This R/Bioconductor package provides tools to simulate (longitudinal) time series data from popular models in microbial ecology. The homepage provides tutorials and references for the implemented models:
- Self-organised instability (SOI)
- Hubbell's neutral model
- generalized Lotka-Volterra (gLV)
- Ricker model (discrete gLV)
- Stochastic logistic model
- Consumer-resource model
These methods can be used for in silico studies of microbial
community dynamics or multi-omic or host-microbiome interactions. The
miaSim package supports the Bioconductor multi-assay data science
framework for multi-omic data
integration and time series analysis, and utilizes the
(Tree)SummarizedExperiment data container.
The accompanying miaSimShiny package allows users to explore the parameter space of their models in real-time in an intuitive graphical interface.
You can experiment with miaSimShiny online.
Contributions and acknowledgments
< 76C3 a id="user-content-contributions-and-acknowledgments" class="anchor" aria-label="Permalink: Contributions and acknowledgments" href="#contributions-and-acknowledgments">You can find us online from Gitter.
Contributions are very welcome through issues and pull requests at the
development site. We follow a git
flow kind of approach. Development version should be done against the
main branch and then merged to release for release.
(https://guides.github.com/introduction/flow/)
We are grateful to all contributors.
This research has received funding from
- the Horizon 2020 Programme of the European Union within the FindingPheno project under grant agreement No 952914.
- Research Council of Finland (grant 330887)
Kindly cite this work as follows:
Gao et al. (2023). Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.14129
For citation details, see R command citation("miaSim").
Code of conduct Please note that the project is released with a Bioconductor Code of Conduct. By contributing to this project, you agree to abide by its terms.