VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can

SUBMITTER: Wang D 

PROVIDER: S-EPMC6364131 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature