基于多项式矩阵分解的微生物宏基因组测序数据贝叶斯双聚类
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization
摘要：
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、噪声性和异质性等特点，这对统计建模提出了严峻的挑战。我们提出了一个可识别的贝叶斯多项式矩阵分解模型来推断微生物和宿主上的重叠簇。所提出的方法表示观察到的过度分散的零膨胀计数矩阵的狄利克雷多项式的混合物上的潜在集群结构的层次。在贝叶斯框架下，聚类的数量是自动确定的，并且自然地结合了来自微生物分类等级树的可用信息，这大大提高了我们发现的可解释性。我们证明了所提出的方法的实用性，通过比较模拟中的替代方法。应用于涉及患有炎性肠病的患者的人类肠道微生物组数据集揭示了有趣的簇，其包含根据生物学文献已知与炎性肠病及其亚型相关的细菌家族拟杆菌科、双歧杆菌科、肠杆菌科、梭杆菌科、毛螺菌科、瘤胃球菌科、巴斯德菌科和卟啉单胞菌科。我们的研究结果可以帮助产生潜在的假设，用于未来对人类肠道微生物组异质性的研究。
英文摘要：
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 dataset 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.
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