SimSCSnTree

SimSCSnTree simulates single-cell DNA sequencing data by generating an evolutionary tree of cells and evolving single nucleotide variants (SNVs) and copy number aberrations (CNAs) along that tree to model cellular evolution.

Key Features:

• Single-cell DNA sequencing simulation: Generates synthetic single-cell DNA sequencing datasets that mimic the complexity of real single-cell genomic data.
• Evolutionary tree generation: Constructs an evolutionary tree of cells to represent lineage relationships within a cellular population.
• SNV evolution: Simulates the emergence and propagation of single nucleotide variants (SNVs) along the cell tree.
• CNA evolution: Simulates copy number aberrations (CNAs) evolving along the cell tree.
• Benchmark dataset generation: Produces controlled datasets for benchmarking computational methods for SNV and CNA detection and analysis at single-cell resolution.
• Modeling cellular evolution: Models evolutionary processes within cellular populations to study tumor evolution and heterogeneity in cancer genomics.

Scientific Applications:

• Benchmarking variant and CNA callers: Benchmarking SNV and CNA detection algorithms and pipelines using simulated single-cell DNA sequencing datasets.
• Method development in cancer genomics: Developing and testing computational methods for studying tumor evolution, clonal structure, and heterogeneity in cancer genomics.
• Performance evaluation of analytical tools: Evaluating the performance of single-cell analytical tools under controlled evolutionary scenarios.

Methodology:

Generates an evolutionary tree of cells and simulates the evolution of single nucleotide variants (SNVs) and copy number aberrations (CNAs) along that tree to produce single-cell DNA sequencing datasets.