IGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

ABSTRACT: Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states. We demonstrated the performance of iGTP in a diverse biological context using gene ontology, canonical pathway, and different PPI curation. iGTP not only elucidated the ground truth of cellular responses but also surpassed other deep learning models an

SUBMITTER: Hsieh KL 

PROVIDER: S-EPMC12206099 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature