It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions which might be dormant in a single source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile such as copy number changes and RNA sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. The developed method is wrapped in R package semmcmc available at R CRAN.
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。
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）
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）
