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Updated: Jan 13, 2026

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基于单细胞树的基因组疾病关联模型
bioRxiv : the preprint server for biology
|January 9, 2026
概括
我们开发了scanCT,这是一个基于树的新型框架,用于分析单细胞多omics数据. 该方法识别了与不同COVID-19临床表型相关的协同基因和蛋白质标记物组合.
科学领域:
- 单细胞多组体的单细胞多组体
- 发现生物标志物的发现.
- 计算生物学是一种计算生物学.
背景情况:
- 单细胞多组技术为疾病映射和生物标志物识别提供了高分辨率.
- 目前的方法经常忽视基因组特征的组合相互作用,使表型分析复杂化.
- 识别复杂的相互作用对于理解临床结果至关重要.
研究的目的:
- 介绍scanCT,一种基于树的框架,用于可解释地识别与疾病表型相关的基因组特征组.
- 为了捕捉特征相互作用,并在单细胞数据中调整混因素.
- 将scanCT应用于COVID-19多omics数据以发现生物标志物.
主要方法:
- 在数据驱动的分割选择中,scanCT使用基于树的框架,对数据驱动的分割选择进行无偏见的基于模型的变量选择.
- 该架构捕捉了特征交互效应,使得组合生物标志物模式的分析成为可能.
- 协会建模调整了诸如年龄和性别等混因素.
主要成果:
- 扫描CT应用于纵向单细胞多组COVID-19数据集.
- 该框架确定了表型特定的基因和蛋白质标记物.
- 揭示了可解释的协同标记组合,解释了临床表型变异.
结论:
- scanCT提供了一种强大且可解释的方法来分析单细胞多组数据.
- 该框架有效地识别了驱动疾病表型的复杂生物标志物相互作用.
- 这种方法促进了对COVID-19等复杂疾病的生物标志物发现.
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