README.md

In JunQu-Lab/MAsP: R Shiny-based Application for Micro-scaffold Assisted Spatial Proteomics (MASP)

MAsP, R Shiny-based web application

Micro-scaffold Assisted Spatial Proteomics (MASP) is a novel method for spatial proteomic studies. MASP precisely procures spatial information by precise tissue spatial compartmentalization using a 3D-printed micro-scaffold, and accurately/sensitively quantifies thousands of proteins in a whole-tissue slice.

MAsP is a R Shiny-based interactive application designed for processing, visualization and analysis of quantitative proteomics data generated by MASP.

MAsP Installation (less than 5 minutes)

R (Version 4.0.5 or above) is required for Windows 10 or MacOS.

Users can install MAsP with the following line in R:

install.packages("devtools")
library(devtools)
devtools::install_github("JunQu-Lab/MAsP")

Troubleshooting

• R will occasionally ask users whether they want to update any dependent packages that have a new version. We recommend that you update them all.
• Rtools is also required to install R packages.
• During the installation of dependent packages, sometimes R would inquire, "Do you want to install from sources the packages which need compliation?" If users select "Yes" and receive an error, selecting "No" is equally acceptable.

Run MAsP

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

library(MAsP)
MAsP::MAsPShiny()

Manual

User can download the manual either at the mainpage of MAsP app or at the github directory MAsP/inst/shiny/MAsP.

Test data

The files provided in this Google Drive link can be uploaded in "Data upload" section in the App, please refer to the manual for details.

The files are: - "MASP_data.csv", the abundance ratios of the 5019 proteins quantified by MASP with spatial locations; - "Locations.csv", the spatial locations of the micro-specimans; - "Brain_cover.png", a brain cover image.

Related Articles

Whole-tissue Mapping of >5000 proteins by Micro-scaffold Assisted Spatial Proteomics(MASP), preprint.

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.

Fonville, Judith M., et al. "Robust data processing and normalization strategy for MALDI mass spectrometric imaging." Analytical chemistry 84.3 (2012): 1310-1319.

JunQu-Lab/MAsP documentation built on Nov. 23, 2022, 1:51 a.m.