CNAsim

CNAsim simulates single-cell copy number alteration (CNA) data to generate realistic CNA profiles and DNA sequencing data for studies of tumor heterogeneity and evolution.

Key Features:

• Realistic Error Modeling: Incorporates an error model that accounts for single-cell sequencing biases, including read count fluctuations and resolution limitations.
• Diverse CNA Mechanisms: Simulates whole-genome duplication (WGD), whole-chromosomal CNAs, and chromosome-arm CNAs.
• Subclonal Population Structure Simulation: Models the accumulation of chromosomal CNAs to produce subclonal population structures.
• Population Dilution Options: Supports dilution of sampled cell populations with normal diploid cells and pseudo-diploid cells.
• DNA-seq Data Generation: Generates DNA sequencing data for sampled cells.
• Scalability: Efficiently generates realistic single-cell CNA profiles for thousands of simulated tumor cells.

Scientific Applications:

• Tumor Heterogeneity and Evolution: Provides realistic single-cell CNA and DNA-seq data to study tumor heterogeneity and evolutionary dynamics.
• Benchmarking Algorithms: Enables testing and benchmarking of single-cell sequencing and CNA-calling algorithms under controlled conditions.
• CNA Mechanism Assessment: Facilitates assessment of the impacts of different CNA mechanisms such as WGD and arm-level CNAs.
• Cancer Genomics Method Development: Supports development and evaluation of analytical methods in cancer genomics.

Methodology:

Implemented in Python, CNAsim incorporates an error model accounting for read count fluctuations, simulates WGD and chromosome-/arm-level CNAs, models accumulation of chromosomal CNAs to produce subclonal structures, supports dilution with diploid and pseudo-diploid cells, and generates DNA-seq data for sampled cells.