ArticleJournal of computational biology : a journal of computational molecular cell biology2022
SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed.
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Semi-metric optimal transport enables robust spatial multi-omics integration.Research square · 2026Article
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.Nature methods · 2026Article
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- Exploring the structural lexicon of the Proteome via Metric Geometry.PLoS computational biology · 2026Article
- Optimal transport for label transfer in single-cell multi-omics integration.Briefings in bioinformatics · 2026Article
- GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.Briefings in bioinformatics · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Multimodal spatial alignment and morphology mapping with MOSAICField.bioRxiv : the preprint server for biology · 2026Article
- Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- An interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis.GigaScience · 2026Article
- Deriving genetic codes for molecular phenotypes from first principles.bioRxiv : the preprint server for biology · 2026Article
- SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.Bioinformatics advances · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping.bioRxiv : the preprint server for biology · 2025Article
16 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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Abstract
Recent advances in sequencing technologies have allowed us to capture various aspects of the genome at single-cell resolution. However, with the exception of a few of co-assaying technologies, it is not possible to simultaneously apply different sequencing assays on the same single cell. In this scenario, computational integration of multi-omic measurements is crucial to enable joint analyses. This integration task is particularly challenging due to the lack of sample-wise or feature-wise correspondences. We present single-cell alignment with optimal transport (SCOT), an unsupervised algorithm that uses the Gromov-Wasserstein optimal transport to align single-cell multi-omics data sets. SCOT performs on par with the current state-of-the-art unsupervised alignment methods, is faster, and requires tuning of fewer hyperparameters. More importantly, SCOT uses a self-tuning heuristic to guide hyperparameter selection based on the Gromov-Wasserstein distance. Thus, in the fully unsupervised setting, SCOT aligns single-cell data sets better than the existing methods without requiring any orthogonal correspondence information.
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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.