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

Authors

Yuanyuan Liu, Cheng Yang, Chenghui Wang, Mingwang Zhang, Kai Luo, Lihua Wu, Xiufeng Xie

Published in

Communications biology. Jun 24, 2026. Epub Jun 24, 2026.

Abstract

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.

PMID:
42342867
Bibliographic data and abstract were imported from PubMed on 25 Jun 2026.

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