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 () and Yu Li ()
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Yanshuo Chen: CUHK
Yixuan Wang: CUHK
Yuelong Chen: CUHK
Yuqi Cheng: Weill Cornell Medicine
Yumeng Wei: CUHK
Yunxiang Li: CUHK
Jiuming Wang: CUHK
Yingying Wei: The Chinese University of Hong Kong
Ting-Fung Chan: CUHK
Yu Li: CUHK

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34550-9

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