A Primer for Microbiome Time-Series Analysis

Coenen, Ashley R.; Hu, Sarah K.; Luo, Elaine; Muratore, Daniel; Weitz, Joshua S.

doi:10.3389/fgene.2020.00310

Cited by 54 publications

(55 citation statements)

References 119 publications

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“…Unfortunately, even this cannot be guaranteed. As we discuss below, entirely independent temporal processes can also appear highly correlated [39,38,10].…”

Section: Pairwise Correlation and Reichenbach's Common Cause Principlementioning

confidence: 99%

“…In the first example, two microbial cultures in exponential phase independently grown in different tubes have correlated densities because of their shared growth law ( Fig 3A). In the second example involving island populations controlled by migrations, population sizes of two causally unrelated island populations usually appear significantly correlated according to currently popular statistical techniques ( Fig 3B; see also [39,10]). The third example considers island populations whose sizes are driven by immigration and cell death ( Fig 3B).…”

Section: Pairwise Correlation and Reichenbach's Common Cause Principlementioning

confidence: 99%

“…In contrast to Granger causality, which relies entirely on stochastic process noise ( Fig B5), the "state space reconstruction" (SSR) approach is intended for systems that are primarily deterministic. SSR has been widely proposed as an alternative to model-based inference for detecting interactions within microbial ecosystems [69,70,27,71,24,10]. Intuitively, the SSR approach is based on the idea that if X drives Y , then a record of the X's influence will be present in the history of Y .…”

Section: State Space Reconstruction (Ssr)mentioning

confidence: 99%

“…Unfortunately, this approach is inadequate for multiple reasons [7,8,9], and it is often impossible to assess the appropriateness of a mechanistic model for a given ecosystem. As there are multiple thoughtful explorations of the many considerations related to mechanistic modeling [6,10], we will not focus on these topics here.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven causal analysis of observational time series in ecology

Shou

2020

Preprint

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Complex systems such as microbial communities play key roles in global processes and human life, yet are often challenging to understand. Although mechanistic knowledge in biology is generally rooted in manipulative experiments, perturbing these systems can encounter practical and ethical barriers. Thus, extensive attempts have been made to infer causal knowledge by analyzing observations of taxon abundance over time. When, and to what extent, does this strategy yield genuine insight? Unfortunately, the literature of causal inference can be formidable and controversial, as it draws from divergent fields such as philosophy, statistics, econometrics, and chaos theory. Most benchmarking papers focus on performance details of causal inference approaches, rather than fundamental issues such as the the underlying assumptions and their reasons, conceptual distinctions, and universal limitations. Here, we provide a synthesis of popular causal inference approaches including pairwise correlation and Reichenbach's common cause principle, Granger causality, and state space reconstruction. We find that each of these requires that certain properties of the data do not change with time (e.g. "IID", "stationarity", "reverting dynamics"). We provide new ways of visualizing key concepts, point out important issues that have been under-emphasized, and in some cases describe novel pathologies of causal inference methods. Although our synthesis is motivated by microbial communities, all arguments apply to other types of dynamic systems. We strive to balance precision with accessibility, and hope that our synthesis will motivate future development on causal inference approaches. To facilitate communication to a broad audience, we have made an accompanying video walkthrough (https://youtu.be/TZvEk3jXQfY).

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“…Unfortunately, even this cannot be guaranteed. As we discuss below, entirely independent temporal processes can also appear highly correlated [39,38,10].…”

Section: Pairwise Correlation and Reichenbach's Common Cause Principlementioning

confidence: 99%

Section: Pairwise Correlation and Reichenbach's Common Cause Principlementioning

confidence: 99%

Section: State Space Reconstruction (Ssr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-driven causal analysis of observational time series in ecology

Shou

2020

Preprint

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“…To date, there exist few analytical tools for this kind of data in the microbiome field. Two examples can be found at Bodein et al (2019) and Coenen et al (2020) , but they are restricted to unsupervised analysis.…”

Section: Introductionmentioning

confidence: 99%

kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

et al. 2021

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The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt.

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Wolbachia impacts microbiome diversity and fitness‐associated traits for Drosophila melanogaster in a seasonally fluctuating environment

Henry,

Fernandez,

Wolf

et al. 2024

Ecology and Evolution

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The microbiome contributes to many different host traits, but its role in host adaptation remains enigmatic. The fitness benefits of the microbiome often depend on ecological conditions, but theory suggests that fluctuations in both the microbiome and environment modulate these fitness benefits. Moreover, vertically transmitted bacteria might constrain the ability of both the microbiome and host to respond to changing environments. Drosophila melanogaster provides an excellent system to investigate the impacts of interactions between the microbiome and the environment. To address this question, we created field mesocosms of D. melanogaster undergoing seasonal environmental change with and without the vertically transmitted bacteria, Wolbachia pipientis. Sampling temporal patterns in the microbiome revealed that Wolbachia constrained microbial diversity. Furthermore, Wolbachia and a dominant member of the microbiome, Commensalibacter, were associated with differences in two higher‐order fitness traits, starvation resistance and lifespan. Our work here suggests that the interplay between the abiotic context and microbe–microbe interactions may shape key host phenotypes that underlie adaptation to changing environments. We conclude by exploring the consequences of complex interactions between Wolbachia and the microbiome for our understanding of eco‐evolutionary processes that shape host‐microbiome interactions.

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A Primer for Microbiome Time-Series Analysis

Cited by 54 publications

References 119 publications

Data-driven causal analysis of observational time series in ecology

Data-driven causal analysis of observational time series in ecology

kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

Wolbachia impacts microbiome diversity and fitness‐associated traits for Drosophila melanogaster in a seasonally fluctuating environment

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