AI 中文总结
研究针对微生物组计数网络分析难题，提出CoLaDAG方法，结合多种模型与优化技术。模拟中表现良好，案例研究里拟合图对重采样和参考选择敏感，所得关系为探索性假设，优先考虑厌氧肠道分类群用于相关研究。
AI 中文摘要
微生物组计数的直接网络分析因成分采样、高维度和有限的生物学复制而变得复杂。我们提出了CoLaDAG，一种固定参考潜在加性对数比（ALR）估计器，用于从成分计数中生成稀疏的有向条件依赖假设。该方法结合了多项观察模型、工作线性高斯结构方程模型、非凸DC-ADMM优化以及带有贪婪无环投影的估计后阈值处理。在与该观察模型一致的模拟中，CoLaDAG在评估的实现中获得了最大的平均精确方向马修斯相关性和最小的平均错误发现率；在连续数据和缺失值误设下性能会下降。在12只小鼠的银纳米颗粒（AgNP）案例研究中，拟合的58节点图对块重采样和ALR参考选择敏感：284条主要边中的60条达到了至少0.60的小鼠块选择频率。报告的方向和剂量分层斜率是探索性的、特定坐标的假设，而不是已确定的因果或暴露效应。领先的稳定关系优先考虑厌氧肠道分类群，用于靶向丰度、代谢物和扰动研究，但未建立交叉喂养或毒理学机制。
英文摘要
Directed network analysis of microbiome counts is complicated by compositional sampling, high dimensionality, and limited biological replication. We present CoLaDAG, a fixed-reference latent additive log-ratio (ALR) estimator for generating sparse directed conditional-dependence hypotheses from compositional counts. The method combines a multinomial observation model, a working linear Gaussian structural equation model, nonconvex DC-ADMM optimization, and post-estimation thresholding with greedy acyclic projection. Under simulations aligned with this observation model, CoLaDAG obtained the largest mean exact-direction Matthews correlation and the smallest mean false discovery rate among the evaluated implementations; performance deteriorated under continuous-data and dropout misspecification. In the 12-mouse silver-nanoparticle (AgNP) case study, the 58-node fitted graph was sensitive to block resampling and ALR reference choice: 60 of 284 primary edges attained a mouse-block selection frequency of at least 0.60. The reported orientations and dose-stratified slopes are exploratory, coordinate-specific hypotheses rather than identified causal or exposure effects. The leading stable relations prioritize anaerobic gut taxa for targeted abundance, metabolite, and perturbation studies, but do not establish cross-feeding or toxicological mechanisms.
