ArticleiScience2022
SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Genome-wide mapping of theiScience · 2026Article
- RNA sequencing of mRNA 5'-ends reveals regulators of bovine embryonic genome activation.BMC genomics · 2025Article
- Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine.Chinese medicine · 2024Review
- Microfluidic cell sorter sample preparation for genomic assays.Biomicrofluidics · 2022Article
- Inducing human retinal pigment epithelium-like cells from somatic tissue.Stem cell reports · 2022Article
- Reprogramming epiblast stem cells into pre-implantation blastocyst cell-like cells.Stem cell reports · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis and interpretation of single-cell RNA sequencing (scRNA-seq) experiments are compromised by the presence of poor-quality cells. For meaningful analyses, such poor-quality cells should be excluded as they introduce noise in the data. We introduce SkewC, a quality-assessment tool, to identify skewed cells in scRNA-seq experiments. The tool's methodology is based on the assessment of gene coverage for each cell, and its skewness as a quality measure; the gene body coverage is a unique characteristic for each protocol, and different protocols yield highly different coverage profiles. This tool is designed to avoid misclustering or false clusters by identifying, isolating, and removing cells with skewed gene body coverage profiles. SkewC is capable of processing any type of scRNA-seq dataset, regardless of the protocol. We envision SkewC as a distinctive QC method to be incorporated into scRNA-seq QC processing to preclude the possibility of scRNA-seq data misinterpretation.
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