发表机构
Department of Informatics（信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维多组学数据因果发现忽略层级结构或计算不可行的问题，提出基于约束的双层因果框架ASCEND，采用分治策略降低计算复杂度，实现基因组尺度高效精准的因果关系推断。
AI 中文摘要
生物系统呈现层级结构，其特征为从上游调控因子到下游效应的定向信息流。尽管这种有序结构为因果推断提供了天然框架，但多数因果发现与基因调控网络（GRN）方法要么忽略这种层级组织，要么对所有上游变量进行条件化处理，在高维组学数据场景下会变得不可行。本文提出ASCEND（通过继承性下降实现的祖先级可扩展因果发现，Ancestral Scalable Causal discovEry via iNherited Descent），这是一种基于约束的框架，利用已知的双层结构实现基因组尺度的因果发现。ASCEND引入分治策略，为每个下游变量维护动态更新的祖先条件集，大幅减少所需的条件独立性测试次数，在传统方法面临指数级计算膨胀的场景下实现了多项式时间复杂度。通过大量模拟实验与真实生物数据验证，证明ASCEND可精准恢复祖先关系，具备良好的可扩展性且运行速度快得多，在因果精度与计算效率上均优于现有基因调控网络推断方法。该算法解析因果方向性的能力使其尤其适用于联合测量上游调控因子（如单核苷酸多态性SNP、甲基化位点）与下游响应（如基因表达）的多组学数据整合场景。
英文摘要
Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffold for causal inference, most causal discovery and GRN methods either ignore the tiered organisation or condition on all upstream variables, which becomes infeasible for high-dimensional omics data. We present ASCEND (Ancestral Scalable Causal discovEry via iNherited Descent), a constraint-based framework that leverages known two-tiered structure to enable genome-scale causal discovery. ASCEND introduces a divide-and-conquer strategy that maintains dynamically updated ancestral conditioning sets for each downstream variable, dramatically reducing the number of conditional independence tests required, and achieves polynomial-time complexity where traditional approaches face exponential blow-up. Through extensive simulations and real biological data, we demonstrate that ASCEND accurately recovers ancestral relationships, scales properly and much faster, and outperforms existing gene regulatory network inference methods in both causal precision and computational efficiency. The algorithm's ability to resolve directionality makes it particularly suited for integrating multi-omic data where upstream regulators (e.g., SNPs, methylation sites) and downstream responses (e.g., gene expression) are measured jointly.
CommentsMain material: 8 pages + supplementary material. 16 pages in all
