ArticleBioinformatics (Oxford, England)2023
Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 19 citations in OpenAlex.
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- Characterizing pancreatic cancer-associated fibroblast heterogeneity in vascular microphysiological systems.Angiogenesis · 2026Article
- DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy response.Cell reports. Medicine · 2026Article
- Uncovering the molecular logic of cortical wiring between neuronal subtypes across development through ligand-receptor inference.Nature communications · 2026Article
- BayesRare: Bayesian mixture model for population-level rare cell type detection in multi-subject single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Integrative single-cell and bulk RNA sequencing unravels the role of ACTN1 in promoting lung cancer with brain metastasis and epidermal growth factor receptor-tyrosine kinase inhibitor resistance.Frontiers in cell and developmental biology · 2026Article
- Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.Frontiers in immunology · 2026Article
- Revealing the Best Strategies for Rare Cell Type Detection in Multi-Sample Single-Cell Datasets.Genes · 2025Article
- Single-Cell Sequencing Unravels Pancreatic Cancer: Novel Technologies Reveal Novel Aspects of Cellular Heterogeneity and Inform Therapeutic Strategies.Biomedicines · 2025Review
- Article
- Tumor-Intrinsic Kinome Landscape of Pancreatic Cancer Reveals New Therapeutic Approaches.Cancer discovery · 2025Article
- CAFomics: convergence to translation for precision stroma approaches.Carcinogenesis · 2024Review
- scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.Nature communications · 2024Article
- Myofibroblastic cancer-associated fibroblast subtype heterogeneity in pancreatic cancer.Journal of surgical oncology · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- Integrative analysis of single-cell and bulk multi-omics data to reveal subtype-specific characteristics and therapeutic strategies in clear cell renal cell carcinoma patients.Journal of Cancer · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.bioRxiv : the preprint server for biology · 2023Article
- CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data.Briefings in bioinformatics · 2023Article
- Article
- Consistent analysis of differentially expressed genes across 7 cell types in papillary thyroid carcinoma.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
motivationSingle-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression.
resultsHere, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. AVAILABILITY AND IMPLEMENTATION: SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.
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Registered trials
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.