EMBED: Essential Microbiome Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Shahin, Mayar; Ji, Brian W.; Dixit, Purushottam D.

doi:10.21203/rs.3.rs-1450996/v1

Cited by 4 publications

(6 citation statements)

References 45 publications

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“…are species-specific but host-independent. For model fitting with a fixed 𝐾, we minimize the cross-entropy [26] (Eq. 4):…”

Section: Resultsmentioning

confidence: 99%

“…That is, latent variables z sk are host-specific but species-independent and θ ko are species-specific but host-independent. For model fitting with a fixed K , we minimize the cross-entropy [27] (Eq. 4): As we show in SI Section 1 , this minimization is equivalent to a nonlinear low rank matrix factorization problem wherein z and θ can be learnt using gradient descent.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Designing host-associated microbiomes using the consumer/resource model

Plata

Krishnamurthy

Herron

et al. 2023

Preprint

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Correlated variation between host phenotypes and microbiomes suggest that emergent global variables may simultaneously describe the microbial ecosystem and the host. Mechanistic models cannot yet identify these descriptors because of their inherent complexity. To that end, we present a phenomenological model based on the consumer/resource framework wherein microbial species and hosts' phenotypes are coupled through their shared dependence on a small number of generalized resources (latent variables). We show that animal microbiomes are surprisingly low-dimensional; the number of latent variables needed to accurately describe these ecosystems is significantly smaller than the typical number of resources or microorganisms present. The model reproduces key metrics of biodiversity through probabilistic sampling of the latent variables. It also identifies host phenotypes that significantly determine the latent space, and therefore predict the microbiome composition from host phenotypes, and vice versa. Finally, clustering of hosts using their latent representation identifies subsets of hosts with context-specific phenotype-microbiome associations. We believe that this phenomenological approach will be key to quantitative exploration of host-associated microbiomes.

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“…are species-specific but host-independent. For model fitting with a fixed 𝐾, we minimize the cross-entropy [26] (Eq. 4):…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Designing host-associated microbiomes using the consumer/resource model

Plata

Krishnamurthy

Herron

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our approach shares common elements with other top-down approaches like the Stochastic Logistic Model [54,55] and recent data-driven models [56,57] without explicit interspecies interactions. While these other models attribute growth rate fluctuations to external factors, our model focuses on endogenously-driven environmental change.…”

Section: Discussionmentioning

confidence: 96%

Dynamic coexistence driven by physiological transitions in microbial communities

Narla,

Hwa,

Murugan

2024

Preprint

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Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states. Our model bypasses specific interactions (e.g., nutrient starvation, stress, aggregation) that lead to different combinations of physiological states, referred to collectively as "community states", and modeled by specifying the growth preference of each species along a global ecological coordinate, taken here to be the total community biomass density. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: increased tolerance of community diversity to fast growth rates of species dominating different community states, enhanced community stability through staggered dominance of different species in different community states, and increased requirement on growth dominance for the inclusion of late-growing species. These features, derived explicitly for simplified models, are proposed here to be principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on idealized inter-species interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics.

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“…The FARMM dataset was published by [8] (BioProject ID PRJNA675301). Preprocessed relative abundance data were obtained from https://github .com/syma -research/ microTensor/tree/main/data/FARMM.…”

Section: Sample-level Beta Diversity and Subject-specific Trajectoriesmentioning

confidence: 99%

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

Shi

Martino

Han

et al. 2023

Preprint

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Complex dynamics of microbial communities underlie their essential roles in health and disease, but our understanding of these dynamics remains incomplete. To bridge this gap, longitudinal microbiome data are being rapidly generated, yet their power is limited by technical challenges in design and analysis, such as missing temporal samples, complex correlation structure, and high dimensionality. Here, we present TEMPoral TEnsor Decomposition (TEMPTED), the only time-informed dimensionality reduction method that extracts the underlying microbial dynamics while overcoming the statistical challenges posed by this type of data. TEMPTED facilitates beta-diversity analysis at both sample- and subject-level and enables the transfer of the learned low-dimensional representation from training data to unseen test data. In data-driven simulations, TEMPTED enables host phenotype classification at 90% accuracy compared to random using existing methods. In real data we show that TEMPTED deconvolutes the dynamics of the vaginal microbiome during pregnancy, allowing for the detection of microbial signatures associated with term and preterm births that are reproducible across datasets.

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EMBED: Essential Microbiome Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Cited by 4 publications

References 45 publications

Designing host-associated microbiomes using the consumer/resource model

Designing host-associated microbiomes using the consumer/resource model

Dynamic coexistence driven by physiological transitions in microbial communities

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

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