He Linchen, Wang Chan, Hu Jiyuan, Gao Zhan, Falcone Emilia, Holland Steven M, Blaser Martin J, Li Huilin
Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States.
Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States.
Front Genet. 2022 Feb 25;13:777877. doi: 10.3389/fgene.2022.777877. eCollection 2022.
Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.
微生物群落的动态变化可能在人类健康和疾病中发挥重要作用。最近纵向微生物组研究的增加，需要能够对时间动态模式进行建模并同时量化微生物相互作用和群落稳定性的统计方法。在此，我们提出了一种新颖的自回归零膨胀混合效应模型（ARZIMM），以捕捉稀疏的微生物相互作用并估计群落稳定性。ARZIMM采用零膨胀泊松自回归模型分别对过多的零丰度和非零丰度进行建模，使用随机效应来研究组内共享的潜在动态模式，并使用套索型惩罚来捕捉和估计稀疏的微生物相互作用。基于估计的微生物相互作用矩阵，我们进一步推导群落稳定性的估计值，并通过网络推断确定核心动态模式。通过广泛的模拟研究和实际数据分析，我们将ARZIMM与其他方法进行了比较评估。
