Package: cptac
Easy access to CPTAC dataThis 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 calledcptac, but you can seamlessly use it in an R environment with the help of thereticulatepackage (demonstrated inTutorial 6). Our package is installed in one step withpip:pip install cptacSee theInstallation section belowif you have further questions.The package gives you the data aspandasDataFrameobjects in Python. If you are using R,reticulateconverts the tables todata.frameobjects. 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.InstallationThis package is intended to run on Python 3.6 or greater. If you plan on interfacing with it from R viareticulate, you must still have Python installed on your computer, and download the package into that Python environment.Installing PythonIf you do not already have Python installed on your computer, we suggest using either thestandard Python distributionor theAnaconda 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 packageWe distribute the packagethrough the Python Package Index (PyPI), so regardless of which Python distribution you are using, you install the package using thepipprogram:pip install cptacIf you are using the Anaconda distribution of Python, this will installcptacto the currently active environment as long aspipis available in that environment, which it would be by default. Ifpipis not installed in your environment, you can install it withconda install -c anaconda pip. Then, you can usepipto install thecptacpackage. We plan on makingcptacdirectly available throughcondain the near future.The package depends on several other Python libraries includingnumpy,pandas,requests, and others. Normally,pipwill automatically handle these dependencies when it installscptacand 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 theinstall_requireslist in thesetup.py file.DocumentationOur 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 thenotebooks folderon the GitHub repository. If you are unfamiliar with Jupyter, follow the installation and usage instructions givenhereon 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 notebookshere.TutorialsTutorial 1: CPTAC data introductionTutorial 2: Using pandas to work with cptac dataframesTutorial 3: Joining dataframes with cptacTutorial 4: Understanding multi-indexesTutorial 5: How to keep up to date with new package and data releasesTutorial 6: Easy integration with RUse casesUse Case 1: Comparing transcriptomics and proteomicsUse Case 2: Correlation between clinical attributesUse Case 3: Associating clinical variables with omics dataUse Case 4: How Do Mutations Affect Protein Abundance?Use Case 5: Gene Set Enrichment AnalysisUse Case 6: Comparing Derived Molecular Data with ProteomicsUse Case 7: Trans Genetics EffectsUse Case 8: OutliersUse Case 9: Clinical OutcomesUse Case 10: Pathway diagram overlayDeveloper documentationDocumentation for anyone wanting to understand the internal workings of the package is available on the GitHub repository in thedevdocs folder.LicenseSee theLICENSE.mddocument on the GitHub repository. Please note the difference between the license as it applies to code versus data.ContactThis package is maintained bythe Payne labat Brigham Young University.