这篇文章介绍了一种名为多分辨率变分推断 (MrVI) 的深度生成模型，旨在处理大规模单细胞基因组学研究中的样本异质性。MrVI 通过解决样本分层和评估组间细胞分子差异这两个核心问题，超越了传统分析中简单地对细胞信息求平均的方法。该模型通过利用反事实分析和分层潜在变量来整合来自多个样本的数据，同时区分感兴趣的协变量和干扰因素。文章随后展示了MrVI在分析COVID-19队列、药物扰动筛选以及克罗恩病（Crohn’s disease） 研究中的应用，证明了其在识别临床相关细胞亚群和发现新生物学见解方面的有效性。References: Boyeau P, Hong J, Gayoso A, et al. Deep generative modeling of sample-level heterogeneity in single-cell genomics[J]. Nature Methods, 2025: 1-11.
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