Correction - Correction: Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations - apology for omission

Sep 29, 2026

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This correction states that a closely related study was inadvertently omitted from the original article. The authors explain that Li et al developed BEEM, an iterative expectation-maximization method for generalized Lotka-Volterra models under sparsity assumptions for the interaction matrix. They clarify BEEM is not suitable for systems with fewer than six species, whereas iLV is designed for relatively small species without requiring sparsity. The authors apologize for failing to cite Li et al.

LanguageEnglish

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Correction

Main omission and related study (Li et al.)

Method applicability: BEEM vs iLV

Apology and references

CORRECTION Correction: Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations Fengzhu Sun, Yue Huang, Tianqi Tang, Xiaowu Dai After the publication of this article [ 1] the authors became aware that a closely related study was inadvertently omitted. Specifically, the work by Li et al [2] developed an iterative expectation-maximization (EM) algorithm, BEEM, to estimate parameters in generalized Lotka-Volterra models under the assumption of sparsity in the interaction coefficient matrix. As noted by Li et al. [2], BEEM is not suitable for systems with a small number of species, particularly fewer than six. In contrast, the current version ofiLV [ 1] is designed to work with a relatively small number of species without assuming sparsity in the interaction matrix. Therefore, the application scenarios of BEEM and iLV are complementary, and their performance cannot be directly compared. The authors apologize for the oversight in not citing Li et al. [2] in our manuscript [ 1] . References 1. Huang Y, Tang T, Dai X, Sun F. Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations. PLoS Comput Biol. 2025;21(11):e1013691 . [https://doi.org/10.1371/journal.pcbi.1013691](https://doi.org/10.1371/journal.pcbi.1013691) PMID: 41202104 2. Li CH, Chng KR, Kwah JS, Av-Shalom TV, Tucker-Kellogg L, Nagarajan N. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome. 2019;7(1):118 . [https://doi.org/10.1186/s40168-019-0729-z](https://doi.org/10.1186/s40168-019-0729-z PMID:)[ PMID:](https://doi.org/10.1186/s40168-019-0729-z PMID:) 31439018 OPEN ACCESS Citation: Sun F, Huang Y, Tang T, Dai X (2026) Correction: Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations. PLoS Comput Biol 22(1): e1013876 . [https://doi.org/10.1371/journal.pcbi.1013876](https://doi.org/10.1371/journal.pcbi.1013876) [Published:](Published: January 6)[ January 6](Published: January 6) , 2026 Copyright: © 2026 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PLOS Computational Biology | [https://doi.org/10.1371/journal.pcbi.1013876](https://doi.org/10.1371/journal.pcbi.1013876) January 6, 2026 1 / 1

FAQs

What was corrected in the published article?

The authors report that a related study by Li et al. was inadvertently omitted from the manuscript and should have been cited.

What method did Li et al. introduce and what assumption does it rely on?

Li et al. developed BEEM, an iterative expectation-maximization algorithm for generalized Lotka-Volterra models under an interaction-matrix sparsity assumption.

How do BEEM and iLV differ in terms of usable species counts?

BEEM is not suitable for systems with a small number of species, particularly fewer than six, while the current iLV version is designed to work with relatively small species without assuming sparsity.

Sep 29, 2026

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