uniConSig

uniConSig quantifies genome-wide gene functional signatures and identifies pathway enrichment by integrating molecular concept sets (ontologies, pathways, interactions, domains) to discover novel gene functions and pathways.

Key Features:

• Molecular concept integration: Integrates ontologies, pathways, interactions, and domains into extensive molecular concept sets.
• Universal Concept Signature (uniConSig) analysis: Computes signature molecular concepts from known functional gene lists for genome-wide quantification of gene functions.
• Concept Signature Enrichment Analysis (CSEA): Assesses pathway enrichment in experimental gene lists based on shared concept signatures between gene sets at multiple functional levels.
• Multi-level functional signatures: Captures shared concept signatures across ontologies, pathways, interactions, and domains.
• Applicability to gene expression and single-cell data: Operates on gene expression and single-cell transcriptomic datasets, including contexts with low gene coverage and complex cellular state transitions.
• Meta-analytic capability: Supports analysis across transcriptomic datasets such as cancer cell line models and single hematopoietic stem cells.

Scientific Applications:

• Pathway discovery from transcriptomic data: Identifies pathways enriched in gene expression and single-cell transcriptomic experimental gene lists.
• Discovery of novel gene functions and disease-associated pathways: Quantifies and uncovers new biological or pathological gene functions genome-wide.
• Analysis of genetic perturbations and cellular state changes: Reveals pathways related to genetic perturbations and changes in cellular states.
• Meta-analysis of transcriptomic datasets: Applicable to meta-analyses including cancer cell line models and single hematopoietic stem cells.

Methodology:

Integrates molecular concept sets (ontologies, pathways, interactions, domains) to compute signature molecular concepts from known functional gene lists and performs uniConSig analysis and CSEA based on shared concept signatures between gene sets at multiple functional levels.