🌟 If you find MicrobiomeStat helpful, please consider giving us a star on GitHub! Your support greatly motivates us to improve and maintain this project. 🌟
The MicrobiomeStat package is a dedicated R tool for exploring longitudinal microbiome data. It also accommodates multi-omics data and cross-sectional studies, valuing the collective efforts within the community. One of its standout features is the ability to generate comprehensive microbiome analysis reports, spanning dozens of pages, with just a single click. This tool aims to support researchers through their extensive biological inquiries over time, with a spirit of gratitude towards the community’s existing resources and a collaborative ethos for furthering microbiome research.
News
📢 Update (January 8th): Enhancement in Color Palette Functionality
We are excited to announce a significant update to our color palette management in the majority of our functions. Users can now directly use predefined palette names such as “lancet”, “nejm”, among others, as values for the palette parameter. This update is part of our ongoing effort to enhance user experience and provide more intuitive and flexible options for data visualization.
Key highlights of this update: - Integration of the mStat_get_palette function across various functions in our package. - Allows users to easily specify color palettes by name, such as “lancet”, “nejm”, “npg”, “aaas”, “jama”, “jco”, and “ucscgb”. - Ensures backward compatibility and introduces a more user-friendly approach to selecting color schemes for visualizations. - Aims to enhance the visual appeal and interpretability of plots and heatmaps generated using our package.
This update is immediately available and we encourage users to explore these new options in their analyses. For detailed usage, please refer to the updated function documentation.
📢 Update (October 20th): The Shiny interface is now officially available for use. It is currently configured to handle analysis for small to medium-sized datasets. The interface can be accessed via this link.
In the event of server limitations affecting your analysis, or for those preferring to work with smaller modules, we recommend using our package directly. For a more flexible deployment, consider cloning our Shiny repository from here and deploying it on your local server or computer.
We appreciate your understanding and continued engagement.
Citations
General Citation for MicrobiomeStat
If you are using features beyond the linda and linda.plot functions, please cite as follows, until a preprint version is published:
Citation for Specialized MicrobiomeStat Functions
If you are using the linda, linda.plot, generate_taxa_association_test_long, generate_taxa_test_pair, generate_taxa_test_single, or generate_taxa_trend_test_long functions, please cite the following paper:
@article{zhou2022linda,
title={LinDA: linear models for differential abundance analysis of microbiome compositional data},
author={Zhou, Huijuan and He, Kejun and Chen, Jun and Zhang, Xianyang},
journal={Genome biology},
volume={23},
number={1},
pages={1--23},
year={2022},
publisher={BioMed Central}
}We will update the citation guidelines as soon as the preprint is published.
Important Note on CRAN Version
The MicrobiomeStat package is under continuous development. As a result, the most recent features have not yet been incorporated into the version available on the CRAN repository. The current CRAN version supports only the linda and linda.plot functions. For users who require a broader range of functionalities, especially those related to the analysis of longitudinal data, it is advisable to install the development version directly from GitHub. This process necessitates the prior installation of the devtools package.
install.packages("devtools")Once devtools is installed, you can install MicrobiomeStat from GitHub using the following command:
devtools::install_github("cafferychen777/MicrobiomeStat")Table of Contents
Online Tutorials
📘 Explore MicrobiomeStat Tutorials
MicrobiomeStat provides a comprehensive suite of tools for microbiome data analysis, encompassing a variety of functions from data input to visualization.
To acquaint users with MicrobiomeStat, we offer an extensive online tutorial on GitBook. The tutorial covers the following areas:
-
Installation and Configuration Instructions
- These guidelines help ensure that your setup is correctly configured and optimized.
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Analysis Demonstrations Based on Real-World Scenarios
- These demonstrations provide practical insights and skills.
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Code Examples for Practice
- These examples allow users to familiarize themselves with MicrobiomeStat coding practices.
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Guides for Interpreting Results and Creating Visualizations
- These guides help users understand and effectively present their data.
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Answers to Frequently Asked Questions
- This section provides quick solutions to common questions.
Discovering MicrobiomeStat
The realm of microbiome research is intricate and continually advancing. The analytical tools selected can play a crucial role in navigating through the research journey. In this scenario, MicrobiomeStat aims to be a supportive companion.
Acknowledgements
We stand on the shoulders of giants with MicrobiomeStat, and our heartfelt gratitude goes out to the diligent and brilliant developers of the dependencies that our package relies on. Their remarkable efforts have not only made our work possible but have also significantly elevated the standards of computational tools available to the scientific community:
- Core Dependencies:
- R (>= 3.5.0), rlang, tibble
- Imported Packages:
- ggplot2, matrixStats, lmerTest, foreach, modeest, vegan, dplyr, pheatmap, tidyr, ggh4x, ape, GUniFrac, scales, stringr, rmarkdown, knitr, pander, tinytex
- Suggested Packages:
- ggrepel, parallel, ggprism, aplot, yaml, biomformat, Biostrings
Furthermore, we extend our deepest appreciation and respect to the trailblazers in the microbiome research community who have created and maintained the following remarkable tools. Their pioneering work has laid down paths through the complex landscape of microbiome data analysis, and we are truly honored to walk alongside:
-
microbiomeutilities,phyloseq,microbiomemarker,MicrobiomeAnalyst,microbiomeeco,EasyAmplicon,STAMP,qiime2, andMicrobiotaProcess
Their contributions inspire us to continue improving and expanding the capabilities of MicrobiomeStat, and we sincerely hope our humble addition proves to be a useful complement to the incredible array of tools already available to researchers.
User Support
MicrobiomeStat is designed with users in mind. Comprehensive documentation and tutorials are available to assist both novice and experienced researchers. Before posting a question or issue, we encourage users to check previous questions and issues to see if the topic has already been addressed.
In case you have specific comments or questions about a particular function’s documentation and you find that the RStudio’s search box leads to a 404 error, you can directly access the function’s documentation at https://cafferychen777.github.io/MicrobiomeStat/reference/index.html.
If your question or issue has not been previously addressed, feel free to open a new issue on GitHub. We are here to help you navigate any challenges you may encounter.
Ongoing Development
The MicrobiomeStat tool is under continuous development to incorporate user feedback and to keep up with advancements in the field. 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.
The Shiny interface will be maintained and updated along with the main package. 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.
Collaborative Development
MicrobiomeStat is an open-source tool, and we highly value contributions from the community. If you have suggestions, improvements, or feedback for future development directions and feature additions, pull requests are welcomed, and you can also share your ideas in the discussion area of our GitHub repository. Engage with other community members and help us make MicrobiomeStat an even more useful tool for microbiome research.
Conclusion
MicrobiomeStat aims to serve as a dependable and efficient resource for microbiome data analysis. We extend an invitation to those who value open-source collaboration to join our community and contribute to its ongoing development.
| Feature | Description |
|---|---|
| Data Import and Conversion | Accommodates multiple input formats from platforms such as QIIME2, Mothur, DADA2, Phyloseq and others. |
| Cross-sectional Study Analysis | Offers thorough analysis for cross-sectional studies. |
| Paired Sample Analysis | Provides tools for paired samples analysis. |
| Longitudinal Study Analysis | Facilitates exploration of the temporal dynamics of the microbiome. |
| Report Generation Functions | Includes individual report functions for cross-sectional, paired, longitudinal study designs. Development of a Shiny interface for one-click reporting is underway. |
| Visualization Capabilities | Supports a broad range of visualization styles. |
| Ongoing Development | Committed to continuous refinement of existing features and addition of new functionalities. |
This approach ensures that users can effortlessly navigate to the specific sections of the MicrobiomeStat documentation, garnering detailed information and guidelines for diverse analysis types. The structure and accessibility assist users in leveraging MicrobiomeStat effectively for their microbiome data analysis needs.
Demo Reports
For those interested in seeing MicrobiomeStat in action, we have prepared demo reports tailored to different study designs:
- Cross-sectional Study Design: Reporting Microbial Analysis with MicrobiomeStat
- Paired Samples Analysis: Reporting Microbial Analysis with MicrobiomeStat
- Longitudinal Study Design: Microbiome Analysis Automation with MicrobiomeStat
We encourage you to explore these examples and discover the powerful capabilities of our tool.
Assistance & Contact Information
For assistance or inquiries, feel free to reach out to:
| Name | |
|---|---|
| Dr. Jun Chen | Chen.Jun2@mayo.edu |
| Chen Yang | cafferychen7850@gmail.com |
Engage in Our Discord Community
Join our Discord community to stay on top of the latest updates, developments, and enhancements in MicrobiomeStat. Be part of vibrant discussions, ask questions, share insights, and avail support from peers and experts alike:
Join the MicrobiomeStat Discord Server!
In our Discord server, an automated bot keeps you informed about every package and tutorial update, ensuring you never miss out on new features, improvements, and learning materials. Our active community thrives on collaboration, feedback, and continuous learning, making it an invaluable space for both novice and experienced researchers navigating the world of microbiome data analysis. Stay connected, stay informed, and let’s advance the field of microbiome data analysis together!
