Simulating Longitudinal Single-cell RNA Sequencing Data with RESCUE

2025 · doi:10.1101/2025.01.06.631629
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The paper studies how to simulate longitudinal single-cell RNA-sequencing (scRNA-seq) data for planning and evaluating analytical methods, focusing on how to model variability across both repeated samples and subjects. It introduces RESCUE, an R package that generates longitudinal scRNA-seq counts using a gamma–Poisson framework and explicitly adds between-sample and between-subject variability, improving over existing single-sample simulation approaches. The authors show that RESCUE can reproduce key data properties and demonstrate an application for study planning, including power-related sample size considerations. The paper does not provide a clinical validation target and is mainly a methodological contribution, with the limitation that performance is demonstrated through simulation-based property reproduction rather than broad empirical longitudinal benchmarks. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

As single-cell RNA-sequencing (scRNA-seq) becomes more widely used in transcriptomic research, complex experimental designs, such as longitudinal studies, become increasingly feasible. 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 longitudinal scRNA-seq data. Here, we introduce RESCUE (REpeated measures Single Cell RNA-seqUEncing data simulation), a novel method that simulates longitudinal scRNA-seq data using a gamma-Poisson frame-work 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. RES-CUE is implemented as an R package and is available at https://github.com/ewynn610/RESCUE .
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Abstract As single-cell RNA-sequencing (scRNA-seq) becomes more widely used in transcriptomic research, complex experimental designs, such as longitudinal studies, become increasingly feasible. 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 longitudinal scRNA-seq data. Here, we introduce RESCUE (REpeated measures Single Cell RNA-seqUEncing data simulation), a novel method that simulates longitudinal scRNA-seq data using a gamma-Poisson frame-work 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. RES-CUE is implemented as an R package and is available at https://github.com/ewynn610/RESCUE. Competing Interest Statement The authors have declared no competing interest.

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