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.
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.
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.
nature.com