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

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

SUBMITTER: Chen Y 

PROVIDER: S-EPMC9641692 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature