Fig. 1: Outline of the SAVER-X transfer learning framework. | Nature Methods

Fig. 1: Outline of the SAVER-X transfer learning framework.

From: Data denoising with transfer learning in single-cell transcriptomics

Fig. 1

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).

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