在多个计数表数据中特征相互作用的稀有贝叶斯群因子模型.
Shuangjie Zhang1, Yuning Shen2, Irene A Chen2
1Department of Statistics, University of California Santa Cruz.
Journal of the American Statistical Association
|July 18, 2025
概括
这项研究引入了微生物群计数数据的稀疏贝叶斯群因子模型,有效地捕捉了不同领域的微生物相互作用. 该模型通过使用关节稀疏性和灵活建模来增强复杂,高维的微生物组数据的分析.
科学领域:
- 微生物组研究的研究.
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 微生物组研究产生复杂的,高维数计数数据跨多个领域.
- 现有的模型很难捕捉这些数据集中的微生物之间的复杂相互作用.
- 下一代测序数据带来了挑战,因为它具有很大的可变性和多余的零.
研究的目的:
- 开发一种新的稀疏贝叶斯群因子模型 (Sp-BGFM) 用于分析多个微生物群计数表.
- 为了有效地捕捉域间微生物相互作用.
- 为了纳入对微生物丰度的共变效应.
主要方法:
- 开发了一种稀疏贝叶斯群因子模型 (Sp-BGFM) 使用圆核混合模型与迪里克莱特过程 (DP) 之前.
- 采用了计数向量的日志-正常混合核心和协差矩阵的组系模型.
- 引入了因子负载载向量的关节稀疏性之前的迪里克莱特-马 (Dir-HS) 收缩,并纳入了对共变量效应的回归.
主要成果:
- 在高维度应用中,Sp-BGFM表现出卓越的性能,这是由于先前的Dir-HS引发的关节稀疏性.
- 灵活的DP模型有效地处理了观察到的数量中的大变化和多余的零.
- 实现了对微生物相互作用和共变效应的可靠估计.
结论:
- 关节稀疏性对于准确分析高维微生物群数据至关重要.
- 拟议的Sp-BGFM为微生物群计数数据分析提供了一个灵活而强大的框架.
- 该模型为了解微生物社区结构和相互作用提供了显著的好处.
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