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 Jingshu Wang
A1 Divyansh Agarwal
A1 Mo Huang
A1 Gang Hu
A1 Zilu Zhou
A1 Chengzhong Ye
A1 Nancy R. Zhang
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.