Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, USA.
Bioinformatics Research Group (BioRG), Florida International University, 11200 SW 8th Street, Miami, 33199, Florida, USA.
Microbiome. 2019 Apr 2;7(1):54. doi: 10.1186/s40168-019-0660-3.
Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.
Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public .
We propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.
许多研究都集中在微生物群落上，这些微生物群落在人体等环境中生活。在这些研究中，研究人员收集纵向数据，目的不仅是了解微生物组的组成，而且还要了解不同分类群之间的相互作用。然而，这种数据分析具有挑战性，很少有方法可以从时间序列微生物组数据中重建动态模型。
在这里，我们提出了一种计算流程，可实现个体之间数据的集成，以重建此类模型。我们的流程首先对齐所有个体收集的数据。然后，将对齐的谱用于学习动态贝叶斯网络，该网络表示分类群与临床变量之间的因果关系。在三个纵向微生物组数据集上进行测试表明，我们的方法优于为此任务开发的先前方法。我们还讨论了模型提供的生物学见解，其中包括几个已知和新颖的相互作用。扩展的 CGBayesNets 包根据 MIT 开源许可协议免费提供。源代码和文档可从 https://github.com/jlugomar/longitudinal_microbiome_analysis_public 下载。
我们提出了一种用于分析纵向微生物组数据的计算流程。我们的结果提供了证据，证明微生物组比对与动态贝叶斯网络相结合，可以提高预测性能，优于以前的方法，并增强了我们推断微生物组内以及分类群与临床因素之间生物学关系的能力。
