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ScNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

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.

SUBMITTER: Zhu B

PROVIDER: S-EPMC10726524 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Zhu Biqing B Wang Yuge Y Ku Li-Ting LT van Dijk David D Zhang Le L Hafler David A DA Zhao Hongyu H

Genome biology 20231218 1

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 effec ...[more]

PMID: 38111007

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