scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation
- 1Fred Hutchinson Cancer Center
Abstract
Single-cell RNA sequencing (scRNA-seq) studies often profile only one or a few biological samples, limiting assessment of discovery stability under between-sample variation. We present scStable, a framework for assessing scRNA-seq analysis stability by generating synthetic samples that preserve cell-level heterogeneity from a single-sample reference while injecting bulk-derived between-sample variation. In gastric cancer data comprising 21 matched scRNA-seq and bulk RNA-seq profiles, scStable better recapitulates observed between-sample variation than ZINB-WaVE, subsampling, and Gaussian noise injection. Using synthetic sam-ples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.
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BibTeX
@misc{jiang2026scstable,
title = {{scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation}},
author = {C. Jiang and J. J. Li},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.755876},
url = {https://www.biorxiv.org/content/10.64898/2026.10.01.755876v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation
AU - C. Jiang
AU - J. J. Li
PY - 2026
DA - 2026/10/01
PB - bioRxiv
DO - 10.64898/2026.10.01.755876
UR - https://www.biorxiv.org/content/10.64898/2026.10.01.755876v1
AB - Single-cell RNA sequencing (scRNA-seq) studies often profile only one or a few biological samples, limiting assessment of discovery stability under between-sample variation. We present scStable, a framework for assessing scRNA-seq analysis stability by generating synthetic samples that preserve cell-level heterogeneity from a single-sample reference while injecting bulk-derived between-sample variation. In gastric cancer data comprising 21 matched scRNA-seq and bulk RNA-seq profiles, scStable better recapitulates observed between-sample variation than ZINB-WaVE, subsampling, and Gaussian noise injection. Using synthetic sam-ples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.
ER -