ArticleComputational and structural biotechnology journal2024
scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A who's who of cell types in skeletal muscle.Communications biology · 2026Review
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- FUNCellA: A Tool for Single-Sample Enrichment Analysis and Relative Pathway Activity Estimation in Single-Cell RNA Sequencing Data.Computational and structural biotechnology journal · 2026Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.PLoS computational biology · 2025Article
- Mining alternative splicing patterns in scRNA-seq data using scASfind.Genome biology · 2024Article
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9 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.
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