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

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 explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10X Genomics dataset with more cells and higher dropout rate.
PMID: 30576740 [PubMed - as supplied by publisher]

Source: Genomics Proteomics ... - December 18, 2018 Category: Genetics & Stem Cells Authors: Wang D, Gu J Tags: Genomics Proteomics Bioinformatics Source Type: research