Skip to content

A large-scale spectral counting data analysis using PAW pipeline, edgeR, R, and Jupyter notebooks

License

Notifications You must be signed in to change notification settings

pwilmart/Smith_SpC_2018

Repository files navigation

Smith_SpC_2018

A large-scale spectral counting data analysis using PAW pipeline, edgeR, R, and Jupyter notebooks.

Phil Wilmarth, OHSU

February 2019

The data is from a recent study where human retinal and choroidal endothelial cells were compared. The study was 5 donor eyes where retinal and choroidal cells were collected and cultured in a paired design. The cell lysates from each of the 10 cell cultures were profiled using large-scale separations with a fast-scanning linear ion trap. There were about half a million MS2 scans per sample for a dataset size of a little over 5 million. The data are available at the PRIDE archive (PXD005972).

There is a direct link to the rendered notebook HTML file.


A few relevant files from the archive are present in the repository:

  • analysis_overview.pptx - summary of the data analysis steps
  • quant_protein_summary_8.txt - a grouped protein summary file
  • edgeR_input.txt - data extracted from results file for edgeR analysis
  • edgeR_results.txt - statistical testing results
  • HCEC_HREC_quant_protein_summary_sprot.xlsx - final summary sheet
    • proteomics data from quant_protein_summary_8.txt
    • statistical results from edgeR
    • extra protein annotations

About

A large-scale spectral counting data analysis using PAW pipeline, edgeR, R, and Jupyter notebooks

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published