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Updated: Jan 20, 2026

Transcriptome Analysis of Single Cells
Published on: April 25, 2011
Data denoising with transfer learning in single-cell transcriptomics
Jingshu Wang1, Divyansh Agarwal2, Mo Huang1
1Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA.
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.
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