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

Although single-cell RNA sequencing (scRNA-seq) technologies have shed light on the role of cellular diversity in human pathophysiology, the resulting data remains noisy and sparse, making reliable quantification of gene expression challenging. Here, we show that a deep autoencoder coupled to a Bayesian model remarkably improves UMI-based scRNA-seq data quality by transfer learning across datasets. This new technology, SAVER-X, outperforms existing state-of-the-art tools. The deep learning model in SAVER-X extracts transferable gene expression features across data from different labs, generated by varying technologies, and obtained from divergent species. Through this framework, we explore the limits of transfer learning in a diverse testbed and demonstrate that future human sequencing projects will unequivocally benefit from the accumulation of publicly available data. We further show, through examples in immunology and neurodevelopment, that SAVER-X can harness existing public data to enhance downstream analysis of new data, such as those collected in clinical settings.