ArticleProceedings of the National Academy of Sciences of the United States of America2022
Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Proteomic profiling across early postnatal development of midbrain dopamine axons innervating the dorsal striatum.bioRxiv : the preprint server for biology · 2026Article
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 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
- Spatial multi-omics imputation and embedding with SpaMIE.Cell reports methods · 2026Article
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- T2Pdecoder enables protein-centric analyses from transcriptomic data.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
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?Briefings in bioinformatics · 2026Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- Multi-omics integration and batch correction using a modality-agnostic deep learning framework.bioRxiv : the preprint server for biology · 2025Article
- ACE: A Versatile Contrastive Learning Framework for Single-cell Mosaic Integration.Genomics, proteomics & bioinformatics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
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3 authors.
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Abstract
Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and impute multimodal datasets with mosaic measurements. A key advance is the use of a missing mask for learning the conditional distribution of unobserved modalities and features, which makes scVAEIT flexible to combine different panels of measurements from multimodal datasets accurately and in an end-to-end manner. Imputing the masked features serves as a supervised learning procedure while preventing overfitting by regularization. Focusing on gene expression, protein abundance, and chromatin accessibility, we validate that scVAEIT robustly imputes the missing modalities and features of cells biologically different from the training data. scVAEIT also adjusts for batch effects while maintaining the biological variation, which provides better latent representations for the integrated datasets. We demonstrate that scVAEIT significantly improves integration and imputation across unseen cell types, different technologies, and different tissues.
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