Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data
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Abstract
Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data.
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Low-quality nuclei isolation protocols can introduce artifacts that compromise data interpretation and experimental reliability.
“Low-quality nuclei isolation protocols can introduce artifacts that compromise the proper data interpretation and the experimental reliability[32, 33].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [32]
The Introduction says that sample-preparation artifacts in single-cell RNA sequencing can confound downstream analyses or produce erroneous findings. It discusses cell dissociation and other cell-based procedures, not low-quality nuclei isolation protocols specifically, so the claim extends beyond the source's stated setting. (LLM, conf=0.85)
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- Authors
- Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Wang, Z., Alabdullatif, S., Jansen, F., Yajima, M., Johnson, W. E., & Campbell, J. D.
- Affiliations
- Boston University (US)
- Corresponding author
- Campbell, Joshua D.
- ORCID
- Venue
- Nature Communications
- Published
- 2022-03-30
- Volume
- 13
- Issue
- 1
- Pages
- 1688-1688
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8967915
- OpenAlex
- W4226031055
- Cited by
- 82
- References
- 32
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 20 · 2023: 14 · 2022: 6
- FWCI
- 5.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Instituto Tecnológico de Costa Rica
- National Institutes of Health (1u2cca233238-01, 1U2CCA233238)
- National Cancer Institute (U01CA220413, U2C-CA233238, 1U2CCA233238)
- National Institute of General Medical Sciences (T32GM100842, R01GM127430)
- U.S. National Library of Medicine (R01LM013154)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, quality control metrics, doublet detection, technical artifacts, data visualization, bioinformatics pipeline
- MeSH terms
- Exome Sequencing, Quality Control, Software, Sequence Analysis, RNA, Benchmarking
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