Inferring Microbial Interactions from Metagenomic Time-series Using Prior Biological Knowledge

Chieh Lo, R. Marculescu
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引用次数: 11

Abstract

Due to the recent advances in modern metagenomics sequencing methods, it becomes possible to directly analyze the microbial communities within human body. To understand how microbial communities adapt, develop, and interact over time with the human body and the surrounding environment, a critical step is the inference of interactions among different microbes directly from sequencing data. However, metagenomics data is both compositional and highly dimensional in nature. Consequently, new approaches that can accurately and robustly estimate the interactions among various microbe species are needed to analyze such data. To this end, we propose a novel framework called Microbial Time-series Prior Lasso (MTPLasso) which integrates sparse linear regression with microbial co-occurrences and associations obtained from scientific literature and cross-sectional metagenomics data. We show that MTPLasso outperforms existing models in terms of precision and recall rates, as well as the accuracy in inferring the interaction types. Finally, the interaction networks we infer from human gut data demonstrate credible results when compared against real data.
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利用先验生物学知识从宏基因组时间序列推断微生物相互作用
由于现代宏基因组测序方法的最新进展,直接分析人体微生物群落成为可能。为了了解微生物群落是如何适应、发展并随时间与人体和周围环境相互作用的,关键的一步是直接从测序数据推断不同微生物之间的相互作用。然而,宏基因组学数据在本质上既是组成的,又是高度多维的。因此,需要新的方法来准确和稳健地估计各种微生物物种之间的相互作用来分析这些数据。为此,我们提出了一个新的框架,称为微生物时间序列先验套索(MTPLasso),它将稀疏线性回归与从科学文献和横断面宏基因组学数据中获得的微生物共现和关联相结合。我们表明,MTPLasso在精度和召回率以及推断交互类型的准确性方面优于现有模型。最后,与真实数据相比,我们从人类肠道数据推断出的相互作用网络显示出可信的结果。
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