Splatter: simulation of single-cell RNA sequencing data.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) technologies have advanced rapidly, necessitating sophisticated analysis methods.
• Current simulation methods for scRNA-seq data often lack documentation, validation against real data, or reproducible code.
• This gap hinders the reliable testing and development of new scRNA-seq analysis tools.

Purpose of the Study:

• To present Splatter, a novel Bioconductor package for simulating scRNA-seq data.
• To provide a simple, reproducible, and well-documented framework for scRNA-seq data simulation.
• To offer an interface to various simulation approaches, including the novel Splat method.

Main Methods:

• Development of the Splatter R package within the Bioconductor framework.
• Implementation of the Splat simulation method, based on a gamma-Poisson distribution.
• Integration of multiple simulation strategies within a unified package interface.

Main Results:

• Splatter enables the simulation of diverse scRNA-seq data scenarios.
• The Splat method allows for simulating single cell populations, multiple cell types, and differentiation trajectories.
• The package ensures reproducibility and detailed documentation for simulation workflows.

Conclusions:

• Splatter provides a robust and user-friendly solution for generating realistic simulated scRNA-seq data.
• The package facilitates the development and validation of scRNA-seq analysis methods.
• Improved simulation practices through Splatter will advance the field of single-cell genomics.