{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:45:23Z","timestamp":1775627123458,"version":"3.50.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Translational Cancer Research Training Program (BIG-TCR) funded by the Cancer Prevention & Research Institute of Texas","award":["CPRIT RP210045"],"award-info":[{"award-number":["CPRIT RP210045"]}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["CPRIT RP240610"],"award-info":[{"award-number":["CPRIT RP240610"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute\u2019s","doi-asserted-by":"crossref","award":["NCT04947254, NCT03748641"],"award-info":[{"award-number":["NCT04947254, NCT03748641"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Mulva Family Fund","award":["CFS: 600655\u201380\u2013120663-19"],"award-info":[{"award-number":["CFS: 600655\u201380\u2013120663-19"]}]},{"name":"Z.Z","award":["U01AG079847, R03AG077191, R01LM012806, R01DE030122, and R01DE029818"],"award-info":[{"award-number":["U01AG079847, R03AG077191, R01LM012806, R01DE030122, and R01DE029818"]}]},{"name":"National Institutes of Health grants awarded to Y.D., L.Y., and Z.Z","award":["R21AG087299"],"award-info":[{"award-number":["R21AG087299"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2013protein 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaf296","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T07:48:40Z","timestamp":1749196120000},"source":"Crossref","is-referenced-by-count":1,"title":["iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics"],"prefix":"10.1093","volume":"26","author":[{"given":"Kang-Lin","family":"Hsieh","sequence":"first","affiliation":[{"name":"Division of Cancer Medicine, Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center , 1515 Holcombe Blvd, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Chu","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Department of Radiation Physics, UT MD Anderson Cancer Center , 1400 Pressler St, Houston, Houston, TX 77030 ,","place":["United States"]},{"name":"Center for Artificial Intelligence and Genome Informatics, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, Houston, TX 77030 ,","place":["United States"]},{"name":"Center for Design and Analysis, Amgen , One Amgen Center Drive, Thousand Oaks, CA 91320 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lishan","family":"Yu","sequence":"additional","affiliation":[{"name":"Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 ,","place":["United States"]},{"name":"Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston , 1200 Pressler St, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuo","family":"Hu","sequence":"additional","affiliation":[{"name":"Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 ,","place":["United States"]},{"name":"Department of Statistics, Rice University , 6100 Main St MS 364, Houston, TX 77005 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isha","family":"Kawosa","sequence":"additional","affiliation":[{"name":"Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 ,","place":["United States"]},{"name":"Department of Computer Science and Engineering, Texas A&M University , 435 Nagle St, College Station, TX 77843 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick G","family":"Pili\u00e9","sequence":"additional","affiliation":[{"name":"Division of Cancer Medicine, Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center , 1515 Holcombe Blvd, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratip K","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center , 1515 Holcombe Blvd, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Degui","family":"Zhi","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence and Genome Informatics, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[{"name":"Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , 7000 Fannin St, Houston, TX 77030 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