Identifying T-cell clubs by embracing the local harmony between TCR and gene expressions

T cell receptors (TCR) and gene expression provide two complementary and essential aspects in T cell understanding, yet their diversity presents challenges in integrative analysis. We introduce TCRclub, a novel method integrating single-cell RNA sequencing data and single-cell TCR sequencing data using local harmony to identify functionally similar T cell groups, termed ‘clubs’. We applied TCRclub to 298,106 T cells across seven datasets encompassing various diseases. First, TCRclub outperforms the state-of-the-art methods in clustering T cells on a dataset with over 400 verified peptide-major histocompatibility complex categories. Second, TCRclub reveals a transition from activated to exhausted T cells in cholangiocarcinoma patients. Third, TCRclub discovered the pathways that could intervene in response to anti-PD-1 therapy for patients with basal cell carcinoma by analyzing the pre-treatment and post-treatment samples. Furthermore, TCRclub unveiled different T-cell responses and gene patterns at different severity levels in patients with COVID-19. Hence, TCRclub aids in developing more effective immunotherapeutic strategies for cancer and infectious diseases. Synopsis TCRclub integrates scRNA-seq and scTCR-seq data by local harmony to identify functionally similar T cell groups, termed ‘clubs’, enhancing the understanding of T cell diversity. TCRclub models the relationship between pairwise TCR embedding distances and pairwise expression distances, emphasizing local homogeneity to integrate TCRs and gene expressions. TCRclub considers the nested cell hierarchy to capture the cellular functional diversity of T cells. TCRclub demonstrates better performance in clustering T cells into pMHC-specificity clusters. TCRclub unveils the functional landscape of T cells in diverse biological contexts, including cholangiocarcinoma, Anti-PD1 therapy, and SARS-CoV-2 infection. TCRclub integrates scRNA-seq and scTCR-seq data by local harmony to identify functionally similar T cell groups, termed ‘clubs’, enhancing the understanding of T cell diversity.