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针对样本分配进行反集群,以尽量减少批量效应
Martin Papenberg1, Cheng Wang2, Maïgane Diop3
1Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Cell reports methods
|August 19, 2025
概括
反集群是一种新的自动化方法,用于在高吞吐量测序中将样本分配到平衡批次中,最大限度地减少批次效应并提高数据可靠性. 这种方法确保了更好的生物信号检测,并支持特定的用户限制,以实现准确的实验设计.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 高通量测序产生大量数据集,但易受批量效应的影响,可能会混生物信号.
- 批量效应源于样品处理过程中的系统变化,可能导致不准确的实验结论.
研究的目的:
- 引入反集群作为创建平衡实验批次的自动化方法.
- 为了最大限度地减少共同变量失衡,并解决用户定义的约束在批次分配的高吞吐量测序.
主要方法:
- 反集群算法,特别是双相Must-Link (2PML) 变体,用于样本到批量分配.
- 进行了模拟,以与现有方法对抗集群性能进行比较.
- 来自UCSF-斯坦福ENACT中心的现实世界案例研究被用来展示实际应用.
主要成果:
- 与模拟中的现有方法相比,反集群在生成平衡批次方面表现优越.
- 2PML算法成功地结合了"必须链接"约束,平衡了疾病阶段和月经周期阶段等关键共变量.
- 该方法使用来自ENACT中心的样本进行了验证,其中个人内样本分批是至关重要的.
结论:
- 反集群提供了一种有效的自动化解决方案,用于在高吞吐量测序中平衡批次分配.
- 该 anticlust R 包和 RShiny 应用程序提供了可访问的工具来实现和可视化这些方法.
- 这种方法通过减轻混批量效应来提高生物信号检测的可靠性.
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