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Data Denoising with transfer learning in single-cell transcriptomics

Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, Nancy R. Zhang
doi: https://doi.org/10.1101/457879
Jingshu Wang
1Department of Statistics, University of Pennsylvania, Philadelphia, PA
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Divyansh Agarwal
2Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA
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Mo Huang
1Department of Statistics, University of Pennsylvania, Philadelphia, PA
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Gang Hu
3School of Mathematical Sciences, Nankai University, Tianjin, China
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Zilu Zhou
2Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA
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Chengzhong Ye
4School of Medicine, Tsinghua University, Beijing, China
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Nancy R. Zhang
1Department of Statistics, University of Pennsylvania, Philadelphia, PA
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  • For correspondence: nzh@wharton.upenn.edu
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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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Posted August 05, 2019.
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Data Denoising with transfer learning in single-cell transcriptomics
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, Nancy R. Zhang
bioRxiv 457879; doi: https://doi.org/10.1101/457879
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Data Denoising with transfer learning in single-cell transcriptomics
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, Nancy R. Zhang
bioRxiv 457879; doi: https://doi.org/10.1101/457879

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