Department of Statistics, Texas A&M University, College Station, TX, USA and Institute of Statistics and Big Data, Renmin University of China, Beijing, China.
Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China.
Biostatistics. 2022 Jul 18;23(3):891-909. doi: 10.1093/biostatistics/kxab002.
High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are compositional, sparse, noisy, and heterogeneous, which pose serious challenges for statistical modeling. We propose an identifiable Bayesian multinomial matrix factorization model to infer overlapping clusters on both microbes and hosts. The proposed method represents the observed over-dispersed zero-inflated count matrix as Dirichlet-multinomial mixtures on which latent cluster structures are built hierarchically. Under the Bayesian framework, the number of clusters is automatically determined and available information from a taxonomic rank tree of microbes is naturally incorporated, which greatly improves the interpretability of our findings. We demonstrate the utility of the proposed approach by comparing to alternative methods in simulations. An application to a human gut microbiome data set involving patients with inflammatory bowel disease reveals interesting clusters, which contain bacteria families Bacteroidaceae, Bifidobacteriaceae, Enterobacteriaceae, Fusobacteriaceae, Lachnospiraceae, Ruminococcaceae, Pasteurellaceae, and Porphyromonadaceae that are known to be related to the inflammatory bowel disease and its subtypes according to biological literature. Our findings can help generate potential hypotheses for future investigation of the heterogeneity of the human gut microbiome.
高通量测序技术为定量探索人类肠道微生物组及其与疾病的关系提供了前所未有的机会。微生物组数据具有组成性、稀疏性、噪声性和异质性，这给统计建模带来了严峻的挑战。我们提出了一种可识别的贝叶斯多项矩阵分解模型，以推断微生物和宿主上的重叠簇。该方法将观察到的过离散零膨胀计数矩阵表示为狄利克雷-多项混合物，在该混合物上分层构建潜在的聚类结构。在贝叶斯框架下，自动确定簇的数量，并自然纳入微生物分类等级树的可用信息，这极大地提高了我们发现结果的可解释性。我们通过与模拟中的替代方法进行比较，展示了所提出方法的效用。对涉及炎症性肠病患者的人类肠道微生物组数据集的应用揭示了有趣的簇，其中包含已知与炎症性肠病及其亚型相关的细菌科，如拟杆菌科、双歧杆菌科、肠杆菌科、梭菌科、lachnospiraceae、瘤胃球菌科、巴斯德氏菌科和卟啉单胞菌科。我们的发现可以帮助为未来研究人类肠道微生物组的异质性生成潜在的假说。
