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

• Published in: Bioinformatics (Oxford, England) Vol. 41; no. 8
• Main Authors: Wynn, Elizabeth A, Mould, Kara J, Vestal, Brian E, Moore, Camille M
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.08.2025; Oxford Publishing Limited (England)
• Subjects: Algorithms; Availability; Computer Simulation; Data simulation; Gene Expression Profiling - methods; Gene sequencing; Humans; Longitudinal studies; Original Paper; Ribonucleic acid; RNA; RNA-Seq - methods; Sequence Analysis, RNA - methods; Simulation; Single-Cell Analysis - methods; Software; Transcriptomics; Variability
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btaf442

Abstract Motivation As single-cell RNA-sequencing (scRNA-seq) becomes more widely used in transcriptomic research, complex experimental designs, such as paired or longitudinal studies, become increasingly feasible. Paired/longitudinal scRNA-seq enables the study of transcriptomic changes over time within specific cell types, yet guidance on analytical approaches and resources for study planning, such as power analysis, remains limited. Data simulation is a valuable tool for evaluating analysis method performance and informing study design decisions, including sample size selection. Currently, most scRNA-seq simulation methods simulate cells for a single sample, thus ignoring the between-sample and between-subject variability inherent to paired/longitudinal scRNA-seq data. Results Here, we introduce rescueSim (REpeated measures Single Cell RNA-seqUEncing data SIMulation), a novel method that simulates paired/longitudinal scRNA-seq data using a gamma-Poisson framework and incorporates additional variability between samples and subjects. We demonstrate our method’s ability to reproduce important data properties and demonstrate its application in study planning. Availability and implementation rescueSim is implemented as an R package and is available at https://github.com/ewynn610/rescueSim.