A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis
作者: Eugene LinSudipto MukherjeeSreeram Kannan
作者单位: 1Department of Electrical & Computer Engineering, University of Washington, 98195, Seattle, WA, USA
2Department of Biostatistics, University of Washington, 98195, Seattle, WA, USA
3Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
刊名: BMC Bioinformatics, 2020, Vol.21 (8), pp.1138-1142
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-020-3401-5
关键词: Adversarial autoencoderVariational autoencoderDimensionality reductionGenerative adversarial networksSingle-cell RNA sequencing
英文摘要: Abstract(#br)Background(#br)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). Results(#br)To 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...
全文获取路径: Springer Nature  (合作)
影响因子:3.024 (2012)