Data denoising with transfer learning in single-cell transcriptomics.
Nat Methods. 2019 09;16(9):875-878
Authors: Wang J, Agarwal D, Huang M, Hu G, Zhou Z, Ye C, Zhang NR
Single-cell RNA sequencing (scRNA-seq) data are 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 new target datasets.
PMID: 31471617 [PubMed – indexed for MEDLINE]