Department of Computer and Information Science, University of Massachusetts Dartmouth, Massachusetts, USA.
Department of Microbiology and Physiological Systems, University of Massachusetts, Medical School, Worcester, Massachusetts, USA.
mSystems. 2022 Oct 26;7(5):e0013222. doi: 10.1128/msystems.00132-22. Epub 2022 Sep 7.
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases. Due to the large scale and complexity of microbiome data, computational methods are essential. Existing computational methods for linking changes in the microbiome to the status of the human host are either unable to scale to large and complex microbiome data sets or cannot produce human-interpretable outputs. We present a new computational method and software package that overcomes the limitations of previous methods, allowing researchers to analyze larger and more complex data sets while producing easily interpretable outputs. Our method has the potential to enable new insights into how changes in the microbiome over time maintain health or lead to disease in humans and facilitate the development of diagnostic tests based on the microbiome.
越来越多的研究正在生成纵向微生物组数据集，人们普遍认识到，这些研究对于揭示微生物组影响人类健康和疾病的机制至关重要。然而，目前用于分析微生物组时间序列数据的计算工具还很缺乏。为了解决这一差距，我们开发了一个开源软件包 Microbiome Differentiable Interpretable Temporal Rule Engine（MDITRE），它实现了一种新的高效方法，利用深度学习技术从纵向微生物组数据中提取可预测宿主状态的人类可解释规则。使用半合成和大量公开可用的 16S rRNA 扩增子和宏基因组测序数据集，我们证明在几乎所有情况下，MDITRE 的性能与流行的不可解释机器学习方法相当或更好，并且速度比之前的可解释技术快几个数量级。MDITRE 还提供了一个图形用户界面，我们通过案例研究表明，该界面可用于得出将微生物组随时间变化的模式与宿主表型联系起来的有生物学意义的解释。
人类微生物组，或生活在我们身上和体内的微生物集合，随时间而变化。将这些变化与人类宿主的状态联系起来对于理解微生物组如何影响各种人类疾病至关重要。由于微生物组数据的规模大和复杂性，计算方法是必不可少的。将微生物组的变化与人类宿主的状态联系起来的现有计算方法要么无法扩展到大型和复杂的微生物组数据集，要么无法生成人类可解释的输出。我们提出了一种新的计算方法和软件包，克服了以前方法的局限性，使研究人员能够分析更大和更复杂的数据集，同时生成易于解释的输出。我们的方法有可能使我们深入了解微生物组随时间的变化如何保持健康或导致人类疾病，并促进基于微生物组的诊断测试的发展。
