Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat.
Dive into MicrobiomeStat's offerings for longitudinal data exploration in microbiome research. Designed for ease, collaboration, and reproducibility, it upholds openness to the scientific community.
MicrobiomeStat: Supporting Longitudinal Microbiome Analysis in R
MicrobiomeStat is a specialized tool for analyzing longitudinal and paired microbiome data. It can also be used for longitudinal analysis of other omics datatypes as long as the data are properly normalized and transformed. As a special case, cross-sectional and case-control microbiome data analysis is also supported.
Gitbook LLM Integration
Maximize the potential of MicrobiomeStat by utilizing its Gitbook wiki documentation in conjunction with Gitbook LLM (Large Language Model). In the top right corner of the Gitbook, you'll find the "Ask or Search" feature. Feel free to pose any questions you might have, such as "How to filter microbiome data in MicrobiomeStat?"
The Gitbook LLM, fine-tuned specifically for the MicrobiomeStat wiki, will provide you with the answers you need. For example:
By leveraging this feature, you can quickly find solutions and guidance for using MicrobiomeStat effectively in your research.
Dependencies
It depends on packages such as rlang, tibble, ggplot2, matrixStats, lmerTest, foreach, modeest, vegan, dplyr, pheatmap, tidyr, ggh4x, ape, GUniFrac, scales, stringr, rmarkdown, knitr, pander, and tinytex. We thank the authors of these packages, whose work has greatly facilitated the development of MicrobiomeStat.
We also acknowledge the work by the developers of microbiomeutilities, phyloseq, microbiomemarker, MicrobiomeAnalyst, microeco, EasyAmplicon, STAMP, qiime2, MicrobiotaProcess, q2-longitudinal, SplinectomeR, and coda4microbiome. Their contributions have significantly advanced the field and inspired us in creating MicrobiomeStat.
News
January 8th, 2024
Exciting news for our users! We have enhanced the color palette functionality in our package. You can now use predefined palette names like "lancet", "nejm", "npg", "aaas", "jama", "jco", and "ucscgb" directly in the palette parameter across most functions. This update simplifies the process of customizing color schemes in your data visualizations, making your plots more visually appealing and easier to interpret. For more details, please refer to the function documentation.
October 20th, 2023
We are pleased to announce that the Shiny interface for MicrobiomeStat is now officially available. This interface provides an interactive platform for microbiome data analysis, making it more accessible and user-friendly. The Shiny application can be accessed directly at this link. For users who prefer a local setup or require more customization, the Shiny application files and instructions are available on its dedicated GitHub repository.
Stay tuned for more updates and enhancements as we continue to improve MicrobiomeStat for our user community.
Contribute to the MicrobiomeStat Wiki
We welcome contributions from the community to enhance the MicrobiomeStat Wiki. If you would like to contribute, please follow these steps:
Fork the MicrobiomeStat Wiki repository on GitHub.
Make your desired changes, additions, or updates to the Wiki content.
Submit a pull request with a clear description of your changes.
Our team will review your pull request and merge the changes once approved. By contributing to the Wiki, you'll be helping to improve the documentation and resources available to the MicrobiomeStat user community. We appreciate your support in making this project better for everyone.
Resources
MicrobiomeStat Repository: GitHub
MicrobiomeStat Shiny Repository: GitHub
Shiny Web Application: Link
MicrobiomeStat Github Pages: Link
Function Documentation: Link
Gitbook Wiki: Link
Contributors
We would like to express our gratitude to the following contributors for their valuable contributions to the MicrobiomeStat project:
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