ArticleCell reports methods2025
Single-cell multiomics data integration and generation with scPairing.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- Clonal Metamorphosis: Deconstructing MPN Evolution with Single-Cell and Spatial Multi-Omics.Clinical and experimental medicine · 2026Review
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Metabolic-Epigenetic Crosstalk in Takayasu Arteritis: The ANK2-MAVS-IL-8 Axis as a Novel Therapeutic Paradigm.International journal of molecular sciences · 2026Review
- Multi-omics insights into spondyloarthritis and psoriatic arthritis: integrating genomics, transcriptomics, proteomics, and the microbiome for immunological and clinical translation.Frontiers in immunology · 2026Review
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
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Authors and funding
3 authors.
Funding
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Abstract
Single-cell multiomics technologies generate paired measurements of different cellular modalities, such as gene expression and chromatin accessibility. However, multiomics technologies are more expensive than their unimodal counterparts, resulting in smaller and fewer available multiomics datasets. Here, we present scPairing, a deep learning model inspired by contrastive language-image pre-training (CLIP), which embeds different modalities from the same single cells onto a common embedding space. We leverage the common embedding space to generate novel multiomics data following bridge integration, a method that uses an existing multiomics bridge to link unimodal data. Through extensive benchmarking, we show that scPairing constructs an embedding space that fully captures both coarse and fine biological structures. We then use scPairing to generate new multiomics data from retina, immune, and renal cells. Furthermore, we extend scPairing to generate trimodal data. The generated multiomics datasets can facilitate the discovery of novel cross-modality relationships and the validation of existing biological hypotheses.
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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.