interactive Metagenome Data Analysis and Visualization (MetaDAVis) application analyzes 16S and whole metagenome sequence results at various levels (kingdom to species). It is a browser-based and user-friendly R Shiny application for researchers to analyze and visualize without programming proficiency. It comprises six functional analyses.
- Data Summary and Distribution
- Diversity analysis
- Dimension reduction
- Correlation analysis
- Heatmap
- Differential abundance (Two and multiple groups)
iMGDAViz is deployed at: https://www.gudalab-rtools.net/MetaDAVis
In this tutorial, we will go through the installation and usage of each functional module using the example dataset. The MetaDAVis is publicly available at (https://github.com/GudaLab/MetaDAVis). The example dataset is provided on the GitHub page (https://github.com/GudaLab/MetaDAVis/tree/main/www/example_data).
How to start MetaDAVis locally
Download the MetaDAVis application locally from the GitHub page (https://github.com/GudaLab/MetaDAVis).
• R (≥ 4.4.2), available at (https://www.r-project.org/)
• RStudio (≥ 2024.12.0) available at (https://posit.co/download/rstudio-desktop/)
• Bioconductor (≥ 3.20) and
• Shiny (≥ 1.10.0)
Start an R session using RStudio and run the following commands to install the shiny package: if Bioconductor version is less than 3.19. Bioconductor could be updated by :
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
install.packages("shiny")
To run MetaDAVis by the following commands in R:
library(shiny)
shiny::runGitHub("MetaDAVis","GudaLab")
Or Alternatively, download the source code from GitHub and run the following command in the R session using RStudio:
library(shiny)
runApp('/path/to/the/MetaDAVis-master', launch.browser=TRUE)
Tutorial for MetaDAVis https://github.com/GudaLab/MetaDAVis/blob/main/www/manual/MetaDAVis_manual.pdf
This Application was tested in Linux (Red Hat and Ubuntu) and Windows (10 and 11)
Jagadesan S, Guda C (2025) MetaDAVis: An R shiny application for metagenomic data analysis and visualization. PLoS ONE 20(4): e0319949. (https://doi.org/10.1371/journal.pone.0319949)