VASC
:: DESCRIPTION
VASC (deep Variational Autoencoder for SCRNA-seq data) is a deep multi-layer generative model, for the dimension reduction and visualization. It can do nonlinear hierarchical feature representations and model the dropout events of scRNA-seq data. Tested on more than twenty datasets, VASC show better performances in most cases and higher stability compared with several dimension reduction methods. VASC successfully re-establishes the embryo pre-implantation cell lineage and its associated genes based on the 2D representation of a large-scale scRNA-seq from human embryos.
::DEVELOPER
Bioinformatics & Intelligent Information Processing Research Group
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux
- Python 3.5+
- numpy 1.12.1
- h5py 2.7.0
- sklearn 0.18.1
- tensorflow 1.1.0
- keras 2.0.6
:: DOWNLOAD
:: MORE INFORMATION
Citation
VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder.
Genomics, Proteomics & Bioinformatics 2018, 16(5):320-331.
Dongfang Wang, Jin Gu