Abstract
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.
Highlights
Single-cell RNA sequencing 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
The main contributions of this work are as follows: In this work, we propose a novel Generative Adversarial Networks (GANs)-based architecture, which we refer to as DR-A (Dimensionality Reduction with Adversarial variational autoencoder), for dimensionality reduction in scRNA-seq analysis
Overview of DR-A DR-A represents a deep adversarial variational autoencoderbased framework, which combines the concepts of two deep learning models including Adversarial AutoEncoder [19] and Variational AutoEncoder [22]
Summary
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. 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). Dimension reduction is crucial for analysis of scRNA-seq data because the high dimensional scRNA-seq measurements for a large number of genes and cells may contain high level of technical and biological noise [2]. A special characteristic of scRNA-seq data is that it contains an abundance of zero expression measurements that could be either due to biological or technical causes. This phenomenon of zero measurements due to technical reasons is often referred. A limitation of ZIFA, is that the zero-inflation model may not be
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