SAVER-X: Data denoising with transfer learning in single-cell transcriptomics

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SAVER-X

Data denoising with transfer learning in single-cell transcriptomics

ID：30360Uploader：BioTreasury

2022.01.19

http://singlecell.wharton.upenn.edu/saver-x/

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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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Publication

DOI：10.1038/S41592-019-0537-1

PMID：31471617

PMCID：PMC7781045

Data denoising with transfer learning in single-cell transcriptomics

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