一种单细胞转录组与免疫组库的整合方法

Single-cell RNA sequencing (scRNA-seq) and single-cell T-cell receptor sequencing (scTCR-seq) are pivotal for deciphering immune cell characteristics, offering complementary insights into the immune system′s complexity through gene expression and antigen recognition, respectively. However, conventional analytical methods are often confined to a single modality, hindering the effective integration of complementary information from the two omics. To overcome this limitation and achieve efficient integration of cross-omics data, this study proposes a novel data integration framework named scRTIA (single-cell RNA and TCR integrative analysis). Based on deep learning theory, the model employs a multimodal variational autoencoder and a Transformer as its core architecture, jointly embedding the scRNA-seq gene expression matrix and scTCR-seq TCR sequence features into a unified low-dimensional latent space, thereby constructing a fused cell representation that simultaneously preserves transcriptomic features and immune repertoire information. Experimental validation on real datasets demonstrates that the cell representations generated by scRTIA exhibit significantly superior resolution in identifying cell subpopulations, enabling the discovery of rare T-cell populations with specific functional states that are difficult to detect using traditional methods. By effectively integrating transcriptomic and immunome data, our work transcends the limitations of single-modal analysis and enables a multidimensional characterization of T-cell identity and function, offering valuable insights for immune-related diseases research and precision medicine.