ArticleNature communications2022
A unified computational framework for single-cell data integration with optimal transport.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 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
- Regulatory-prior-guided attention preserves biological structure during unpaired single-cell RNA-ATAC integration.Bioinformatics (Oxford, England) · 2026Article
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Robust integration of single-cell datasets with imbalanced modality composition.Nature communications · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Single-cell data integration across weakly linked modalities.PLoS computational biology · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization.PLoS computational biology · 2026Article
- STGNET: extending panel coverage in imaging-based spatial transcriptomics using deep generative adversarial networks.Briefings in bioinformatics · 2026Article
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- An interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis.GigaScience · 2026Article
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- Optimal transport analysis of high-dimensional flow cytometry data in immuno-oncology.Frontiers in immunology · 2026Article
- SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.Bioinformatics advances · 2026Article
- 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
- Haplotype-resolved T2T genome assembly of the Populus nigra NL-1976.Scientific data · 2025Article
4 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Single-cell data integration can provide a comprehensive molecular view of cells. However, how to integrate heterogeneous single-cell multi-omics as well as spatially resolved transcriptomic data remains a major challenge. Here we introduce uniPort, a unified single-cell data integration framework that combines a coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration to handle the heterogeneity across datasets, and it is scalable to large-scale datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for gene imputation across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute heterogeneous spatial transcriptomic data using an optimal transport plan, instead of embedding latent space. We demonstrate the capability of uniPort by applying it to integrate a variety of datasets, including single-cell transcriptomics, chromatin accessibility, and spatially resolved transcriptomic data.
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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.