CVCDAP

CVCDAP enables cohort-level analysis of large-scale multi-omics cancer datasets from TCGA, CPTAC, and user-uploaded data to identify molecular and clinical associations.

Key Features:

• Dataset integration: Integrates multi-omics data from TCGA, CPTAC, and user-uploaded datasets for combined analysis.
• Customizable analysis toolbox: Provides configurable analytical modules for cohort-level studies.
• Virtual cohort creation: Selects patients across multiple studies based on shared molecular and clinical characteristics to form virtual cohorts.
• Multi-omics analysis: Supports genomic, transcriptomic, proteomic, and clinical analyses, including single-cohort and two-cohort comparisons.
• Reproducibility and discovery: Enables reproduction of published findings and identification of novel molecular mechanisms and potential therapeutic approaches.

Scientific Applications:

• Virtual cohort analysis: Build and analyze cohorts defined by molecular or clinical criteria to study specific cancer subgroups.
• Comparative cohort studies: Compare two cohorts to identify differential genomic, transcriptomic, or proteomic patterns.
• Validation of published results: Reproduce and validate findings reported in the literature using integrated TCGA and CPTAC data.
• Mechanism and target discovery: Identify candidate molecular mechanisms and potential therapeutic approaches from multi-omics associations.

Methodology:

Integrates multi-omics data from multiple sources into a cohesive analytical framework, enables definition of virtual cohorts based on shared molecular or clinical characteristics, and applies built-in tools ranging from descriptive statistics to comparative cohort analyses.