ArticleJournal of computational biology : a journal of computational molecular cell biology2022
SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- 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
- Joint representation learning for oncology applications.Bioinformatics (Oxford, England) · 2026Article
- SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.Bioinformatics advances · 2026Article
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- Exploring the Structural Lexicon of the Proteome via Metric Geometry.bioRxiv : the preprint server for biology · 2025Article
- scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation.PLoS computational biology · 2025Article
- Debiasing Sinkhorn divergence in optimal transport of cellular dynamics.bioRxiv : the preprint server for biology · 2025Article
- Scalable integration of multiomic single-cell data using generative adversarial networks.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when there is disproportionate cell-type representation across measurement domains. Therefore, we extend our previous work-Single Cell alignment using Optimal Transport (SCOT)-by using unbalanced Gromov-Wasserstein optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. Our method, SCOTv2, gives state-of-the-art alignment performance across five non-coassay data sets (simulated and real world). It can also integrate multiple (
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