CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples

Area of Science:

• Genomics
• Computational Biology
• Evolutionary Biology

Background:

• Single-cell studies offer high resolution for biological systems.
• Somatic evolution in healthy and pathological tissues is a key research area.
• Single-cell sequencing data is often noisy, requiring careful analysis.

Purpose of the Study:

• Introduce CellCoal, a software tool for coalescent simulation of single-cell sequencing genotypes.
• Provide a flexible tool to understand somatic evolutionary processes at the single-cell level.
• Enable benchmarking of bioinformatic tools for single-cell sequencing data analysis.

Main Methods:

• CellCoal simulates single-cell sample histories from somatic cell populations.
• It generates single-nucleotide variants with various mutation models.
• Accounts for allelic imbalance, dropout, amplification, and sequencing errors.

Main Results:

• CellCoal simulates realistic single-cell sequencing genotypes.
• The tool incorporates technical biases common in single-cell data.
• It provides a platform for understanding and analyzing somatic evolution.

Conclusions:

• CellCoal is a valuable resource for studying somatic evolution using single-cell sequencing data.
• The software aids in interpreting noisy data and validating analytical methods.
• It supports research in both healthy and pathological cellular contexts.