ArticleCell genomics2024
Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- CoMBCR: Co-Learning Multi-Modalities of BCRs and gene expressions.Bioinformatics (Oxford, England) · 2026Article
- Role of the neurotransmitter-receptor pathway in T-cell tumor immunology and cancer immunotherapy.Acta biochimica et biophysica Sinica · 2026Review
- scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.Nature communications · 2025Article
- Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.Research square · 2025Article
- Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.bioRxiv : the preprint server for biology · 2025Article
- OmniNeo: a multi-omics pipeline incorporating proteomics and AI selection for neoantigen optimization in tumor immunotherapy.Frontiers in immunology · 2025Article
- TCR clustering by contrastive learning on antigen specificity.Briefings in bioinformatics · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
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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.