Data Denoising with transfer learning in single-cell transcriptomics
Jingshu WangiD, Divyansh AgarwaliD, Mo Chao HuangiD, Gang HuiD, Zilu ZhouiD, Chengzhong YeiD, Nancy Ruonan ZhangiD
Abstract
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.
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