Identification of cell types from single-cell transcriptomes using a novel clustering method
Open accessAbstract
Motivation: The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes). Results: In this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy. Availability and implementation: The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq. Contact: zcsu@uncc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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The cellular transcriptomic state space can be modeled as a smooth, low-dimensional manifold or a set of manifolds.
“We begin by modeling the cellular transcriptomic state space as a smooth, low-dimensional manifold or set of manifolds.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [7]
Many unsupervised scRNA-seq clustering methods have been proposed, which are followed by time-consuming and labor-costly annotations.
“Many unsupervised scRNA-seq clustering methods have been proposed2–4, which are followed by time-consuming and labor-costly annotations5.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [4]
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- Authors
- Xu, C., & Su, Z.
- Affiliations
- University of North Carolina at Charlotte (US)
- Corresponding author
- Su, Zhengchang
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-02-11
- Volume
- 31
- Issue
- 12
- Pages
- 1974-1980
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6280782
- OpenAlex
- W2109890799
- Cited by
- 559
- References
- 32
- Citations by year
- 2026: 25 · 2025: 32 · 2024: 41 · 2023: 76 · 2022: 61 · 2021: 76 · 2020: 66 · 2019: 70 · 2018: 51 · 2017: 38 · 2016: 17 · 2015: 4 · 2014: 2
- FWCI
- 15.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Science Foundation (0849615, 1048261)
- University of North Carolina at Charlotte
- National Institute of General Medical Sciences (R01GM106013)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- single-cell transcriptomics, cell type annotation, cell clustering, shared nearest neighbor, high-dimensional data, gene expression patterns
- MeSH terms
- Algorithms, Animals, Embryo, Mammalian, Gene Expression Regulation, Humans, Neoplasms, Programming Languages, Cluster Analysis, Cell Lineage, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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