蝙蝠侠:通过分层来缓解批量效应以预测生存结果
Ai Ni1, Mengling Liu2, Li-Xuan Qin3
1Division of Biostatistics, College of Public Health, Ohio State University, Columbus, OH.
JCO clinical cancer informatics
|June 19, 2023
概括
转录学数据中的批量效应阻碍了可复制的生存预测. 一种名为BatMan (BATch Mitigation via stratification) 的新方法,性能优于ComBat,并建议对生存模型的数据规范化保持谨慎.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 批量效应是转录学数据中的一个重大挑战,影响了分析的可重复性,特别是在生存结果预测中.
- 现有的方法,如ComBat,根据样本组比较进行调整,在应用到生存预测时,由于顺序应用和缺乏定义的群体,具有局限性.
- 对于可靠的生物见解来说,需要强大的方法来减轻高维生存预测中的批量效应,这是至关重要的.
研究的目的:
- 引入和评估BatMan (BATch MitigationAction via stratificatioN),这是一个新的统计方法,用于解决生存预测中的批量效应.
- 在各种模拟场景下,将BatMan的性能与已建立的ComBat方法进行比较,有或没有数据规范化.
- 为生存模型提供有关适当使用数据规范化与批量效应校正方法相结合的指导.
主要方法:
- 蝙蝠侠通过将它们纳入生存回归框架中的层次来调整批量效应.
- 该方法采用可变选择技术,如正规化回归,以管理高维转录组学数据.
- 通过基于重新抽样的模拟研究和对来自癌症基因组图谱的卵巢癌微RNA数据的评估来评估性能.
主要成果:
- 与ComBat相比,BatMan在几乎所有带有批量效果的模拟场景中表现出了优越的性能.
- 添加数据规范化通常会使BatMan和ComBat在生存预测任务中的表现恶化.
- 对卵巢癌数据的分析证实了蝙蝠侠的优势,正常化对预测准确性产生了负面影响.
结论:
- 蝙蝠侠在生存预测中提供了一种比ComBat.Bat更有效的方法来减轻批量效应.
- 该研究强调了应用数据规范化与批量效应校正一起用于预测生存结果的潜在缺点.
- 蝙蝠侠方法和相关的模拟工具在R中公开提供,以便更广泛地应用和验证.
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