Data denoising with transfer learning in single-cell transcriptomics

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. SAVER-X trains a deep neural network for transfer learning that improves the quality of scRNA-seq data using prior information learnt from existing public studies.