RNMF

RNMF quantifies associations between mutational signatures and genes using the cumulative contribution abundance (CCA) model to elucidate pathogenic somatic mutational processes in cancer.

Key Features:

• Mutational signature analysis: Identifies and analyzes patterns of somatic single-base substitution (SBS) and insertion-deletion (ID) signatures to characterize mutational processes.
• Cumulative Contribution Abundance (CCA) model: Implements the CCA model to quantify and highlight gene-level contributions to specific mutational signatures.
• Discovery of known and novel signatures: Applied to detect both previously described and undescribed SBS and ID signatures in tumor cohorts.
• Association findings: Reports specific associations such as APOBEC-related signatures (SBS2* and SBS13*) with PIK3CA E545k and age-related signatures with frequent TP53 mutations (notably R342*).
• Application to ESCC meta-analysis: Used in a meta-analysis of 1,073 esophageal squamous cell carcinoma (ESCC) cases to characterize signature–gene relationships.
• CCA matrix images: Generates CCA matrices/images for genes within signatures (including New, SBS3*, and SBS17b*) to support preliminary survival outcome evaluation.
• Implementation: Provided as an R package implementation for computational analysis.

Scientific Applications:

• Mutational process elucidation: Elucidates underlying pathogenic biological processes driving somatic mutations in cancer.
• Signature–gene correlation studies: Establishes correlations between mutational signatures and specific genes to inform hypotheses about mutational mechanisms.
• Clinical association screening: Provides CCA-derived data to support preliminary evaluations of survival outcomes and other clinical factor associations.

Methodology:

Uses the cumulative contribution abundance (CCA) model to quantify associations between mutational signatures and genes and generates CCA matrices/images for downstream analysis.