ArticleNature methods2024
SIMBA: single-cell embedding along with features.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer's disease.Nature communications · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.Briefings in bioinformatics · 2026Article
- Robust integration of single-cell datasets with imbalanced modality composition.Nature communications · 2026Article
- GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.Briefings in bioinformatics · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- Integrating natural and engineered genetic variations to decode regulatory influence on blood traits.Cell reports · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Tumor Genomic and Transcriptomic Analysis Integrated With Liquid Biopsy ctDNA Monitoring: Analytical Validation and Clinical Insights.Cancer medicine · 2025Article
- The cell as a token: high-dimensional geometry in language models and cell embeddings.Bioinformatics (Oxford, England) · 2025Review
- scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.Bioinformatics (Oxford, England) · 2025Article
- Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data.Briefings in bioinformatics · 2025Article
- Learning interpretable representations of single-cell multi-omics data with multi-output Gaussian processes.Nucleic acids research · 2025Article
- CellWalker2: Multi-omic discovery using hierarchical cell type relationships.Cell genomics · 2025Article
- DeepNanoHi-C: deep learning enables accurate single-cell nanopore long-read data analysis and 3D genome interpretation.Nucleic acids research · 2025Article
- scRECL: representative ensembles with contrastive learning for scRNA-seq data clustering analysis.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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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 Socratic graph.