ScQCenrich enables multi-metric quality control for single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) is highly susceptible to dissociation stress, partial lysis, and nuclear–cytoplasmic imbalance, yet quality control still often relies on fixed thresholds for mitochondrial RNA, gene counts, and UMIs. Here we present scQCenrich, an interpretable multi-metric QC framework for post-cell-calling whole-cell scRNA-seq that integrates canonical metrics with intronic fraction, MALAT1 enrichment, dissociation-stress features, and optional splice-aware information. Across mouse brain, mouse heart and lung cancer datasets, scQCenrich reduces over-filtering relative to conventional and model-based comparators while preserving coherent neuronal, erythroid, cardiomyocyte and malignant-cell populations. In high-quality peripheral blood mononuclear cell data, the method remains conservative. Automated reports link quality-control calls to cluster-level metrics, marker genes and functional enrichment. scQCenrich therefore provides a transparent and reproducible framework for quality-control decisions in whole-cell single-cell RNA sequencing analyses. scQCenrich integrates canonical, nuclear-enrichment, and stress-related metrics to improve quality control in single-cell RNA sequencing while preserving biologically coherent cell populations.