In this comment on Davidsen et al., "Deep generative models for T cell receptor protein sequences ", eLife 2019;8:e46935, we compare the performance of the variational auto-encoder presented in that article to a previously proposed approachfor which a software implementation, SONIA, has been recently released. We find that SONIA performs as well as the variational auto-encoder , at a lower computational cost.
T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free Variational Auto-Encoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable , at a lower computational cost.
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In this comment on Davidsen et al., "Deep generative models for T cell receptor protein sequences ", eLife 2019;8:e46935, we compare the performance of the variational auto-encoder presented in that article to a previously proposed approachfor which a software implementation, SONIA, has been recently released. We find that SONIA performs as well as the variational auto-encoder
T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free Variational Auto-Encoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable