DRAGON：在多组学网络上使用图模型确定调控关联
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks
AI总结：
本研究提出基于高斯图模型的多组学网络分析方法DRAGON，可适配组学层差异以提升推断效果，在乳腺癌数据中识别出潜在生物标志物，且已开源发布。
摘要：
针对同一标本甚至同一细胞采集的甲基化组、转录组谱等多组学数据数量日益增长，为探索定义细胞表型、调控细胞对扰动响应的复杂相互作用提供了独特契机。我们提出一种基于高斯图模型（GGMs）的网络方法，可实现配对组学数据的联合分析。该方法名为DRAGON（在多组学网络上使用图模型确定调控关联），通过校准参数在网络复杂度与估计精度间实现最优权衡，同时明确考量每个受评估组学“层”的特征。在模拟研究中，我们发现DRAGON可适应不同组学层间的边密度与特征规模差异，与现有最优方法相比，提升了模型推断与边恢复的效果。我们进一步对TCGA乳腺癌标本的转录组-甲基化组联合数据开展分析，证明DRAGON能够识别启动子甲基化介导的基因调控等关键分子机制。具体而言，我们识别出转录因子AP-2β（TFAP2B）可作为基底型乳腺癌的潜在多组学生物标志物。DRAGON以Python开源代码形式通过Network Zoo包（netZooPy v0.8；netzoo.github.io）提供使用。
英文摘要：
The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics "layers." In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
