论文标题
微生物组数据的基于树的稀疏聚类以表征胰腺癌中的微生物组异质性
Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer
论文作者
论文摘要
鉴于越来越多的证据表明其在确定治疗结果中的重要作用，人们对表征微生物组的变异的兴趣浓厚。在这里，我们的目标是发现具有相似微生物组谱的患者的亚组。我们在贝叶斯框架中提出了一种新颖的无监督聚类方法，该方法在现有基于模型的聚类方法（例如Dirichlet多项式混合模型）上进行了创新，这在三个关键方面：我们合并了特征选择，从数据中学习适当数量的群集，并将其集成在树结构上，将信息集成在树结构上。我们将提出方法的性能与旨在模拟真实微生物组数据的模拟数据的现有方法进行了比较。然后，我们说明了激励数据集获得的结果，该数据集旨在表征胰腺癌患者的肿瘤微生物组。
There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.
