PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Cellular lineage trees are crucial for understanding differentiation.
• Current single-cell RNA sequencing (scRNA-seq) datasets lack complex topologies, limiting tool development.
• Accurate lineage reconstruction requires robust simulation tools.

Purpose of the Study:

• To develop a simulator, PROSSTT, for generating scRNA-seq data with complex lineage trees.
• To provide a platform for testing and improving lineage tree reconstruction algorithms.
• To enable the creation of synthetic datasets with controllable complexity and noise.

Main Methods:

• PROSSTT simulates scRNA-seq data based on user-defined lineage tree complexity, noise levels, and models.
• The tool generates datasets of variable sizes to mimic real experimental conditions.
• Included scripts allow for the quantitative assessment of predicted lineage trees.

Main Results:

• PROSSTT successfully simulates scRNA-seq datasets for differentiation processes with arbitrary lineage tree complexity.
• The simulator allows for fine-tuning of noise parameters and dataset size.
• Associated scripts facilitate the evaluation of lineage reconstruction tool performance.

Conclusions:

• PROSSTT addresses the need for complex synthetic scRNA-seq data for lineage tree research.
• This tool will accelerate the development and validation of computational methods for cell lineage analysis.
• PROSSTT enhances the ability to study complex differentiation pathways using scRNA-seq data.