A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis
A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis
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DOI：
10.1186/s12859-020-3401-5
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发表时间：
2020-02-21
影响因子：
3
通讯作者：
Kannan, Sreeram
中科院分区：
文献类型：
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作者：
Lin, Eugene;Mukherjee, Sudipto;Kannan, Sreeram
背景单细胞RNA测序（scRNA-seq）是一种新兴技术，可以在单细胞水平上以无偏的方式评估单个细胞的功能和细胞间的变异性。减少重复性是scRNA-seq数据下游分析中必不可少的第一步。然而，scRNA-seq数据对于传统方法是具有挑战性的，因为它们的高维测量以及大量的脱落事件（即，零表达测量）。结果为了克服这些困难，我们提出了一种数据驱动的降维方法DR-A（Adversarial Variational Autoencoder）。DR-A利用了一种新的基于变分自动编码器的对抗框架，这是生成对抗网络的一种变体。DR-A非常适合scRNA-seq数据的无监督学习任务，其中细胞类型的标签成本很高，而且通常不可能获得。与现有方法相比，DR-A能够提供scRNA-seq数据的更准确的低维表示。我们通过利用DR-A对scRNA-seq数据进行聚类来说明这一点。结论我们的研究结果表明，DR-A显着提高聚类性能的国家的最先进的方法。
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.