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Integrative cancer research currently is hampered by the fact that important datasets are stored in separate, non-interoperable repositories. The Cancer Data Aggregator (CDA) is being developed to allow researchers to aggregate diverse data types generated by NCI-funded programs, such as the Human Tumor Cell Atlas Network (HTAN) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Using the CDA and a harmonized data model developed by the Center for Cancer Data Harmonization (CCDH), users can discover, query, retrieve, and aggregate data according to a variety of search parameters, such as participant, sample, tissue, disease, or study.
In addition to a query engine, the CDA will provide a central repository for basic clinical and biospecimen metadata to serve as a primary resource for the CRDC. It will contain both open- and controlled-access metadata in a structured format to support federation across multiple repositories. This central repository will be accessible via an Application Programming Interface (API) and includes mechanisms for receiving new data and updating existing data. The central repository will eliminate the need for each CRDC repository to store redundant copies of common clinical and biospecimen data. This will avoid discrepancies between individual CRDC repositories that could compromise the CDA’s ability to return accurate results. The CDA will facilitate interoperability within the cancer data ecosystem to make complex datasets available to the research community to perform integrative analysis.
cdapython (/c-d-a python/) is a Python library sits on top of the machine generated CDA Python Client <https://github.com/CancerDataAggregator/cda-service-python-client/>_ built to make it more pleasant to query the CDA.
It pulls data from various :doc:sources ETL.
and offers a simple and intuitive API.