Data denoising with transfer learning in single-cell transcriptomics.
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ABSTRACT:
SUBMITTER: Wang J
PROVIDER: S-EPMC7781045 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
Data denoising with transfer learning in single-cell transcriptomics.
Wang Jingshu J Agarwal Divyansh D Huang Mo M Hu Gang G Zhou Zilu Z Ye Chengzhong C Zhang Nancy R NR
Nature methods 20190830 9
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. ...[more]
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