Data Denoising with transfer learning in single-cell transcriptomics

2019 
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 conditions, and divergent species to denoise target new datasets.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    6
    Citations
    NaN
    KQI
    []