RT Journal
A1 Wehbe, Fabien
A1 Adams, Levi
A1 Babadoudou, Jordan
A1 Yuen, Samantha
A1 Kim, Yoon-Seong
A1 Tanaka, Yoshiaki
T1 Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
JF Genome Research 
JO Genome Research 
YR 2025 
FD January 01 
VO 35 
IS 1 
SP 135 
OP 146 
DO 10.1101/gr.278812.123 
UL http://genome.cshlp.org/content/35/1/135.abstract 
AB 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 multiple independent data resources and are advantageous to infer cells related to certain disease risks and comorbidities. Taken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease-associated molecular features.