基于多尺度混杂与测量误差的扰动性和人群规模单细胞数据的因果路径分析
Causal Path Analysis from Perturbational and Population-Scale Single-Cell Data with Multiscale Confounding and Measurement Error
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出一个整合扰动与人群单细胞数据的因果路径分析框架，通过外部祖先关系约束网络拓扑并联合校正混杂与测量误差，在急性髓系白血病中识别出独特调控通路。
中文摘要 AI 辅助
单细胞扰动实验提供了关于基因调控的因果信息，而人群规模的单细胞研究则表征了人类群体中的基因表达和表型。我们开发了一个整合这些互补数据源以进行因果路径分析的框架。我们不假设扰动基因网络直接转移到目标人群，而是利用外部学习的祖先关系来约束网络拓扑结构，并从人群数据中重新估计其直接边和效应。为了解决多尺度单细胞测量中的潜在异质性和测量误差，我们开发了一种在细胞和受试者两个层面运作的替代变量程序，并结合网络和结果回归的误差变量校正。我们为混杂因素恢复以及网络和基因-结果效应的高维估计建立了理论保证。模拟实验证明了联合校正混杂和测量误差的重要性。一项针对急性髓系白血病的应用识别出了将转录调节因子与原始细胞计数联系起来的独特调控通路。
英文摘要
Single-cell perturbation experiments provide causal information on gene regulation, whereas population-scale single-cell studies characterize gene expression and phenotypes in human populations. We develop a framework that integrates these complementary data sources for causal path analysis. Rather than assuming that a perturbational gene network transfers directly to the target population, we use externally learned ancestral relationships to constrain the network topology and re-estimate its direct edges and effects from population data. To address latent heterogeneity and measurement error in multiscale single-cell measurements, we develop a surrogate-variable procedure operating at both the cell and subject levels, combined with errors-in-variables correction for network and outcome regressions. We establish theoretical guarantees for confounder recovery and high-dimensional estimation of network and gene-outcome effects. Simulations demonstrate the importance of jointly correcting confounding and measurement error. An application to acute myeloid leukemia identifies distinct regulatory pathways linking transcriptional regulators to blast count.
