Data denoising with transfer learning in single-cell transcriptomics.

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ID: 43610
2019
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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.
Reference Key
wang2019datanature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Jingshu;Agarwal, Divyansh;Huang, Mo;Hu, Gang;Zhou, Zilu;Ye, Chengzhong;Zhang, Nancy R;
Journal Nature Methods
Year 2019
DOI
10.1038/s41592-019-0537-1
URL
Keywords
apoptosis curcumin endoplasmic reticulum stress palmitic acid leydig cell

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