The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from conventional approaches may not be necessarily associated with clinical outcomes. The collection of rich clinical data along with omics data has provided an unprecedented opportunity to facilitate the disease subtyping process and to discovery clinically meaningful disease subtypes. Thus, we developed an outcome-guided Bayesian clustering (GuidedBayesianClustering) method to fully integrate the clinical data and the high-dimensional omics data. A Gaussian mixed model framework was applied to perform sample clustering; a spike-and-slab prior was utilized to perform gene selection; a mixture model prior was employed to incorporate the guidance from a clinical outcome variable; and a decision framework was adopted to infer the false discovery rate of the selected genes. We deployed conjugate priors to facilitate efficient Gibbs sampling. Our proposed full Bayesian method is capable of simultaneously (i) obtaining sample clustering (disease subtype discovery); (ii) performing feature selection (select genes related to the disease subtype); and (iii) utilizing clinical outcome variable to guide the disease subtype discovery. The superior performance of the GuidedBayesianClustering was demonstrated through simulations and applications of breast cancer expression data.

翻译：疾病亚型的发现是精准医学发展的关键环节，而基于组学数据的疾病分型已逐渐成为主流方法。尽管前景广阔，传统方法获得的亚型未必与临床结局相关联。丰富的临床数据与组学数据的同步采集，为优化疾病分型流程并发现具有临床意义的疾病亚型提供了前所未有的机遇。为此，我们开发了一种结局导向的贝叶斯聚类方法（GuidedBayesianClustering），旨在整合临床数据与高维组学数据。该方法采用高斯混合模型框架进行样本聚类，利用spike-and-slab先验实现基因选择，运用混合模型先验整合临床结局变量的指导信息，并借助决策框架推断所选基因的假发现率。通过采用共轭先验，我们实现了高效的吉布斯采样。所提出的完整贝叶斯方法可同时：（i）实现样本聚类（疾病亚型发现）；（ii）进行特征选择（筛选与疾病亚型相关的基因）；（iii）利用临床结局变量指导疾病亚型发现。通过模拟实验和乳腺癌表达数据的应用，验证了GuidedBayesianClustering方法的优越性能。