ArticleNature communications2025
Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Modeling pediatric low-grade glioma heterogeneity using human forebrain organoids.Molecular cancer · 2026Article
- DNMT3A R882H Is Not Required for Disease Maintenance in Primary Human AML but Is Associated with Increased Leukemia Stem Cell Frequency.Cancer discovery · 2026Article
- scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data.Nucleic acids research · 2026Article
- RBM15-MKL1 fusion protein promotes leukemia via m6A methylation and Wnt pathway activation.Blood · 2025Article
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.Nature communications · 2025Article
- Dissecting the stress granule RNA world: dynamics, strategies, and data.RNA (New York, N.Y.) · 2025Review
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22 authors.
Funding
Abstract
Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in health and disease. However, reliable identification of cell types and states represents a bottleneck. Available databases and analysis tools employ dissimilar markers, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, limiting their applicability to disease. We present the Cell Marker Accordion, a user-friendly platform providing automatic annotation and unmatched biological interpretation of single-cell populations, based on consistency weighted markers. We validate our approach on multiple single-cell and spatial datasets from different human and murine tissues, improving annotation accuracy in all cases. Moreover, we show that the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of disease contexts. The breadth of these applications elevates the Cell Marker Accordion as a fast, flexible, faithful and standardized tool to annotate and interpret single-cell and spatial populations in studying physiology and disease.
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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.