ArticleNature methods2023
Robust single-cell matching and multimodal analysis using shared and distinct features.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 49 citations in OpenAlex.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.Briefings in bioinformatics · 2026Article
- Article
- Single-cell data integration across weakly linked modalities.PLoS computational biology · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics.bioRxiv : the preprint server for biology · 2025Article
- A unified multimodal single-cell framework reveals a discrete state model of hematopoiesis in mice.Nature immunology · 2025Article
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
- From Microbial Switches to Metabolic Sensors: Rewiring the Gut-Brain Kynurenine Circuit.Biomedicines · 2025Review
- COEXIST: Coordinated single-cell integration of serial multiplexed tissue images.PLoS computational biology · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
- Spatial phenotyping of human bronchial airways in obstructive lung disease.Respiratory research · 2025Article
- CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data.Nature immunology · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Quantitative characterization of tissue states using multiomics and ecological spatial analysis.Nature genetics · 2025Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.
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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.