CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data

Irene Creus-Martí, Andrés Moya and Francisco J Santonja

PLOS Computational Biology, 2026, vol. 22, issue 6, 1-17

Abstract: In this paper we present CoDaLoMic, an R package for analyzing longitudinal and compositional microbiome datasets. The CoDaLoMic package implements three models specifically designed for the analysis of microbiome data that are both compositional and longitudinal. Unlike many existing methods that focus solely on pairwise interactions, CoDaLoMic also captures interactions among groups of bacteria, providing a more robust methodological framework for studying microbial relationships at the community level. In addition, the package facilitates the analysis of microbiome variability in relation to host health status and allows for the identification of groups of taxa that exhibit similar temporal dynamics. Working with time series data makes it possible to understand not only the current state of a microbial community but also its dynamics over time, which is essential for identifying patterns of ecological succession, detecting events of dysbiosis or recovery, and inferring potential causal relationships between taxa. On the other hand, focusing on interactions among groups of bacteria, rather than analyzing only pairwise relationships, enables a more integrated and functionally meaningful view of the microbiome. Many key ecological functions are the result of the collective behavior of functionally related groups of taxa. Two datasets have been considered in CoDaLoMic, one real and one simulated. The real dataset contains the information of the genera present in the microbiome of the Blatella germanica cockroach at 105 time points. The simulated dataset is defined taking Lotka-Volterra structure into account. CoDaLoMic is available at CRAN.Author summary: Understanding how the microbiome changes over time is fundamental to unraveling its role in health and disease, as microbial communities influence processes such as digestion, immune response, and pathogen resistance. In this study, we present CoDaLoMic, a user-friendly R package designed to analyze longitudinal and compositional microbiome data, accounting not only for pairwise bacterial interactions but also for collective interactions among functional groups of taxa. This is crucial because many ecological functions emerge from the joint behavior of microbial groups rather than isolated species. CoDaLoMic includes advanced models that capture temporal dynamics, enabling the detection of ecological succession patterns, dysbiosis and recovery events, and potential causal relationships among microorganisms. Additionally, the package facilitates the identification of groups of bacteria with similar temporal dynamics, which may reveal shared functions or relevant ecological interactions. We tested CoDaLoMic with a real dataset of the gut microbiome of cockroaches across 105 time points, as well as a simulated dataset based on classical ecological models. The package produces publication-ready plots and tables, helping researchers interpret and communicate their biological findings. CoDaLoMic is freely available on CRAN and aims to support scientists from various disciplines in the detailed and dynamic study of microbial communities.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014328

DOI: 10.1371/journal.pcbi.1014328

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