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.