ArticleNucleic acids research2022
Cluster-independent marker feature identification from single-cell omics data using SEMITONES.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Single-cell marker gene clustering: A unified deep learning framework for marker gene-based clustering of single-cell RNA-sequencing data.Quantitative biology (Beijing, China) · 2026Article
- Evolutionary diversity of cell-type-specific expression and stress response in Brassicaceae roots.Nature communications · 2026Article
- Evolutionary diversity of cell-type-specific expression and stress response in Brassicaceae roots.bioRxiv : the preprint server for biology · 2026Article
- stGCL: a versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics.Genome biology · 2026Article
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.bioRxiv : the preprint server for biology · 2024Article
- Probe set selection for targeted spatial transcriptomics.Nature methods · 2024Article
- Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.International journal of molecular sciences · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of cell identity markers is an essential step in single-cell omics data analysis. Current marker identification strategies typically rely on cluster assignments of cells. However, cluster assignment, particularly for developmental data, is nontrivial, potentially arbitrary, and commonly relies on prior knowledge. In response, we present SEMITONES, a principled method for cluster-free marker identification. We showcase and evaluate its application for marker gene and regulatory region identification from single-cell data of the human haematopoietic system. Additionally, we illustrate its application to spatial transcriptomics data and show how SEMITONES can be used for the annotation of cells given known marker genes. Using several simulated and curated data sets, we demonstrate that SEMITONES qualitatively and quantitatively outperforms existing methods for the retrieval of cell identity markers from single-cell omics data.
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