supersigs

supersigs generates SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome.

Key Features:

• Supervised signature learning: Functions allow users to learn supervised mutational signatures from their own data.
• Signature application: Functions allow users to apply learned supervised mutational signatures to new data.

Scientific Applications:

• Cancer genome analysis: Generation of supervised mutational signatures from single nucleotide variants for cancer genome characterization.

Methodology:

The methodology is based on the approach described in Afsari (2021, ELife).