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cptac

Python packaging for CPTAC data

https://github.com/paynelab/cptac

Science Score: 62.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    8 of 38 committers (21.1%) from academic institutions
  • ✓
    Institutional organization owner
    Organization paynelab has institutional domain (payne.byu.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Python packaging for CPTAC data

Basic Info
  • Host: GitHub
  • Owner: PayneLab
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 562 MB
Statistics
  • Stars: 96
  • Watchers: 7
  • Forks: 27
  • Open Issues: 13
  • Releases: 53
Created over 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

NOTE With current release

We are having difficulty with the API on Zenodo and are working to find a better host for our data. Please be patient while we fix these issues.

Easy access to CPTAC data

This software provides easy access to cancer data from the National Cancer Institute's CPTAC program, which characterizes and studies the proteogenomic landscape of tumors. We implement the software as a Python package called cptac, but you can seamlessly use it in an R environment with the help of the reticulate package (demonstrated in Tutorial 6). Our package is installed in one step with pip: pip install cptac See the Installation section below if you have further questions.

The package gives you the data as pandas DataFrame objects in Python. If you are using R, reticulate converts the tables to data.frame objects. By providing the tables natively in your programming environment, we eliminate the need for parsing and formatting, allowing you to quickly feed the data into whatever analysis code you have written. Follow our walkthrough tutorials and use cases for examples of how to use the software.

Additionally, the software automatically handles data downloading, storage, and updates. You need only to tell it which datasets you want downloaded, and it will automatically get the data without requiring you to write any HTTP requests or database queries.

Installation

This package is intended to run on Python 3.6 or greater. If you plan on interfacing with it from R via reticulate, you must still have Python installed on your computer, and download the package into that Python environment.

Installing Python

If you do not already have Python installed on your computer, we suggest using either the standard Python distribution or the Anaconda distribution. Follow the installation instructions at the respective links. The Anaconda distribution allows you to set up multiple distinct Python environments and comes with many useful Python packages pre-installed. For more information, see the Ananconda documentation.

Installing the cptac package

We distribute the package through the Python Package Index (PyPI), so regardless of which Python distribution you are using, you install the package using the pip program: pip install cptac If you are using the Anaconda distribution of Python, this will install cptac to the currently active environment as long as pip is available in that environment, which it would be by default. If pip is not installed in your environment, you can install it with conda install -c anaconda pip. Then, you can use pip to install the cptac package. We plan on making cptac directly available through conda in the near future.

The package depends on several other Python libraries including numpy, pandas, requests, and others. Normally, pip will automatically handle these dependencies when it installs cptac and you don't have to worry about any of it. However, if you have a special use case or are interested in exactly which versions of which packages are needed, you can consult the install_requires list in the setup.py file.

Documentation

Our goal is that our documentation will make this software and data accessible both to people without a computer science background, and people without a biology background. We provide two types of documentation to accomplish this: tutorials and use cases. The tutorials give a basic introduction to the software as well as conventions for storing and accessing the data. The use cases are short examples focused on a biological question and show practical uses of the software and data for biological discovery. Each use case works with a different combination of data types and explores meaningful cancer research hypotheses.

You can access the tutorials and use cases as static webpages using the links below. They were originally written in Python as interactive Jupyter notebooks, so if you want to run them interactively with Jupyter you can download the notebooks from the notebooks folder on the GitHub repository. If you are unfamiliar with Jupyter, follow the installation and usage instructions given here on the Jupyter website. You will then be able to run our tutorials as interactive, exploratory data analyses. If you want to run them interactively without installing anything, please visit our Binder site which hosts the notebooks here.

Tutorials

Use cases

Developer documentation

Documentation for anyone wanting to understand the internal workings of the package is available on the GitHub repository in the devdocs folder.

License

See the LICENSE.md document on the GitHub repository. 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.

Owner

  • Name: Payne Lab, Biology Department, BYU
  • Login: PayneLab
  • Kind: organization

Citation (CITATION.cff) 

# YAML 1.2
---
authors: 
  -
    family-names: Lindgren
    given-names: "Caleb M."
    orcid: "https://orcid.org/0000-0001-6484-9757"
  -
    family-names: Adams
    given-names: "David W."
  -
    family-names: Kimball
    given-names: Benjamin
  -
    family-names: Boekweg
    given-names: Hannah
  -
    family-names: Tayler
    given-names: Sadie
  -
    family-names: Pugh
    given-names: "Samuel L."
  -
    family-names: Payne
    given-names: "Samuel H."
cff-version: "1.1.0"
date-released: 2021-02-09
doi: "10.1021/acs.jproteome.0c00919"
keywords: 
  - Cancer
  - Genetics
  - Genomics
  - Proteomics
  - Software
message: "If you use this software, please cite it using these metadata."
title: "Simplified and Unified Access to Cancer Proteogenomic Data"
version: "1.5.14"
...

GitHub Events

Total
  • Issues event: 6
  • Watch event: 10
  • Issue comment event: 5
  • Fork event: 5
Last Year
  • Issues event: 6
  • Watch event: 10
  • Issue comment event: 5
  • Fork event: 5

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 1,900
  • Total Committers: 38
  • Avg Commits per committer: 50.0
  • Development Distribution Score (DDS): 0.633
Top Committers
Name Email Commits
Caleb Lindgren c****n@g****m 697
Robert Oldroyd o****t@g****m 225
David d****2@g****m 219
corbinday r****r@g****m 209
benkk331 k****1@g****m 84
blhmc1 4****1@u****m 82
Lindsey Olsen l****5@g****m 80
seanjib s****b@g****m 43
Sam Payne s****e@g****m 40
blhmc1 b****2@g****m 36
hboekweg h****g@g****m 20
Jose H. Giraldez j****d@u****m 20
unknown s****r@g****m 20
polarisXD 3****D@u****m 19
Samuel Pugh s****4@g****m 19
Robert Oldroyd 4****b@u****m 18
Drew Bonnett a****t@g****m 9
thomashmolina t****a@g****m 8
caleb-lindgren 4****n@u****m 6
JonJarman j****n@g****m 6
sadietayler 3****r@u****m 6
sdsquire s****s@g****m 5
cbminor 4****r@u****m 5
Chelsie Minor c****r@g****m 3
corbinday c****y@C****l 3
Teancum Paquette t****h@g****m 3
Sam Squires s****s@p****n 3
Robert Oldroyd r****d@M****u 2
HannahBoekweg h****g@D****u 1
HannahBoekweg h****g@S****u 1
and 8 more...
Committer Domains (Top 20 + Academic)
samuels-mbp-010037022181.app.byu.edu: 1 samuels-mbp-010037014122.app.byu.edu: 1 corbins-mbp.app.byu.edu: 1 iphone-5.app.byu.edu: 1 samuels-mbp.app.byu.edu: 1 samuels-mbp-010037013063.app.byu.edu: 1 desktop-pgnaqdp.app.byu.edu: 1 macbook-pro-010037071090.app.byu.edu: 1 pop-os.localdomain: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 69
  • Total pull requests: 6
  • Average time to close issues: 10 days
  • Average time to close pull requests: 1 day
  • Total issue authors: 41
  • Total pull request authors: 5
  • Average comments per issue: 2.41
  • Average comments per pull request: 0.33
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 8
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 8
  • Pull request authors: 0
  • Average comments per issue: 0.63
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • zihaoxingstudy1 (13)
  • sgosline (7)
  • tobsecret (2)
  • sky1ove (2)
  • seunghun23 (2)
  • Liz-m57 (2)
  • Sunmile (2)
  • sooheon (2)
  • SAlkh90-temp (2)
  • martingarridorc (2)
  • CCranney (2)
  • sebastianffx (2)
  • DennisGankin (2)
  • nieyage (1)
  • smdb21 (1)
Pull Request Authors
  • seanjib (2)
  • awilliamson518 (1)
  • jhgirald (1)
  • dependabot[bot] (1)
  • hsiaoyi0504 (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 1,112 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 60
  • Total maintainers: 2
pypi.org: cptac

Python packaging for CPTAC data

  • Versions: 60
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 1,112 Last month
Rankings
Downloads: 7.1%
Stargazers count: 8.0%
Forks count: 8.4%
Dependent packages count: 10.1%
Average: 11.0%
Dependent repos count: 21.6%
Maintainers (2)
paynelab c-rb1n
Last synced: 7 months ago

Dependencies

.github/workflows/testing.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite