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Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality
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 communi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471626/ https://www.ncbi.nlm.nih.gov/pubmed/30875820 http://dx.doi.org/10.3390/genes10030216 |
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author | Ai, Dongmei Li, Xiaoxin Liu, Gang Liang, Xiaoyi Xia, Li C. |
author_facet | Ai, Dongmei Li, Xiaoxin Liu, Gang Liang, Xiaoyi Xia, Li C. |
author_sort | Ai, Dongmei |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-6471626 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64716262019-04-27 Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality Ai, Dongmei Li, Xiaoxin Liu, Gang Liang, Xiaoyi Xia, Li C. Genes (Basel) Article 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). MDPI 2019-03-14 /pmc/articles/PMC6471626/ /pubmed/30875820 http://dx.doi.org/10.3390/genes10030216 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ai, Dongmei Li, Xiaoxin Liu, Gang Liang, Xiaoyi Xia, Li C. Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title | Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title_full | Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title_fullStr | Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title_full_unstemmed | Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title_short | Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality |
title_sort | constructing the microbial association network from large-scale time series data using granger causality |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471626/ https://www.ncbi.nlm.nih.gov/pubmed/30875820 http://dx.doi.org/10.3390/genes10030216 |
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