Data from: Comparing regression-based approaches for identifying microbial functional groups
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| Format: | Recurso digital |
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2025
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| _version_ | 1866902255996764160 |
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| author | Yu, Fang Tikhonov, Mikhail |
| author_facet | Yu, Fang Tikhonov, Mikhail |
| contents | <p>Microbial communities are composed of functionally integrated taxa, and identifying which taxa contribute to a given ecosystem function is essential for predicting community behaviors. This study compares the effectiveness of a previously proposed method for identifying ``functional taxa,'' Ensemble Quotient Optimization (EQO), to a potentially simpler approach based on the Least Absolute Shrinkage and Selection Operator (LASSO). In contrast to LASSO, EQO uses a binary prior on coefficients, assuming uniform contribution strength across taxa. Using synthetic datasets with increasingly realistic structure, we demonstrate that EQO's strong prior enables it to perform better in low-data regime. However, LASSO's flexibility and efficiency can make it preferable as data complexity increases. Our results detail the favorable conditions for EQO and emphasize LASSO as a viable alternative.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14796851 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data from: Comparing regression-based approaches for identifying microbial functional groups Yu, Fang Tikhonov, Mikhail Microbial Functional Groups Functional Taxa Identification Sparse Regression Ensemble Quotient Optimization Least Absolute Shrinkage and Selection Operator EQO LASSO Data-Driven Inference Phylogenetic Regularization <p>Microbial communities are composed of functionally integrated taxa, and identifying which taxa contribute to a given ecosystem function is essential for predicting community behaviors. This study compares the effectiveness of a previously proposed method for identifying ``functional taxa,'' Ensemble Quotient Optimization (EQO), to a potentially simpler approach based on the Least Absolute Shrinkage and Selection Operator (LASSO). In contrast to LASSO, EQO uses a binary prior on coefficients, assuming uniform contribution strength across taxa. Using synthetic datasets with increasingly realistic structure, we demonstrate that EQO's strong prior enables it to perform better in low-data regime. However, LASSO's flexibility and efficiency can make it preferable as data complexity increases. Our results detail the favorable conditions for EQO and emphasize LASSO as a viable alternative.</p> |
| title | Data from: Comparing regression-based approaches for identifying microbial functional groups |
| topic | Microbial Functional Groups Functional Taxa Identification Sparse Regression Ensemble Quotient Optimization Least Absolute Shrinkage and Selection Operator EQO LASSO Data-Driven Inference Phylogenetic Regularization |
| url | https://doi.org/10.5281/zenodo.14796851 |