Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
Genome Res. 2024 Oct 11;34(9):1397-1410. doi: 10.1101/gr.279117.124.
Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.
基因调控网络（GRNs）是推断调节生物过程的分子之间复杂相互作用的有效工具，因此可以深入了解生物系统的驱动因素。推断共表达网络是 GRN 推断的关键要素，因为表达模式之间的相关性可能表明基因受到共同因素的共同调控。然而，估计共表达网络的方法通常会得出一个代表群体平均调控特性的综合网络，因此无法完全捕捉到群体异质性。通过整合组学数据获得的贝叶斯优化网络（BONOBO）是一种可扩展的贝叶斯模型，用于推导出个体样本特定的共表达矩阵，该模型可以识别个体间分子相互作用的变化。对于每个样本，BONOBO 假设对数转换的中心化基因表达的正态分布，以及来自数据中所有其他样本的样本特定共表达矩阵的共轭先验分布。将样本特定的基因共表达与先验分布相结合，BONOBO 为样本特定共表达矩阵的后验分布提供了一个封闭形式的解决方案，从而允许对大型数据集进行分析。我们在几个上下文中展示了 BONOBO 的实用性，包括分析酵母转录因子敲除研究中的基因调控、人类乳腺癌亚型中 miRNA-mRNA 相互作用的预后意义以及人类甲状腺组织中基因调控的性别差异。我们发现，BONOBO 优于其他用于样本特定共表达网络推断的方法，并深入了解生物过程驱动因素的个体差异。
