Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery
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.
bioinformatics↗