RT Journal Article
SR Electronic
T1 Data Denoising with transfer learning in single-cell transcriptomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 457879
DO 10.1101/457879
A1 Wang, Jingshu
A1 Agarwal, Divyansh
A1 Huang, Mo
A1 Hu, Gang
A1 Zhou, Zilu
A1 Ye, Chengzhong
A1 Zhang, Nancy R.
YR 2019
UL http://biorxiv.org/content/early/2019/08/05/457879.abstract
AB Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.