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

Daihan Ji^1,2, Mei Han², Shuting Lu^1,2, Jiaying Zeng^2,3, Wen Zhong^1,2,4

¹The Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou, China.

²Guangzhou National Laboratory, Guangzhou, Guangdong Province 510005, China.

³Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.

⁴Department of Early Life Development and Cohort Research Laboratory, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents' Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Municipal Health Commission Key Laboratory of Children's Vital Organ Development and Diseases, Chongqing 401122, China.

PubMed PMC DOI PDF

Abstract

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]…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.