ArticleBioinformatics (Oxford, England)2015
Identification of cell types from single-cell transcriptomes using a novel clustering method.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers, 1 of them a synthesis that pooled it.
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Who cites it
272 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
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- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- Multimodal machine learning integrates clinical and comorbidity data to predict breast cancer prognosis and treatment outcomes.Scientific reports · 2026Article
- An epithelial morphogenetic program for maximal urine concentration.Nature communications · 2026Article
- Hb-EGF directs systemic muscle repair.Development (Cambridge, England) · 2026Article
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
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- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- Key Concepts in Transcriptomics Data Analysis in the Era of Next-Gen Sequencing.Advances in experimental medicine and biology · 2026Review
- scLTF: A self-training clustering framework for large-scale single-cell RNA-Seq data.PloS one · 2026Article
- Integration of spatial and single-nucleus transcriptomics to map gene expression in the developing mouse kidney.Development (Cambridge, England) · 2025Article
- Consensus spectral clustering with weighted similarity functions for single-cell RNA sequencing data.Communications in statistics: Simulation and computation · 2025Article
- Clinical application of single-cell RNA sequencing in disease and therapy.Clinical and translational medicine · 2025Review
212 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
motivationThe 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).
resultsIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.