SingleCellMQC: A comprehensive quality control workflow for single-cell multi-omics

Abstract

Summary As single-cell multi-omics studies scale in size and complexity, comprehensive and modality-aware quality control (QC) is essential to ensure data integrity. Here, we develop SingleCellMQC, an open-source R package that provides a unified QC framework for single-cell RNA sequencing (scRNA-seq), surface proteome profiling (antibody-derived tags, ADTs), and immune repertoire (T cell receptors [TCRs]/B cell receptors [BCRs]) data. SingleCellMQC implements multi-level QC across sample, cell, feature, and batch levels, integrating empirical thresholds, tissue-specific reference ranges, and data-driven outlier detection. Built on Seurat and BPCells, SingleCellMQC supports common preprocessing outputs and generates interactive hypertext markup language (HTML) reports with visual summaries and automated QC flags. Its modular architecture allows flexible integration with existing workflows, and the implementation is optimized for scalability on standard computing environments. The performance and reliability of SingleCellMQC were demonstrated in three datasets: an in-house peripheral blood mononuclear cells (PBMCs) multi-omics dataset (28,498 cells), a public PBMC scRNA-seq dataset (137,214 cells), and a large-scale breast tissue scRNA-seq dataset (> 1 million cells).