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scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
oleh: Biqing Zhu, Yuge Wang, Li-Ting Ku, David van Dijk, Le Zhang, David A. Hafler, Hongyu Zhao
Format: | Article |
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Diterbitkan: | BMC 2023-12-01 |
Deskripsi
Abstract Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.