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 sc...

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Bibliographic Details
Published in:Genome Biology
Main Authors: Biqing Zhu, Yuge Wang, Li-Ting Ku, David van Dijk, Le Zhang, David A. Hafler, Hongyu Zhao
Format: Article
Language:English
Published: BMC 2023-12-01
Subjects:
scRNA-seq
scTCR-seq
Data integration
Deep learning
Variational autoencoder
Clone expansion
Online Access:https://doi.org/10.1186/s13059-023-03129-y
Description
Summary: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.
ISSN:1474-760X

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