README.md

UHRIonStarApp

Ultra High Resolution (UHR)-IonStar is an MS1-based quantitative method for label-free proteomics experiments, devised to address issues related with quantitative precision, missing data, and false-positive discovery of protein changes in large-cohort analysis.

UHR-IonStar, R shiny-based web application

Ultra High Resolution (UHR)-IonStar is an MS1-based quantitative method for label-free proteomics experiments, devised to address issues related with quantitative precision, missing data, and false-positive discovery of protein changes in large-cohort analysis.

UHR-IonStar app is a R shiny-based interactive application designed for processing, visualization and analysis of quantitative proteomics data generated by UHR-IonStar.

UHR-IonStar Installation (less than 20 minutes)

R (Version 4.0.5 or above) is required for Windows 10 or MacOS.\ Due to serveral R packgages from R Bioconductor and a package from GitHub cannot be automatically installed with the same time of UHR-IonStar R package installation, please install the following packages in advance:

install.packages(c('devtools','shiny','shinydashboard'))
library('devtools')
devtools::install_github("vqv/ggbiplot")

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install()

BiocManager::install(c("MSGFplus","limma","TCGAbiolinks","clusterProfiler", "Biobase", "DO.db",
                       "AnnotationHub","mzR","MSnbase","xcms","CAMERA"))
install.packages("patRoon", repos = "https://rickhelmus.github.io/patRoonDeps/", type = "binary")

Then, you can install UHR-IonStar with the following line:

devtools::install_github("JunQu-Lab/UHRIonStarApp")

Troubleshooting

Run UHR-IonStar

If no error pops up, the UHR-IonStar web app could be started with the following codes:

library(UHR.IonStar)
UHR.IonStar::UHRIonStarShiny()

Manual

User can download the manual either at the mainpage of UHR-IonStar application or at the github directory UHRIonStarApp/inst/shiny/UHRIonStar.

Related Articles

Shen, Xiaomeng, et al. "IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts." Proceedings of the National Academy of Sciences 115.21 (2018): E4767-E4776.

Wang, Xue, et al. "Ultra-High-Resolution IonStar Strategy Enhancing Accuracy and Precision of MS1-Based Proteomics and an Extensive Comparison with State-of-the-Art SWATH-MS in Large-Cohort Quantification." Analytical chemistry 93.11 (2021): 4884-4893.



JunQu-Lab/UHRIonStarApp documentation built on July 2, 2022, 12:47 a.m.