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.
Module-structured mixture factor models for molecular subtype discovery in transcriptomic data
School of Mathematics and Physics, The University of Queensland（昆士兰大学数理学院）；Department of Statistics and Applied Probability, University of California, Santa Barbara（加州大学圣塔芭芭拉分校统计与应用概率系）；Health Analytics Network（健康分析网络）
A mixture model for subtype identification in the context of disease progression modeling
Sorbonne Université（索邦大学）；Institut du Cerveau（脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国研究学院）；Inserm（法国国家健康与医学研究院）；AP-HP Groupe Hospitalier Sorbonne Université（巴黎医院集团索邦大学医院）；KIK Amsterdam UMC（阿姆斯特丹大学医学中心）；Université Paris Cité（巴黎西岱大学）；HeKA（HEKA实验室）；GENOVASC INSERM U1127（INSERM U1127 GENOVASC实验室）；Centre de Référence National CERVCO（CERVCO国家级参考中心）；CNVT FHU-NeuroVasc-2030；Hopital Lariboisiére APHP（拉博谢尔医院APHP）；Université Paris-Cité（巴黎西岱大学）
Bayesian latent hierarchical model for transcriptomic meta-analysis to detect biomarkers with clustered meta-patterns of differential expression signals
Sparse integrative clustering of multiple omics data sets
高分辨率微阵列和第二代测序平台是研究与疾病相关的DNA拷贝数、甲基化和基因表达的全基因组改变的有力工具。整合的基因组分析方法在同一组生物样品中同时测量多种组学数据类型。这种方法提供了一个集成的数据解析，这是任何单一数据类型都无法实现的。在这项研究中，我们使用惩罚潜变量回归方法对多种组学数据类型进行联合建模，以识别可用于将患者样本聚类为生物学和临床相关疾病亚型的常见潜变量。我们认为套索[J.罗伊。中央集权主义者Soc.Ser.B58（1996）267-288]、弹性网[J.R. Stat. Soc. Ser. B Stat.美沙酮67（2005）301-320]和融合套索[J. R. Stat. Soc. Ser. B Stat.美沙酮67（2005）91-108]方法来诱导系数向量中的稀疏性，从而揭示对潜在变量具有显著贡献的重要基因组特征。使用迭代岭回归来计算稀疏系数向量。在模型选择中，使用均匀设计[统计学和应用概率专著（1994）Chapman & Hall]来寻找均匀地分散在搜索域上的“实验”点，用于调谐参数组合的有效采样。我们比较了我们的方法稀疏奇异值分解（SVD）和惩罚高斯混合模型（GMM）使用真实和模拟数据集。所提出的方法是适用于整合基因组，表观基因组和转录组数据的亚型分析在乳腺癌和肺癌数据集。
