# MSstatsShiny This repository contains the code for the R Shiny app MSstatsShiny, which utilizes MSstats, MSstatsTMT, and MSstatsPTM to analyze proteomics experiments. ## Availability The application is available both online and locally, via Bioconductor or Github. ### Online The online application is located at [http://www.msstatsshiny.com/](http://www.msstatsshiny.com/). The online version is constrained to processing only input files smaller than 100 MB. Due to this, we recommend processing large datasets using a local installation. ### Bioconductor To install the application via Bioconductor, please use the following steps. 1. Download [R](https://www.r-project.org/) and [RStudio](https://www.rstudio.com/products/rstudio/download/) - [How to](https://rstudio-education.github.io/hopr/starting.html). **Note R version must be >= 4.3** 2. Intall the package via [Bioconductor](https://bioconductor.org/packages/release/bioc/html/MSstatsShiny.html) ### Github To install the application via Github, please use the following steps. 1. Download [R](https://www.r-project.org/) and [RStudio](https://www.rstudio.com/products/rstudio/download/) - [How to](https://rstudio-education.github.io/hopr/starting.html). **Note R version must be >= 4.3** 2. Install the package by executing `devtools::install_github("Vitek-Lab/MSstatsShiny")` in the console. 3. Run the application by executing `library(MSstatsShiny)` and `launch_MSstatsShiny()` or `MSstatsShiny::launch_MSstatsShiny()` in the console. ## Citation To cite this application please use the corresponding publicaiton in the journal of proteome research. **MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments** Devon Kohler, Maanasa Kaza, Cristina Pasi, Ting Huang, Mateusz Staniak, Dhaval Mohandas, Eduard Sabido, Meena Choi, and Olga Vitek. Journal of Proteome Research 2023 22 (2), 551-556 DOI: 10.1021/acs.jproteome.2c00603