Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities

ABSTRACT Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding 1 million per ml. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative spe...

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Autores principales:	Ashley R. Coenen, Joshua S. Weitz
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	correlation inference interaction network microbial ecology viral ecology Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/67e15ea446fd4e4fa864fca9eb513970
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spelling	oai:doaj.org-article:67e15ea446fd4e4fa864fca9eb5139702021-12-02T19:45:30ZLimitations of Correlation-Based Inference in Complex Virus-Microbe Communities10.1128/mSystems.00084-182379-5077https://doaj.org/article/67e15ea446fd4e4fa864fca9eb5139702018-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00084-18https://doaj.org/toc/2379-5077ABSTRACT Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding 1 million per ml. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods that leverage product-moment, time-lagged, and relative-abundance-based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses. IMPORTANCE Inferring interactions from population time series is an active and ongoing area of research. It is relevant across many biological systems—particularly in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference—using correlations to predict interactions—is widespread. However, it is well-known that “correlation does not imply causation.” Despite this, many studies apply correlation-based inference methods to experimental time series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silico virus-microbe communities, raising questions on their relevance when applied in situ.Ashley R. CoenenJoshua S. WeitzAmerican Society for Microbiologyarticlecorrelationinferenceinteraction networkmicrobial ecologyviral ecologyMicrobiologyQR1-502ENmSystems, Vol 3, Iss 4 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	correlation inference interaction network microbial ecology viral ecology Microbiology QR1-502
spellingShingle	correlation inference interaction network microbial ecology viral ecology Microbiology QR1-502 Ashley R. Coenen Joshua S. Weitz Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
description	ABSTRACT Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding 1 million per ml. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods that leverage product-moment, time-lagged, and relative-abundance-based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses. IMPORTANCE Inferring interactions from population time series is an active and ongoing area of research. It is relevant across many biological systems—particularly in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference—using correlations to predict interactions—is widespread. However, it is well-known that “correlation does not imply causation.” Despite this, many studies apply correlation-based inference methods to experimental time series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silico virus-microbe communities, raising questions on their relevance when applied in situ.
format	article
author	Ashley R. Coenen Joshua S. Weitz
author_facet	Ashley R. Coenen Joshua S. Weitz
author_sort	Ashley R. Coenen
title	Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
title_short	Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
title_full	Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
title_fullStr	Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
title_full_unstemmed	Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities
title_sort	limitations of correlation-based inference in complex virus-microbe communities
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/67e15ea446fd4e4fa864fca9eb513970
work_keys_str_mv	AT ashleyrcoenen limitationsofcorrelationbasedinferenceincomplexvirusmicrobecommunities AT joshuasweitz limitationsofcorrelationbasedinferenceincomplexvirusmicrobecommunities
_version_	1718376029989371904

Limitations of Correlation-Based Inference in Complex Virus-Microbe Communities

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