scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation
Year: 2026
Type: preprint
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 samples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.
Authors: Chengfeng Jiang, Jingyi Jessica Li
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Collaborative Research: Development of Classification Theory and Methods for Objective Asymmetry, Sample Size Limitation, Labeling Ambiguity, and Feature Importance, CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing, Statistical methods for elucidating regulatory mechanisms and functional impacts of transcriptome variation at population and single-cell scales, Experimental-data-based in-silico data generation platform to improve the accuracy and reliability of single-cell and spatial omics data analysis
OpenAlex ID: W7221020281