DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

John-William Sidhom (), H. Benjamin Larman, Drew M. Pardoll and Alexander S. Baras
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John-William Sidhom: Johns Hopkins University School of Medicine
H. Benjamin Larman: Johns Hopkins University School of Medicine
Drew M. Pardoll: Johns Hopkins University School of Medicine
Alexander S. Baras: Johns Hopkins University School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved ‘featurization’ of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.

Date: 2021
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DOI: 10.1038/s41467-021-21879-w

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