Python packaging for CPTAC data
Project description
cptac
This project is intended to facilitate accessing and interacting with cancer data from the National Cancer Institute CPTAC consortium, which characterizes and studies the proteogenomic landscape of tumors. These cancer studies are downloadable via our Python package as native dataframe objects and can therefore be integrated very quickly and easily with other Python-based data analysis tools. Follow our walkthrough tutorials for a demonstration of ways to use our system.
Setup instructions can be found in docs/setup.md.
Tutorials
Tutorials for this package describe how to use the package functions for research with the provided data. All the tutorials are written in Python using the interactive Jupyter notebooks. If you are unfamiliar with Jupyter, follow the instructions given at jupyter.org/install. You will then be able to run our tutorials as interactive, exploratory data analyses.
- Tutorial 1: CPTAC data introduction
- Tutorial 2: Using pandas to work with cptac dataframes
- Tutorial 3: Joining dataframes with cptac
- Use Case 1: Comparing transcriptomics and proteomics
- Use Case 2: Looking for correlation between clinical attributes
- Use Case 3: Associating clinical variables with omics data
Requirements
This package is intended to run on Python 3.6 or greater with pandas version 0.25.0 or greater. In the tutorials, we use seaborn 0.9.0 for data visualization.
License
This package contains LICENSE.md document which describes the license for use. Please note the difference between the license as it applies to code versus data.
Contact
This package is maintained by the Payne lab at Brigham Young University, https://payne.byu.edu
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