ArticleNature communications2023
scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.BMC bioinformatics · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- MIND: multimodal integration with neighbourhood-aware distributions.Nature communications · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Robust integration of single-cell datasets with imbalanced modality composition.Nature communications · 2026Article
- CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.Nature communications · 2026Article
- GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.Briefings in bioinformatics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Mosaic integration of spatial multi-omics with SpaMosaic.Nature genetics · 2026Article
- Orthogonal disentanglement of single-cell multi-omics reveals private and shared drivers of tissue development and pathogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multimodal spatial alignment and morphology mapping with MOSAICField.bioRxiv : the preprint server for biology · 2026Article
- Toward Computationally Complete Spatial Omics.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking LLM-based agents for single-cell omics analysis.Genome biology · 2026Article
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- Aligned cross-modal integration and regulatory heterogeneity characterization of single-cell multiomic data with deep contrastive learning.Genome medicine · 2026Article
- Guided co-clustering transfer across unpaired and paired single-cell multi-omics data.Bioinformatics (Oxford, England) · 2025Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
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10 authors.
Funding
Abstract
Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.
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