CPTAC

CPTAC provides programmatic access to processed proteogenomic datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to enable analysis of DNA, RNA, protein, and clinical characterizations across tumor and normal samples.

Key Features:

• Data Accessibility: Provides programmatic access to processed quantitative data tables derived from CPTAC datasets, including DNA, RNA, protein, and clinical characterizations, in a consistent format suitable for downstream graphing, statistical, and machine-learning analyses.
• Cross-Language Compatibility: Implemented in Python and supports use within R via the reticulate package.
• Consistent Data Formatting: Standardizes data presentation across cancer types to enable pan-cancer comparisons and trend analyses.
• Reproducibility: Exposes data via an API that streams datasets directly into programming environments to support reproducible analyses.

Scientific Applications:

• Investigation of cancer mechanisms and therapeutic targets: Distributes finalized processed datasets in an up-to-date format for investigating cancer mechanisms and identifying potential therapeutic targets.
• Pan-cancer trend analysis: Enables exploration of pan-cancer trends across different cancer types using harmonized proteogenomic data.
• Comparative analyses for personalized medicine: Supports detailed comparative analyses that can inform personalized medicine approaches.

Methodology:

Implemented as a Python package that exposes a data API for accessing processed CPTAC datasets directly within Python or via the reticulate package in R.