Annotation-free discovery of functional groups in microbial communities

Shan, Xiaoyu; Goyal, Akshit; Gregor, Rachel; Cordero, Otto X.

doi:10.1038/s41559-023-02021-z

Cited by 13 publications

(19 citation statements)

References 49 publications

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“…(C) We use the synthetic data as input for three families of regression-based algorithms: the EQO of Ref. [25] (which groups species into two groups), and two families we call K-means and Metropolis (see text), which can return any specified number of groups. The panel shows representative outputs of these algorithms for N = 3 metabolites and for the number of groups indicated on the left.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Shan et al . [25] proposed a method (EQO) that appears surprisingly successful given the simplicity of the linear-regression ansatz at its core. This approach takes as input only species abundances and the value of the function, potentially making it highly appealing for microbial applications where such data can be obtained relatively easily, whereas additional information, such as interaction patterns, is difficult to establish.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, of the three examples used in Shan et al . [25], only one (the data from [10]) had an independently established grouping against which the output could be compared (previously investigated in [9]). This issue is more general [1, 23, 26].…”

Section: Introductionmentioning

confidence: 99%

“…Empirical validation of grouping methods often relies on researchers’ intuition, evaluating whether the groups “make biological sense.” Such intuition-based validation can be compelling (e.g., Shan et al . [25] found that, when applied to the TARA Oceans [15,29] dataset with nitrate as the observable of interest, EQO appropriately grouped aerobic and anaerobic ammonia oxidizers). However, to systematically compare or improve such methods, a quantitative assessment of their performance is required.…”

Section: Introductionmentioning

confidence: 99%

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Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function

Zhao,

Cordero,

Tikhonov

2024

Preprint

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Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the EQO approach of Shan et al., 2023). This success seems puzzling, since modeling community function as a linear combination of contributions of individual species appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the "correct" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function

Zhao,

Cordero,

Tikhonov

2024

Preprint

Self Cite

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“…These in-vitro systems allow one to quantify reproducible similarities and differences in community composition when one systematically manipulates individual components of the metabolic environment one at a time (Estrela, Sánchez, and Rebolleda-Gómez 2021; Estrela et al 2021). A major discovery from this body of work is that microbial communities assembled in identical metabolic environments converge to reproducible compositions at coarse phylogenetic and functional levels, despite variability in species composition (Goldford et al 2018; Shan et al 2023). In parallel, similar patterns of compositional variability and reproducibility have been observed in many naturally assembled microbiomes, ranging from the mouse gut (Turnbaugh et al 2009) to the open ocean (Louca, Parfrey, and Doebeli 2016).…”

Section: Introductionmentioning

confidence: 99%

Metabolic similarity and the predictability of microbial community assembly

Vila,

Goldford,

Estrela

et al. 2023

Preprint

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When microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

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