BACKGROUND Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). RESULTS To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. CONCLUSIONS Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

中文翻译：

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用于单细胞RNA测序分析中降维的深度对抗变分自动编码器模型。

背景技术单细胞RNA测序（scRNA-seq）是一种新兴技术，其可以以无偏倚的方式评估单个细胞的功能和单细胞水平的细胞间变异性。降维是scRNA-seq数据下游分析中必不可少的第一步。但是，由于传统方法的scRNA-seq数据具有高维测量以及大量的缺失事件（即零表达测量），因此具有挑战性。结果为了克服这些困难，我们提出了DR-A（具有对抗性变分自动编码器的降维方法），这是一种数据驱动的方法，可以完成降维任务。DR-A利用了新颖的基于对抗变式自动编码器的框架，这是生成对抗网络的一种变体。DR-A非常适合用于scRNA-seq数据的无监督学习任务，因为细胞类型的标签价格昂贵，而且通常无法获得。与现有方法相比，DR-A能够提供更准确的scRNA-seq数据的低维表示。我们通过利用DR-A对scRNA-seq数据进行聚类来说明这一点。结论我们的结果表明，DR-A与现有技术相比，显着增强了聚类性能。