ScDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.

ABSTRACT: The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and

SUBMITTER: Zhao L 

PROVIDER: S-EPMC11540133 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature