• Eugene Lin, Sudipto Mukherjee, and Sreeram Kannan.
• Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.
• Bmc Bioinformatics. 2020 Feb 21; 21 (1): 64.

BackgroundSingle-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).ResultsTo 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.ConclusionsOur results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

10 keywords...

hide…