Saha Enakshi, Fanfani Viola, Mandros Panagiotis, Ben-Guebila Marouen, Fischer Jonas, Hoff-Shutta Katherine, Glass Kimberly, DeMeo Dawn Lisa, Lopes-Ramos Camila, Quackenbush John
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
bioRxiv. 2023 Nov 17:2023.11.16.567119. doi: 10.1101/2023.11.16.567119.
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 co-expression 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 co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, 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 sample-specific co-expression network inference methods 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聚合网络，因此无法完全捕捉群体异质性。为了解决这些问题，我们引入了BONOBO（通过整合组学数据获得的贝叶斯优化网络），这是一种可扩展的贝叶斯模型，用于通过识别个体间分子相互作用的变化来推导个体样本特异性共表达网络。对于每个样本，BONOBO在对数变换后的中心化基因表达上假设一个高斯分布，并在由数据中的所有其他样本构建的样本特异性共表达矩阵上假设一个共轭先验分布。将样本特异性基因表达与先验分布相结合，BONOBO得出了样本特异性共表达矩阵后验分布的闭式解，从而使该方法具有极高的可扩展性。我们在多种情况下展示了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优于其他样本特异性共表达网络推断方法，并能深入了解生物过程驱动因素中的个体差异。
