ArticleGenome biology2023
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- scXpand: Pan-cancer detection of T cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing.Cell genomics · 2026Article
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A pan-disease and population-level single-cell TCRαβ repertoire reference.Cell discovery · 2025Article
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.Scientific reports · 2025Article
- MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.Science advances · 2025Article
- Gut microbiota-mitochondrial crosstalk in obesity: novel mechanistic insights and therapeutic strategies with traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Unveiling the Genomic Symphony: Identification Cultivar-Specific Genes and Enhanced Insights on Sweet Sorghum Genomes Through Comprehensive superTranscriptomic Analysis.Journal of molecular evolution · 2024Article
- Identifying T-cell clubs by embracing the local harmony between TCR and gene expressions.Molecular systems biology · 2024Article
- Measuring single-cell immune clonality to track haematological cancers.Clinical and translational medicine · 2024Article
- Microglia in retinal angiogenesis and diabetic retinopathy.Angiogenesis · 2024Review
- scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.Genome biology · 2023Article
- Quantum graph embedding of transcription factor-gene networks reveals key modules in periodontal bone inflammation: Comparative analysis of GAE and GAN.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.