Differential abundance (DA) analysis in microbiome studies has recently been used to uncover a plethora of associations between microbial composition and various health conditions. While current approaches to DA typically apply only to cross-sectional data, many studies feature a longitudinal design to better understand the underlying microbial dynamics. To study DA in longitudinal microbial studies, we introduce a novel varying coefficient mixed-effects model with local sparsity. The proposed method can identify time intervals of significant group differences while accounting for temporal dependence. Specifically, we exploit a penalized kernel smoothing approach for parameter estimation and include a random effect to account for serial correlation. In particular, our method operates effectively regardless of whether sampling times are shared across subjects, accommodating irregular sampling and missing observations. Simulation studies demonstrate the necessity of modeling dependence for precise estimation and support recovery. The application of our method to a longitudinal study of mice oral microbiome during cancer development revealed significant scientific insights that were otherwise not discernible through cross-sectional analyses. An R implementation is available at https://github.com/fontaine618/LSVCMM.

翻译：微生物组研究中的差异丰度分析近年来被广泛用于揭示微生物组成与各种健康状况之间的丰富关联。虽然当前的差异丰度分析方法通常仅适用于横断面数据，但许多研究采用纵向设计以更好地理解潜在的微生物动态。为研究纵向微生物研究中的差异丰度，我们提出了一种新颖的具有局部稀疏性的变系数混合效应模型。该方法能够在考虑时间依赖性的同时，识别出存在显著组间差异的时间区间。具体而言，我们采用惩罚核平滑方法进行参数估计，并引入随机效应以处理序列相关性。特别地，无论受试者间采样时间是否一致，我们的方法均能有效运行，可适应不规则采样和缺失观测。模拟研究证明了建模依赖关系对于精确估计和支持恢复的必要性。将我们的方法应用于小鼠癌症发展过程中口腔微生物组的纵向研究，揭示了通过横断面分析无法获得的显著科学发现。R语言实现可在 https://github.com/fontaine618/LSVCMM 获取。