Classification of low quality cells from single-cell RNA-seq data
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.
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Cells with a high fraction of reads mapping to the mitochondrial genome (>10%) or the Rn45s repeat (>5%) were filtered out as likely dead cells.
“Additionally, we filtered out cells with a high fraction of reads mapping to the mitochondrial genome (>10%) or the Rn45s repeat (>5%) as likely dead cells (Ilicic et al. 2016).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Ilicic et al. 2016
The Results section (‘Biological features of low quality cells’; page unavailable) reports a higher proportion of reads from mitochondrial genes in broken cells, supporting the mitochondrial part of the rationale. Our copy does not support the >10% mitochondrial or >5% Rn45s cutoffs, or identify Rn45s reads as a marker of dead cells. (LLM, conf=0.86)
Experimental — accuracy not yet measuredViolations of the single-cell-per-droplet assumption are common in practice.
“Violations of this assumption, which may distort the interpretation of scRNA-seq data, are common in practice.”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [12]
The Background (page unavailable) says some scRNA-seq capture sites, including droplets, may contain multiple cells and that these cells can lead to misinterpretation. The Discussion (page unavailable) reports up to 30% multiple-cell captures in a Fluidigm study, but that figure concerns microfluidic capture sites, not droplets; the paper does not establish that single-cell-per-droplet violations are common. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTechnical quality features are highly correlated with biological cell type.
“a recent study44 reported that technical quality features were highly correlated with biological cell type.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [44]
Some methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data, whereas others are designed specifically for single-cell data.
“Some of these methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data1,44,89,90,105, whereas others are designed specifically for single-cell data71,93,135,137.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [44]
Cell QC is commonly performed based on three QC covariates: count depth, number of genes per barcode, and fraction of mitochondrial genes per barcode.
“Cell QC is commonly performed based on three QC covariates: the number of counts per barcode (count depth), the number of genes per barcode, and the fraction of counts from mitochondrial genes per barcode (Ilicic et al, 2016; Griffiths et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Ilicic et al, 2016
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- Authors
- Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A.
- Affiliations
- European Bioinformatics Institute (GB)
- National Health Service (GB)
- Corresponding author
- Teichmann, Sarah A.
- ORCID
- Venue
- Genome Biology
- Published
- 2016-02-17
- Volume
- 17
- Issue
- 1
- Pages
- 29-29
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC4758103
- OpenAlex
- W2286142055
- Cited by
- 693
- References
- 50
- Citations by year
- 2026: 43 · 2025: 58 · 2024: 81 · 2023: 82 · 2022: 108 · 2021: 91 · 2020: 72 · 2019: 68 · 2018: 51 · 2017: 22 · 2016: 16 · 2015: 1
- FWCI
- 27.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, cell quality control, cell classification, CD4+ T cells, dendritic cells, mouse embryonic stem cells, data processing
- MeSH terms
- Animals, Base Sequence, Bone Marrow Cells, Dendritic Cells, RNA, CD4-Positive T-Lymphocytes, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice, Embryonic Stem Cells, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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