监督式贝叶斯联合图模型用于同时网络估计和亚组识别
Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
AI总结：
本文提出了一种监督式贝叶斯联合图模型，用于同时估计异质网络并识别亚组，通过引入新的相似性先验，提升了生物网络构建和亚组识别的临床意义。
摘要：
肿瘤的异质性是其基本特征。为了适应异质性，亚组识别已被广泛研究，并被大致分为无监督和有监督分析。与无监督分析相比，有监督方法可能具有更大的临床意义。在无监督分析框架下，已经开发出几种专注于基于网络的亚组识别的方法，这些方法比仅限于均值、方差和其他简单分布的方法提供了更全面的见解，通过纳入变量之间的相互连接。然而，针对有监督的基于网络的亚组识别研究仍然有限。在本研究中，我们开发了一种新的有监督贝叶斯图模型（SBJGM），用于同时识别多个异质网络和亚组。在所提出的模型中，异质性不仅反映在分子数据中，还与临床结果相关，并引入了一种新的相似性先验，以有效适应不同亚组网络之间的相似性，显著促进具有临床意义的生物网络构建和亚组识别。估计的一致性性质被严格建立，并开发了高效的算法。进行了广泛的模拟研究和对癌症基因组 atlas（TCGA）数据的现实应用，这些研究展示了所提出方法在亚组和网络识别方面的优势。
英文摘要：
Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervised and supervised analysis. Compared to unsupervised analysis, supervised approaches potentially hold greater clinical implications. Under the unsupervised analysis framework, several methods focusing on network-based subgroup identification have been developed, offering more comprehensive insights than those restricted to mean, variance, and other simplistic distributions by incorporating the interconnections among variables. However, research on supervised network-based subgroup identification remains limited. In this study, we develop a novel supervised Bayesian graphical model (SBJGM) for jointly identifying multiple heterogeneous networks and subgroups. In the proposed model, heterogeneity is not only reflected in molecular data but also associated with a clinical outcome, and a novel similarity prior is introduced to effectively accommodate similarities among the networks of different subgroups, significantly facilitating clinically meaningful biological network construction and subgroup identification. The consistency properties of the estimates are rigorously established, and an efficient algorithm is developed. Extensive simulation studies and a real-world application to The Cancer Genome Atlas (TCGA) data are conducted, which demonstrate the advantages of the proposed approach in terms of both subgroup and network identification.
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