DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.

ABSTRACT:

Background

Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.

Results

Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is a

SUBMITTER: Hausmann F 

PROVIDER: S-EPMC10510283 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature