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.