Faculty, Staff and Student Publications
Language
English
Publication Date
5-1-2025
DOI
10.1093/bib/bbaf296
PMID
40551620
PMCID
PMC12206099
PubMedCentral® Posted Date
6-24-2025
PubMedCentral® Full Text Version
Post-print
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 and traditional bioinformatics methods in functional enrichment tasks. By integrating the latent layer with a graph neural network framework, iGTP could effectively infer cellular responses to perturbations. Lastly, we applied iGTP TP embeddings with a latent diffusion model to accurately generate cell embeddings for specific cell types and states. We anticipate that iGTP will offer insights at both PPI and TP levels and holds promise for predicting responses to novel perturbations.
Keywords
Single-Cell Analysis, Transcriptome, Computational Biology, Humans, Gene Expression Profiling, Deep Learning, Protein Interaction Maps, Neural Networks, Computer
Published Open-Access
yes
Recommended Citation
Hsieh, Kang-Lin; Zhang, Kai; Chu, Yan; et al., "iGTP: Learning Interpretable Cellular Embedding for Inferring Biological Mechanisms Underlying Single-Cell Transcriptomics" (2025). Faculty, Staff and Student Publications. 660.
https://digitalcommons.library.tmc.edu/uthshis_docs/660