026
概括
考克斯MDS是一种用于因果调解分析的新方法,可靠地控制DNA甲基化研究中的错误发现率 (FDR). 它增强了识别生存结果的遗传媒介的统计能力,即使使用复杂的数据.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 统计基因组学 统计基因组学
- 流行病学 流行病学
背景情况:
- 因果调解分析识别了暴露与结果关系中的中间变量 (调解者).
- 现有的高维介导方法在有限样本中与FDR控制作斗争,特别是在DNA甲基化研究中常见的相关或非高斯数据.
- 可靠地识别因果媒介对于理解疾病机制和制定有针对性的干预措施至关重要.
研究的目的:
- 介绍CoxMDS,一种新的多重数据分割方法,用于高维设置中的因果调解分析.
- 确保有限样本错误发现率 (FDR) 的控制,特别是对于生存结果分析中的相关或非高斯介质.
- 与现有方法相比,提高识别因果调解者的统计能力.
主要方法:
- 开发了CoxMDS,一种使用Cox比例危险模型的多重数据分割方法.
- 将CoxMDS应用于模拟数据集,以评估其在保持FDR控制和功率方面的表现.
- 利用CoxMDS对来自癌症基因组图谱 (TCGA) 和阿尔茨海默氏症神经成像计划 (ADNI) 的DNA甲基化数据进行因果调解分析.
主要成果:
- 模拟表明,CoxMDS在有限的样本中有效地控制FDR,并且在统计能力上优于现有的方法.
- 考克斯MDS在TCGA数据中确定了八个CpG位点,表明DNA甲基化调解了吸烟对肺癌存活率的影响.
- 在ADNI数据中确定了两个CpG位点,表明DNA甲基化可能会调节吸烟对阿尔茨海默病转化时间的影响.
结论:
- 考克斯MDS为因果调解分析提供了强大的解决方案,使用高维,复杂的数据,确保可靠的FDR控制.
- 该方法成功地确定了吸烟对肺癌和阿尔茨海默病存活率的影响的潜在DNA甲基化媒介.
- 科克斯MDS通过提供一个强大的工具来剖析复杂的生物途径,推动了遗传流行病学领域的发展.
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