Data denoising with transfer learning in single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, UPENN researchers 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.

Outline of the SAVER-X transfer learning framework


a, The autoencoder pretraining step. b, Workflow of SAVER-X. For target data with a UMI count matrix, SAVER-X trains the target data with autoencoder without a chosen pretraining model (item A), then filters unpredictable genes using cross-validation (item B) and estimates the final denoised values with empirical Bayesian shrinkage (item C).

Availability – SAVER-X is publicly available at

Wang J, Agarwal D, Huang M, Hu G, Zhou Z, Ye C, Zhang NR. (2019) Data denoising with transfer learning in single-cell transcriptomics. Nat Methods 16(9):875-878. [abstract]

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