Comparing regression-based approaches for identifying microbial functional groups.

IF 2 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Physical biology Pub Date : 2025-05-30 DOI:10.1088/1478-3975/addc2a

Fang Yu, Mikhail Tikhonov

引用次数: 0

Abstract

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.

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比较基于回归的微生物功能群鉴定方法。

微生物群落是由功能整合的分类群组成的，确定哪些分类群对给定的生态系统功能有贡献是预测群落行为的必要条件。本研究比较了先前提出的识别“功能分类群”的方法，即集合商优化（EQO）与基于最小绝对收缩和选择算子（LASSO）的潜在更简单的方法的有效性。与LASSO相比，EQO对系数使用二元先验，假设跨分类群的贡献强度是一致的。使用结构越来越真实的合成数据集，我们证明了EQO的强先验使其在低数据状态下表现更好。然而，随着数据复杂性的增加，LASSO的灵活性和效率可以使其成为首选。我们的结果详细说明了EQO的有利条件，并强调LASSO是可行的替代方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical biology 生物-生物物理

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.