Data denoising with transfer learning in single-cell transcriptomics.
Clicks: 264
ID: 43610
2019
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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wang2019datanature
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| 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 |
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