Data denoising with transfer learning in single-cell transcriptomics.
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Data denoising with transfer learning in single-cell transcriptomics.

Wang, Jingshu;Agarwal, Divyansh;Huang, Mo;Hu, Gang;Zhou, Zilu;Ye, Chengzhong;Zhang, Nancy R;
Nature Methods 2019 Vol. 16 pp. 875-878
305
wang2019datanature

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.

Keywords

apoptosis curcumin endoplasmic reticulum stress palmitic acid leydig cell

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DOI:
10.1038/s41592-019-0537-1

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