SCONCE

SCONCE profiles copy number alterations (CNAs) from single-cell whole genome sequencing data to characterize tumor evolutionary dynamics.

Key Features:

• Theoretical evolutionary framework: Leverages tumor evolutionary history to improve CNA profiling accuracy.
• Hidden Markov Model on read depth: Integrates a Hidden Markov Model to analyze read depth from single-cell whole genome sequencing data.
• Contrast to traditional approaches: Addresses limitations of traditional Hidden Markov Model and change point detection approaches that rely solely on observed read depth.
• Matched normal controls: Uses matched normal cells as negative controls for CNA calling.
• Technical noise modeling: Accounts for technical noise associated with low coverage sequencing data.
• Evolution-aware CNA calling: Calls copy number alterations that reflect both genomic state and evolutionary context.
• Validation: Validated on public datasets and simulations, demonstrating accurate copy number profiling.
• Implementation: Implemented in C++11.

Scientific Applications:

• CNA profiling in cancer evolution: Reconstruction and characterization of copy number alterations in tumor cells from single-cell whole genome sequencing.
• Lineage tracing: Tracing lineage and progression of cancer cells through CNA patterns.
• Tumor heterogeneity analysis: Decoding copy number profiles to study intra-tumor heterogeneity.
• Benchmarking and validation: Benchmarking and validation of CNA calls using public datasets and simulations.

Methodology:

Integrates a Hidden Markov Model on read depth from single-cell whole genome sequencing, uses matched normal cells as negative controls, models tumor evolutionary history, and accounts for technical noise in low coverage sequencing data; validated on public datasets and simulations.