Simulating paired and longitudinal single-cell RNA sequencing data with rescueSim

Area of Science:

• Genomics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is increasingly used for complex transcriptomic studies.
• Paired/longitudinal designs allow tracking cell-type-specific transcriptomic changes over time.
• Limited guidance exists for analytical approaches and study planning, including power analysis, for these designs.

Purpose of the Study:

• To introduce a novel data simulation method for paired/longitudinal scRNA-seq data.
• To address the need for simulating between-sample and between-subject variability.
• To provide a tool for study planning and analysis method evaluation.

Main Methods:

• Developed rescueSim (REpeated measures Single Cell RNA-seqUEncing data SIMulation).
• Utilizes a gamma-Poisson framework for data simulation.
• Incorporates variability between samples and subjects.

Main Results:

• rescueSim accurately reproduces key properties of paired/longitudinal scRNA-seq data.
• Demonstrated the utility of rescueSim in planning complex transcriptomic studies.
• The method effectively simulates the inherent variability in repeated measures experiments.

Conclusions:

• rescueSim provides a valuable resource for designing and analyzing paired/longitudinal scRNA-seq experiments.
• Accurate data simulation is crucial for robust transcriptomic study planning.
• This tool enhances the feasibility of complex experimental designs in transcriptomic research.