ArticleCell reports methods2024
Directly selecting cell-type marker genes for single-cell clustering analyses.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- Investigating the role of inflammatory bowel disease-associated gene expression in oral cancer using single-cell RNA sequencing.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- On feature selection to disentangle cell type and state transcriptional programs.BMC genomics · 2025Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Ocular surface health and disease: insight from single-cell RNA sequencing.Frontiers in genetics · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8
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