Data denoising with transfer learning in single-cell transcriptomics.

ABSTRACT: 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.

SUBMITTER: Wang J 

PROVIDER: S-EPMC7781045 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature