模态(人机交互)
代表(政治)
转录组
计算生物学
资源(消歧)
受体
计算机科学
细胞
生物
人工智能
遗传学
基因
政治学
基因表达
法学
政治
计算机网络
作者
Yicheng Gao,Kejing Dong,Yixiao Gao,Xingkun Jin,Jingya Yang,Gang Yan,Qi Liu
标识
DOI:10.1016/j.xgen.2024.100553
摘要
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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