FDCE

FDCE assesses consistency across multiple high-throughput compound screening and functional genomics datasets in cancer cell lines to identify concordant functional signals.

Key Features:

• Dataset integration: Integration of nine compound screening datasets and three functional genomics datasets for joint analysis.
• Direct consistency measures: Quantifies alignment between datasets to measure direct concordance.
• Indirect consistency measures: Evaluates associations between functional data and copy number-adjusted gene expression data.
• Multi-dataset assessment: Evaluates consistency across more than two datasets simultaneously rather than only pairwise comparisons.
• Global data analysis: Uses entire datasets rather than selected feature subsets to reduce the risk of overlooking global inconsistencies.

Scientific Applications:

• Functional data consistency assessment: Assess consistency of functional screening signals across compound screening and functional genomics datasets in cancer cell lines.
• Therapeutic agent identification: Identify compounds with consistent responses across multiple datasets.
• Target discovery: Link functional data with copy number-adjusted gene expression to support discovery of candidate targets.
• Cross-dataset validation: Validate findings across diverse datasets to increase reliability of conclusions in cancer genomics studies.

Methodology:

Integration of nine compound screening datasets and three functional genomics datasets; computation of direct measures assessing alignment between datasets; computation of indirect measures evaluating associations between functional data and copy number-adjusted gene expression data.