ArticleGenome biology2024
A comparison of marker gene selection methods for single-cell RNA sequencing data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
79 citing papers in PubMed.
- Molecular characterization of normal and physiologically suppressed human parathyroid glands at single-cell resolution.Journal of the Endocrine Society · 2026Article
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- A comparative single-cell transcriptomic atlas for diverse populations of vertebrate hair cells.bioRxiv : the preprint server for biology · 2026Article
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- Transplantation of human iPSC-derived microglia ameliorates neuropathology and circuit dysfunction in progranulin-deficient mice.Molecular neurodegeneration · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- Protocol for purifying and sequencing cell subpopulations based on RNA FISH signal in planaria.STAR protocols · 2026Article
- Expanding canonical cortical cell type markers in the era of single-cell transcriptomics.Scientific reports · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Yomix: an interactive tool for the exploration of low-dimensional embeddings in omics data.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Characterizing gene perturbations in single cells via network divergence analysis.Nature communications · 2026Article
- Locat: Joint enrichment and depletion testing identifies localized marker genes in single-cell transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- CAR-T cells targeting activated hepatic stellate cells ameliorate liver fibrosis in mouse models.JHEP reports : innovation in hepatology · 2026Article
- Function and Development of Deep-sea Mussel Bacteriocytes Revealed by snRNA-seq and Spatial Transcriptomics.Genomics, proteomics & bioinformatics · 2026Article
- CANTAO: guiding clustering and annotation in single-cell RNA sequencing using average overlap.Molecular systems biology · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Identification of key regulators for the development of Prunus leaves using single-cell RNA sequencing.BMC plant biology · 2026Article
- Inference of marker genes of subtle cell state changes via iLR: iterative logistic regression.Bioinformatics (Oxford, England) · 2026Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundThe development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data.
resultsWe compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.
conclusionsOverall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student's t-test, and logistic regression.
Indexed as
Identifiers
What OpenQuestion holds
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.