Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

中文翻译：

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高斯和混合图形模型作为（多）组学数据分析工具。

高斯图形模型 (GGM) 是推断生物变量之间依赖性的工具。流行的应用是基因、蛋白质和代谢物关联网络的重建。GGM 是一种探索性研究工具，可用于发现基因（功能簇）之间有趣的关系或识别治疗上有趣的基因，但不一定推断机械意义上的网络。尽管 GGM 从理论和应用的角度得到了很好的研究，但重要的扩展在生物界中并不为人所知。例如，GGM 假设多元正态分布数据。如果违反此假设，混合图形模型 (MGM) 可能是更好的选择。在这篇综述中，我们提供了 GGM 的理论基础，提出了 MGM 或多类 GGM 等扩展，并说明了这些方法如何提供对生物机制的见解。我们总结了几种应用并提出了用户友好的估计软件。本文是由 Federico Manuel Giorgi 博士和 Shaun Mahony 博士编辑的特刊的一部分，题为：转录谱和调控基因网络。