PT  - JOURNAL ARTICLE
AU  - Jingshu Wang
AU  - Divyansh Agarwal
AU  - Mo Huang
AU  - Gang Hu
AU  - Zilu Zhou
AU  - Chengzhong Ye
AU  - Nancy R. Zhang
TI  - Data Denoising with transfer learning in single-cell transcriptomics
AID  - 10.1101/457879
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 457879
4099  - http://biorxiv.org/content/early/2019/08/05/457879.short
4100  - http://biorxiv.org/content/early/2019/08/05/457879.full
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.