Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat.

Dive into MicrobiomeStat's offerings for longitudinal data exploration in microbiome research. It is designed for ease, collaboration, and 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.

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.

Resources:

  • MicrobiomeStat Repository: GitHub
  • MicrobiomeStat Shiny Repository: GitHub
  • Shiny Web Application: Link
  • MicrobiomeStat Github Pages: Link
  • Function Documentation: Link
  • Gitbook Wiki: Link