Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality
1
Basic Experimental of Natural Science, University of Science and Technology Beijing, Beijing 100083, China
2
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
3
Department of Medicine, Stanford University School of Medicine, 269 Campus Dr., Stanford, CA 94305, USA
*
Authors to whom correspondence should be addressed.
Genes 2019, 10(3), 216; https://doi.org/10.3390/genes10030216
Received: 3 February 2019
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Revised: 9 March 2019
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Accepted: 11 March 2019
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Published: 14 March 2019
(This article belongs to the Section Microbial Genetics and Genomics)
The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).
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Keywords:
Granger causality; conditional Granger causality; microbial association network; time series data; marine microbes
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MDPI and ACS Style
Ai, D.; Li, X.; Liu, G.; Liang, X.; Xia, L.C. Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality. Genes 2019, 10, 216. https://doi.org/10.3390/genes10030216
AMA Style
Ai D, Li X, Liu G, Liang X, Xia LC. Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality. Genes. 2019; 10(3):216. https://doi.org/10.3390/genes10030216
Chicago/Turabian StyleAi, Dongmei, Xiaoxin Li, Gang Liu, Xiaoyi Liang, and Li C. Xia. 2019. "Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality" Genes 10, no. 3: 216. https://doi.org/10.3390/genes10030216
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