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Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

View ORCID ProfileYanshuo Chen, View ORCID ProfileYixuan Wang, Yuelong Chen, View ORCID ProfileYuqi Cheng, Yumeng Wei, Yunxiang Li, Jiuming Wang, Yingying Wei, View ORCID ProfileTing-Fung Chan, View ORCID ProfileYu Li
doi: https://doi.org/10.1101/2021.10.26.465846
Yanshuo Chen
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
2School of Life Sciences, Tsinghua University, 100084 Beijing, China
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  • ORCID record for Yanshuo Chen
Yixuan Wang
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
3Department of Mathematics, HIT, 264209 Weihai, China
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Yuelong Chen
4School of Life Sciences, CUHK, Hong Kong SAR, China
5State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
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Yuqi Cheng
6Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, 10065, USA
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Yumeng Wei
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
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Yunxiang Li
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
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Jiuming Wang
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
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Yingying Wei
7Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
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Ting-Fung Chan
4School of Life Sciences, CUHK, Hong Kong SAR, China
5State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
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  • For correspondence: liyu@cse.cuhk.edu.hk tf.chan@cuhk.edu.hk
Yu Li
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
8The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen, 518057, China
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  • For correspondence: liyu@cse.cuhk.edu.hk tf.chan@cuhk.edu.hk
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Abstract

Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

Competing Interest Statement

The authors have declared no competing interest.

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Posted October 14, 2022.
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Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
Yanshuo Chen, Yixuan Wang, Yuelong Chen, Yuqi Cheng, Yumeng Wei, Yunxiang Li, Jiuming Wang, Yingying Wei, Ting-Fung Chan, Yu Li
bioRxiv 2021.10.26.465846; doi: https://doi.org/10.1101/2021.10.26.465846
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Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
Yanshuo Chen, Yixuan Wang, Yuelong Chen, Yuqi Cheng, Yumeng Wei, Yunxiang Li, Jiuming Wang, Yingying Wei, Ting-Fung Chan, Yu Li
bioRxiv 2021.10.26.465846; doi: https://doi.org/10.1101/2021.10.26.465846

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