SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model
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Abstract
Background: Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It’s already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses. Results: We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data. Conclusions: SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC .
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Venus protein expression correlates mostly with RNA findings, except weak acinar expression around E18.5 which appears underestimated in scRNA sequencing data.
“These patterns correlate mostly with the RNA findings, except the weak but robust acinar expression around E18.5 which appears underestimated in scRNA sequencing data potentially attributed to technical limitations for weakly expressed genes [50–54].”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [52]
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TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration
Melzer, M. K., Schirge, S., Gout, J., Arnold, F., Srinivasan, D., Burtscher, I., Allgöwer, C., Mulaw, M., Zengerling, F., Günes, C., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01553-x · Cited by 5 · see citing works
- Authors
- Zheng, Y., Zhong, Y., Hu, J., & Shang, X.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Hu, Jialu
- Shang, Xuequn
- Venue
- BMC Bioinformatics
- Published
- 2021-01-06
- Volume
- 22
- Issue
- 1
- Pages
- 5-5
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC7788948
- OpenAlex
- W3108294659
- Cited by
- 18
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 1
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Northwestern University
- National Natural Science Foundation of China (61332014,, 61702420, 62072374, 61772426)
- Northwestern Polytechnical University
- 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, dropout imputation, missing data imputation, mixture models, cell clustering, intra-class distance
- MeSH terms
- Gene Expression Regulation, Models, Genetic, RNA, Small Cytoplasmic, Gene Expression Profiling, Genomics, Single-Cell Analysis
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