PopPert：面向单细胞扰动预测的种群级联合分布建模
PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction
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中文总结 AI 辅助
本研究针对非配对单细胞扰动数据，提出PopPert框架，通过种群级联合分布建模实现更优的转录响应预测，在多基准测试中表现出色。
中文摘要 AI 辅助
预测特定扰动下的转录响应，对于理解细胞调控机制、加速药物发现至关重要。单细胞RNA测序会破坏每个被测细胞，仅产生对照组和扰动组的非配对细胞种群。然而，现有方法通常在单细胞水平上对扰动预测进行建模，并假设细胞间存在对应关系，这与观测数据的非配对特性相冲突。为解决该挑战，我们提出PopPert，这一框架明确对种群级联合基因表达分布进行参数化，以实现集体转录状态建模。给定对照组种群分布和某一扰动条件，PopPert可预测扰动诱导的分布参数变化，无需依赖细胞级对应关系，降低了对单细胞噪声的敏感性。为有效捕捉基因共表达模式，PopPert利用低秩高斯Copula对跨基因统计依赖关系进行建模，构建联合基因表达分布，还可采样生成合成扰动单细胞图谱。在涵盖遗传和化学扰动的多个单细胞基准测试中，PopPert在差异表达恢复、扰动效应估计和种群级分布匹配方面均实现了更优的整体性能。这些结果表明，种群级联合分布学习是从非配对单细胞种群预测转录响应的有效范式。PopPert的代码已公开，访问地址为this https URL。
英文摘要
Predicting transcriptional responses to specific perturbations is critical for understanding cellular regulatory mechanisms and accelerating drug discovery. Single-cell RNA sequencing destroys each measured cell, yielding only unpaired populations of control and perturbed cells. However, existing methods typically model perturbation prediction at the single-cell level and assume cell-to-cell correspondence, which conflicts with the unpaired nature of the observed data. To address this challenge, we propose PopPert, a framework that explicitly parameterizes population-level joint gene expression distributions for collective transcriptional state modeling. Given a control population distribution and a perturbation condition, PopPert predicts perturbation-induced changes in distribution parameters, eliminating the need for cell-level correspondence and reducing sensitivity to single-cell noise. To effectively capture gene co-expression patterns, PopPert leverages a low-rank Gaussian Copula to model cross-gene statistical dependencies and construct the joint gene expression distribution, additionally allowing sampling of synthetic perturbed single-cell profiles. Across multiple single-cell benchmarks spanning both genetic and chemical perturbations, PopPert achieves superior overall performance in differential expression recovery, perturbation effect estimation, and population-level distribution matching. These results establish population-level joint distribution learning as an effective paradigm for predicting transcriptional responses from unpaired single-cell populations. Code for PopPert is publicly available at https://github.com/whd1125/PopPert.
发表机构
- Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
