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Data Denoising with transfer learning in single-cell transcriptomics
Single-cell RNA sequencing (scRNA-seq) data is 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...
Αποθηκεύτηκε σε:
| Τόπος έκδοσης: | Nat Methods |
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| Κύριοι συγγραφείς: | , , , , , , |
| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
2019
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| Θέματα: | |
| Διαθέσιμο Online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7781045/ https://ncbi.nlm.nih.gov/pubmed/31471617 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41592-019-0537-1 |
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