or-dodson/single-cell-proteomics

Group Project for BIO 465

Bio 465 Project

By Evan Dodson, Madelyn Grose, Corbin Day, Robert Oldroyd, and Bryce Lunceford

Reproducibility Instructions

Additional detail can be found under the Additonal Information section.

1. Install Docker Desktop on your machine.

2. Run docker using the command docker run -it -p 8888:8888 evandodson/sc_proteomics /root/single-cell-proteomics/runJupyter.sh
3. This will start a Jupyter Lab server. To connect to the server, copy and paste the link given in the terminal that looks like http://127.0.0.1:8888/lab?token=... into your browser.
4. Once you have access to the Jupyter Lab server, the notebooks need only be run to reproduce the results that we obtained.

Additional Information

Installing Docker

Docker can easily be installed on a variety of Operating Systems. For convenience, we provide instructions for Ubuntu, MacOS, and Windows.

• Ubuntu
  • Run the command sudo apt install docker.io -y && sudo usermod -aG docker $(whoami) && sudo reboot
• MacOs
  • Follow these instructions
• Windows
  • Follow these instructions

Downloading Data

Cleaned data is included in the docker image, but may also be downloded from Box.

Additionally, raw data files can be downloaded from Box if desired.

File Descriptions

Descriptions of files contained in the repository.

• analyze_data.ipynb: Performs analysis on the data to show statistical significance.
• combine_parsed_psm.ipynb: A notebook that takes the mzml-psm files and combines them into all_datatype.tsv files, 1 per datatype, using combined_psm_parser.py which does most of the processing
• combine_psm_mzml.py
• combined_psm_parser.py: A script to fill in missing ions (none intensity) and bin the data
• data_parser.py: Used by parse_data.ipynb to take parsed psm files (data/parsed_psm) and combines them by data type (e.g. all_sc.tsv)
• docker: Contains the Dockerfile for this project
  • Dockerfile: Used by Docker
• figures: Folder that contains code to generate the figures
  • figure3.ipynb: Contains a function to make figure 3 given proper data.
  • make_figure3.ipynb: The notebook that generated Figure 3.
• ion_tables.ipynb: Contains code to create ion tables.
• IonCountChiSquared.ipynb: Performs $\chi^2$ analysis on the data to show statistical significance.
• parse_raw_data.ipynb: A notebook for taking the raw mzml and psm files and combining them into the more usable format using functions defined in combine_psm_mzml.py
• README.md: This file
• runJupyter.sh: Starts the Jupyter Lab server in the Docker container.