ArticleNature biotechnology2024
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Spatial multi-omics imputation and embedding with SpaMIE.Cell reports methods · 2026Article
- A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.BMC bioinformatics · 2026Article
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- MIND: multimodal integration with neighbourhood-aware distributions.Nature communications · 2026Article
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- 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
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.Briefings in bioinformatics · 2026Article
- Mosaic integration of spatial multi-omics with SpaMosaic.Nature genetics · 2026Article
- Machine learning predictions surpass individual mRNAs as a proxy of single-cell protein expression.Genome biology · 2026Article
- Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs.Cell genomics · 2026Article
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
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15 authors.
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Abstract
Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .
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