Modeling time-series data from microbial communities

Ridenhour, Benjamin J.; Brooker, Sarah L; Williams, Janet E.; Leuven, James T. Van; Miller, Aaron W.; Dearing, M. Denise; Remien, Christopher H.

doi:10.1038/ismej.2017.107

Cited by 55 publications

(34 citation statements)

References 71 publications

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“…The increased complexity of these different modelling strategies would all benefit from the increased information content of higher resolution data types such as RADseq (Andrews, Good, Miller, Luikart, & Hohenlohe, 2016), or whole genomes (Bunnefeld et al, 2018). Additionally, the widespread availability of mitochondrial and environmental DNA data also makes our approach amenable to model the assembly of complex microbial systems (Li & Ma, 2016) with time series information (Ridenhour et al, 2017). Such time series data could introduce an additional axis of information allowing increased power to test hypotheses about the process of community assembly within a historical perspective.…”

Section: Discussionmentioning

confidence: 99%

An integrated model of population genetics and community ecology

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Emerson

Hickerson

2019

Journal of Biogeography

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Aim Quantifying abundance distributions is critical for understanding both how communities assemble, and how community structure varies through time and space, yet estimating abundances requires considerable investment in fieldwork. Community‐level population genetic data potentially offer a powerful way to indirectly infer richness, abundance and the history of accumulation of biodiversity within a community. Here we introduce a joint model linking neutral community assembly and comparative phylogeography to generate both community‐level richness, abundance and genetic variation under a neutral model, capturing both equilibrium and non‐equilibrium dynamics. Location Global. Methods Our model combines a forward‐time individual‐based community assembly process with a rescaled backward‐time neutral coalescent model of multi‐taxa population genetics. We explore general dynamics of genetic and abundance‐based summary statistics and use approximate Bayesian computation (ABC) to estimate parameters underlying the model of island community assembly. Finally, we demonstrate two applications of the model using community‐scale mtDNA sequence data and densely sampled abundances of an arachnid community on La Réunion. First, we use genetic data alone to estimate a summary of the abundance distribution, ground‐truthing this against the observed abundances. Then, we jointly use the observed genetic data and abundances to estimate the proximity of the community to equilibrium. Results Simulation experiments of our ABC procedure demonstrate that coupling abundance with genetic data leads to improved accuracy and precision of model parameter estimates compared with using abundance‐only data. We further demonstrate reasonable precision and accuracy in estimating a metric underlying the shape of the abundance distribution, temporal progress towards local equilibrium and several key parameters of the community assembly process. For the insular arachnid assemblage, we find the joint distribution of genetic diversity and abundance approaches equilibrium expectations, and that the Shannon entropy of the observed abundances can be estimated using genetic data alone. Main conclusions The framework that we present unifies neutral community assembly and comparative phylogeography to characterize the community‐level distribution of both abundance and genetic variation through time, providing a resource that should greatly enhance understanding of both the processes structuring ecological communities and the associated aggregate demographic histories.

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Section: Discussionmentioning

confidence: 99%

An integrated model of population genetics and community ecology

Overcast

Emerson

Hickerson

2019

Journal of Biogeography

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“…For example, Ridenhour et al. ( 2017 ) use an autoregressive integrated moving average (ARIMA) model to describe microbial interactions. The autoregressive component of an ARIMA model means that current values depend on previous values, while ARIMA models can also remove non-stationarity by differencing between consecutive values.…”

Section: Network As Representations Of a Complex Worldmentioning

confidence: 99%

From hairballs to hypotheses–biological insights from microbial networks

Röttjers

Faust

2018

FEMS Microbiology Reviews

459

456

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Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

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“…There have been a few methods developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series, including the dynamic linear models (MALLARDs) [ 11 ] with analysis performed on artificial human gut datasets, the sparse vector autoregression (sVAR) model [ 12 ], ARIMA Poisson model [ 13 ] and the Linear Mixed Model (MTV-LMM) [ 14 ]. Most of these models assume equal space sampling (which is usually not the case for the real data) based on a discrete process and most of the papers did not directly address the sampling frequency issues.…”

Section: Introductionmentioning

confidence: 99%

Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes

Kenney

Gao

2020

BMC Bioinformatics

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Background The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. Results For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein–Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein–Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein–Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. Conclusions There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8–3.2 days.

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Modeling time-series data from microbial communities

Cited by 55 publications

References 71 publications

An integrated model of population genetics and community ecology

An integrated model of population genetics and community ecology

From hairballs to hypotheses–biological insights from microbial networks

Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes

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