ArticleNature communications2021
Optimal marker gene selection for cell type discrimination in single cell analyses.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- RECOMBINE identifies recurrent composite markers of cell types and states.Genome research · 2026Article
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.Briefings in bioinformatics · 2026Article
- A Mouthful of Genomic Data: Single-Cell Insights into Salivary Gland Biology and Disease.Biology · 2026Review
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- Inference of marker genes of subtle cell state changes via iLR: iterative logistic regression.Bioinformatics (Oxford, England) · 2026Article
- Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.Briefings in bioinformatics · 2026Article
- scBrainScope: cross-species multidimensional brain atlas.Nucleic acids research · 2026Article
- Integrated single-cell and bulk transcriptomic analyses reveal a stem-like epithelial subpopulation in adenocarcinoma of the esophagogastric junction and identify VASN as a novel regulator of tumor stemness.Frontiers in immunology · 2026Article
- Optimal marker genes forGenome research · 2025Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Fluctuation structure predicts genome-wide perturbation outcomes.Research square · 2025Article
- spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model.bioRxiv : the preprint server for biology · 2025Article
- MuST: multiple-modality structure transformation for single-cell spatial transcriptomics.Briefings in bioinformatics · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.
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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.