Data denoising with transfer learning in single-cell transcriptomics.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 31471617.
- Also identified by DOI 10.1038/s41592-019-0537-1 and PMC identifier 7781045.
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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.
Medical subject headings
- Breast Neoplasms
- Computational Biology
- Leukocytes, Mononuclear
- Sequence Analysis, RNA
- Single-Cell Analysis
- T-Lymphocytes
- Transcriptome