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Physics Maths Engineering

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

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Yuanchen Zhao,

Yuanchen Zhao


Otto X. Cordero,

Otto X. Cordero


Mikhail Tikhonov

Mikhail Tikhonov


  Peer Reviewed

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© attribution CC-BY

  • 0

rating
450 Views

Added on

2024-12-15

Doi: https://doi.org/10.1371/journal.pcbi.1012590

Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology

Abstract

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 ensemble quotient optimization (EQO) approach). Modeling community function as a linear combination of individual species’ contributions 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.

Key Questions

1. Can linear regression-based methods identify functional groups in microbial communities?

Yes, the study shows that simple linear regression-based methods can successfully recover meaningful functional groups in microbial communities, even when the underlying ecological function is non-linear.

2. How do multi-group algorithms compare to single-group methods like EQO?

Multi-group algorithms offer an advantage over single-group methods like EQO. They can recover more information about the community structure, including upstream groups that indirectly affect the function of interest.

3. Are simpler or more complex methods better for identifying functional groups?

The study found that for small or noisy datasets, simpler linear methods can outperform more complex ones (like quadratic models) in identifying functional groups, even if the complex models predict the function better.

4. How does the performance of these methods change with different ecological scenarios?

The performance decreases as the degradation chain becomes longer or more complex. Groups affecting the function more directly (e.g., direct producers) are easier to recover than upstream groups.

5. What are the limitations of these linear regression-based methods?

The methods may struggle with high intra-group heterogeneity, inter-group promiscuity, non-monotonic functions, or strongly context-dependent species contributions. They also perform less well with small or noisy datasets.

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ARTICLE USAGE


Article usage: Dec-2024 to May-2025
Show by month Manuscript Video Summary
2025 May 125 125
2025 April 65 65
2025 March 71 71
2025 February 51 51
2025 January 112 112
2024 December 26 26
Total 450 450
Show by month Manuscript Video Summary
2025 May 125 125
2025 April 65 65
2025 March 71 71
2025 February 51 51
2025 January 112 112
2024 December 26 26
Total 450 450
Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology
copyright icon

© attribution CC-BY

  • 0

rating
450 Views

Added on

2024-12-15

Doi: https://doi.org/10.1371/journal.pcbi.1012590

Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology

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