Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function.
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 represen...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012590 |
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| author | Yuanchen Zhao Otto X Cordero Mikhail Tikhonov |
| author_facet | Yuanchen Zhao Otto X Cordero Mikhail Tikhonov |
| author_sort | Yuanchen Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5d7e99bc1c64d2690fcf6e5ca0dfe1d |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-b5d7e99bc1c64d2690fcf6e5ca0dfe1d2024-12-01T05:30:29ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-11-012011e101259010.1371/journal.pcbi.1012590Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function.Yuanchen ZhaoOtto X CorderoMikhail TikhonovMicrobial 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.https://doi.org/10.1371/journal.pcbi.1012590 |
| spellingShingle | Yuanchen Zhao Otto X Cordero Mikhail Tikhonov Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. PLoS Computational Biology |
| title | Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. |
| title_full | Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. |
| title_fullStr | Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. |
| title_full_unstemmed | Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. |
| title_short | Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function. |
| title_sort | linear regression based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function |
| url | https://doi.org/10.1371/journal.pcbi.1012590 |
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