BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA

• 成果类型: Article
• 署名作者: Choi, Junsouk; Chapkin, Robert S.; Ni, Yang
• 署名单位: Korea University; Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2042
• 发表日期: 2025-09
• 页码: 1908-1930
• 关键词: causal identifiability; differential network; parallel tempering; single-cell RNA-sequencing; bayesian network; beta-catenin; models; wnt; EQUIVALENCE; DISCOVERY; inference; selection; search; rac1

摘要

Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and, therefore, are not suitable for modeling zero-inflated count data. Moreover, it is often interesting to study differences in the causal networks for data collected from two experimental groups (control vs. treatment). To explicitly account for zero-inflation and identify differential causal networks, we propose a novel Bayesian differential zero-inflated negative binomial DAG (DAG0) model. We prove that the causal relationships under the proposed DAG0 are fully identifiable from purely observational, cross-sectional data, using a general proof technique that is applicable beyond the proposed model. Bayesian inference based on parallel-tempered Markov chain Monte Carlo is developed to efficiently explore the multimodal posterior landscape. We demonstrate the utility of the proposed DAG0 by comparing it with state-of-the-art alternative methods through extensive simulations. An application in a single-cell RNA-sequencing dataset, generated under two experimental groups, finds some interesting results that appear to be consistent with existing knowledge. A userfriendly R package that implements DAG0 is available at https://github.com/ junsoukchoi/BayesDAG0.git.