Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.

ABSTRACT: Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and hence, such cellular variability impedes subsequent differential gene expression analyses. To overcome such a heterogeneity issue, we present a novel deep learning approach, scIDST, that infers disease progression levels of individual cells with weak supervision framework. The disease progression-inferred cells display significant differential expression of disease-relevant genes, which cannot be detected by comparative analysis between patients and healthy donors. In addition, we demonstrate that pretrained models by scIDST are applicable to multi

SUBMITTER: Wehbe F 

PROVIDER: S-EPMC11789631 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature