This R Shiny web application allows users to perform differential expression analysis and visualize transcriptome data, making it easier to interpret gene expression patterns. The app supports uploading CSV/XLSX files, performing statistical analysis, and generating various plots to aid in the analysis.
- Authors : Sarath kumar R and Dr. J. Sreekumar.
- Website : https://krsarath.shinyapps.io/shinyvistaApp/ shinyvista Ver. 1.0
- Differential Expression Analysis: Identify differentially expressed genes between conditions.
- Data Upload: Upload transcriptome data in CSV or XLSX format.
- Interactive Visualizations:
- Volcano plots
- MA plots
- Heatmaps
- Venn diagrams
- Downloadable Results: Export analysis results and plots.
- R (version 4.x or higher)
- R Shiny (version 1.6 or higher)
- Additional R packages:
ggplot2,DESeq2,edgeR,Noiseq,dplyr,shiny,shinyWidgets,readxl,readr
Clone the repository and navigate to the project directory:
``bash
- git clone https://github.com/krsarath/shinyapp.git
- cd shinyapp
install.packages(c("shiny", "ggplot2", "DESeq2", "edgeR", "Noiseq", "dplyr", "shinyWidgets", "readxl", "readr"))
To run the app locally, use the following R command shiny::runApp('path_to_your_app_directory')
- Upload RNA-seq Count File: Use the 'Upload' tab to select your count file.
- Perform Differential Expression Analysis: Navigate to the 'Differential Expression' tab, choose your settings, and click 'Run'.
- Visualize GO Terms: In the 'GO Bubble Plot' tab, upload your DE results and GO files, then generate the plot.
- Create Venn Diagrams: Use the 'Venn Diagram' tab to upload multiple DE result files and compare them visually.
- View Results: Explore the generated plots and tables.
- Download: Export the results and plots for further analysis.
Example datasets are included in the data directory to help you get started.matrix.counts (copy).zip
This project is licensed under the MIT License - see the LICENSE file for details.
For any questions or issues, please contact
