ArticleNature methods2020
MARS: discovering novel cell types across heterogeneous single-cell experiments.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interpretation of T cell states from single-cell transcriptomics data using reference atlases.Nature communications · 2021Pooled it
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- MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.Interdisciplinary sciences, computational life sciences · 2026Article
- DBP: Adaptive and Interpretable Factor Analysis for Single-cell RNA-seq Data with Deep Beta Processes.Genomics, proteomics & bioinformatics · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Dialectics for Artificial Intelligence.Entropy (Basel, Switzerland) · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment.Genome research · 2026Article
- Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data.BMC bioinformatics · 2026Article
- Towards a reference cell atlas of liver diversity over the human lifespan.Nature reviews. Gastroenterology & hepatology · 2026Review
- Single-cell sequencing and organoids: applications in organ development and disease.Molecular biomedicine · 2025Review
- The cell as a token: high-dimensional geometry in language models and cell embeddings.Bioinformatics (Oxford, England) · 2025Review
- CellWalker2: Multi-omic discovery using hierarchical cell type relationships.Cell genomics · 2025Article
- Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.BMC bioinformatics · 2025Article
- scMUSCL: multi-source transfer learning for clustering scRNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- M-band wavelet-based multi-view clustering of cells.PLoS computational biology · 2025Article
- Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery.Nature communications · 2025Article
- Article
19 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space.
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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.