High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identification of subpopulations that have similar patterns in gene features. However, as the features corresponding to irrelevant confounders (e.g. gender or age) may dominate the clustering process, the resulting clusters may or may not capture clinically meaningful disease subtypes. This gives rise to a fundamental problem: can we find a subtyping procedure guided by a pre-specified disease outcome? Existing methods, such as supervised clustering, apply a two-stage approach and depend on an arbitrary number of selected features associated with outcome. In this paper, we propose a unified latent generative model to perform outcome-guided disease subtyping constructed from omics data, which improves the resulting subtypes concerning the disease of interest. Feature selection is embedded in a regularization regression. A modified EM algorithm is applied for numerical computation and parameter estimation. The proposed method performs feature selection, latent subtype characterization and outcome prediction simultaneously. To account for possible outliers or violation of mixture Gaussian assumption, we incorporate robust estimation using adaptive Huber or median-truncated loss function. Extensive simulations and an application to complex lung diseases with transcriptomic and clinical data demonstrate the ability of the proposed method to identify clinically relevant disease subtypes and signature genes suitable to explore toward precision medicine.
Multiple kernel learning for integrative consensus clustering of 'omic datasets
MRC Biostatistics Unit（MRC生物统计学单元）；Cambridge Institute of Therapeutic Immunology & Infectious Disease（剑桥治疗免疫学与传染病研究所）；University of Cambridge（剑桥大学）
不同的应用程序-特别是在肿瘤亚型-已经证明了整合聚类技术的重要性，从多个数据源的信息相结合。聚类分析（COCA）就是一种广泛应用于肿瘤亚型分析的方法。然而，COCA的属性从未被系统地探索过，并且它对包含噪声数据集或定义冲突聚类结构的数据集的鲁棒性尚不清楚。我们严格基准COCA，并提出核学习集成聚类（KLIC）作为一种替代策略。KLIC将组合聚类结构的挑战框定为多核学习问题，其中不同的数据集各自为最终聚类提供加权贡献。这允许噪声数据集的贡献相对于信息量更大的数据集被降低权重。我们通过仿真研究比较了KLIC和COCA在各种情况下的性能。我们还介绍了KLIC和COCA在癌症亚型和转录模块发现的实际数据应用中的输出。R软件包“klic”和“coca”可以在Comprehensive R Archive Network上找到。
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Department of Mathematics and Physics, Mathematics Major, Gangneung-Wonju National University（数学与物理系，数学专业，江陵-Wonju国立大学）；Department of Data Science, Gangneung-Wonju National University（数据科学系，江陵-Wonju国立大学）
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Tulane Center for Biomedical Informatics and Genomics（杜兰大学生物医学信息学与基因组学中心）；Division of Biomedical Informatics and Genomics（生物医学信息学与基因组学部门）；Deming Department of Medicine（戴明医学系）；Tulane University（杜兰大学）
General Effect Modelling (GEM) -- Part 2. Multivariate GEM applied to gene expression data of type 2 diabetes detects information that is lost by univariate validation
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
MRC Biostatistics Unit University of Cambridge East Forvie Building（医学研究理事会生物统计学单位剑桥大学东部大楼）；MRC Biostatistics Unit and CRUK Cambridge Centre Ovarian Programme and Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID) University of Cambridge（医学研究理事会生物统计学单位和癌症研究英国委员会剑桥中心卵巢计划及剑桥治疗免疫学和传染病研究所（CITIID）剑桥大学）
