Deep generative models for T cell receptor protein sequences
Deep generative models for T cell receptor protein sequences
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DOI：
10.7554/elife.46935
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发表时间：
2019-09-05
期刊：
影响因子：
7.7
通讯作者：
Matsen, Frederick A.
中科院分区：
文献类型：
--
作者：
Davidsen, Kristian;Olson, Branden J.;Matsen, Frederick A.
适应性免疫谱系序列分布的概率模型可以用来推断免疫细胞对刺激的反应，区分决定免疫谱系共享的遗传因素和环境因素，以及评估各种靶免疫序列对疫苗刺激的适用性。经典地，这些模型是根据概率V(D)J重组模型来定义的，该模型有时与选择模型相结合。在本文中，我们采取了一种不同的方法，将由深度神经网络参数化的变分自动编码器(VAE)模型拟合到T细胞受体(TCR)谱系中。我们发现，简单的VAE模型可以准确地进行队列频率估计，学习VDJ重组的规则，并很好地推广到未知序列。进一步，我们证明了类VAE模型可以区分实序列和根据重组-选择模型生成的序列，并且VAE生成的序列的许多特征与实序列的特征相似。
Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.