Deep Autoencoder for Interpretable Tissue-adaptive Deconvolution and Cell-type-specific Gene Analysis

Overview

Journal Nat Commun

Specialty Biology

Date 2022 Nov 8

PMID 36347853

Authors

Yanshuo Chen

Yixuan Wang

Yuelong Chen

Yuqi Cheng

Yumeng Wei

YunXiang Li

Jiuming Wang

Yingying Wei

Ting-Fung Chan

Yu Li

Affiliations

Soon will be listed here.

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.

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