Exploring correlations in an isobaric labeling mass spectrometry-based proteomic data set.
Developed by Proteome Proteomicsson 101 with educational purposes.
The notebook was created in Jupyter Lab on Ubuntu 20.04 running Python 3.8. The workflow depends on third-party libraries that can be installed via pip:
pip install scipy numpy pandas matplotlib seaborn
Proteomic data set has been reported in the article by Hultqvist et al. LC-MS files have been deposited at PRIDE archive, and the relative protein abundance table from the proteomic analysis can be found in this GitHub repository.