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QuantQC is a package for quality control (QC) of single-cell proteomics data. It is optimized to work with nPOP, a method for massively parallel sample preparation on glass slides.

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QuantQC: nPOP sample preparation evaluation pipline

 

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Requirements

This application has been tested on R >= 3.5.0, OSX 10.14 / Windows 7/8/10. R can be downloaded from the main R Project page or downloaded with the RStudio Application.

There are two required packages that are not installed by default.

  1. Seurat
  2. DIANN r package

To install the QuantQC package, run (you may need to install devtools first):

devtools::install_github("https://github.com/SlavovLab/QuantQC")
library(QuantQC)

Reproducing the data analysis

  1. Download all the data reports from the "search" section of MassIVE MSV000093494.

  2. Download the "AnalysisFromProtocol" folder from the QuantQC Github page. In the R script "Analysis.R", change the path names for the relevant meta data files. There are two types. The linker file which connects the file names from the MS runs to the wells of the plate they were run from, and the cellenONE cell sorting files. These files are origninally named XXX_isolated.xls and are auto generated by the cellenONE in the folder created when the user performs cell sorting to the glass slide.

  3. Run the Analysis.R script line by line following the comments in the script.

About the project

The manuscript is freely available on bioRxiv: Leduc et al., 2023.

For more information, contact Slavov Laboratory or directly Andrew Leduc

License

The QuantQC code is distributed by an MIT license.

Contributing

Please feel free to contribute to this project by opening an issue or pull request.


Help!

For any bugs, questions, or feature requests, please use the GitHub issue system to contact the developers.

About

QuantQC is a package for quality control (QC) of single-cell proteomics data. It is optimized to work with nPOP, a method for massively parallel sample preparation on glass slides.

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