论文标题
通过贝叶斯网络回归模型识别生物表型中的微生物驱动因素
Identifying microbial drivers in biological phenotypes with a Bayesian Network Regression model
论文作者
论文摘要
1。在贝叶斯网络回归模型中，网络被认为是连续响应的预测指标。这些模型已在大脑研究中成功使用，以识别与特定人类特征相关的大脑区域，但它们在微生物组研究中阐明生物学表型中微生物驱动因素的潜力尚不清楚。特别是，由于与大脑网络相比，微生物网络的高维度和较高的稀疏性都具有挑战性。此外，与脑连接组研究不同，在微生物组研究中，通常可以预期，微生物的存在对响应（主要效应）有影响，而不仅仅是相互作用。 2。在这里，我们对贝叶斯网络回归模型是否适用于各种生物学情景下的各种合成和真实数据的微生物数据集进行了首次彻底研究。我们测试仅考虑相互作用效果的贝叶斯网络回归模型（网络中的边缘）是否能够在表型变异性中识别关键驱动因素（微生物）。 3。我们表明，该模型确实能够识别微生物网络中的有影响力的节点和边缘，这些节点和边缘驱动大多数生物环境的表型变化，但是我们还确定了该方法的性能较差的场景，使我们能够为旨在将这些工具应用到其数据集中的域科学家提供实用建议。 4。BNR模型为微生物组研究人员提供了一个框架，以识别微生物与测量表型之间的连接。我们通过提供易于使用的实现来使用此统计模型，该实现是https://github.com/solislemuslab/bayesiannetworkregression.jl公开可用的Julia软件包。
1. In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high-dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes have an effect on the response (main effects), not just the interactions. 2. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. 3. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. 4. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.
