A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

Eugene Lin, Sudipto Mukherjee, Sreeram Kannan
2020 BMC Bioinformatics  
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). To overcome
more » ... se 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. Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.
doi:10.1186/s12859-020-3401-5 pmid:32085701 fatcat:v3qyw26nzna7ljem4pdvfqzzoq