Health AI Central
Peer-Reviewed Publication
Brief Bioinform2025;26(3)May 1, 2025Journal Article

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

Kang-Lin Hsieh1, Kai Zhang2, Yan Chu3,4,5, Lishan Yu2, Xiaoyang Li6,7, Nuo Hu6,8, Isha Kawosa6,9, Patrick G Pilié1, Pratip K Bhattacharya10, Degui Zhi4, Xiaoqian Jiang2, Zhongming Zhao6,11, Yulin Dai6
1Division of Cancer Medicine, Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, United States.
2Center 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, United States.
3Division of Radiation Oncology, Department of Radiation Physics, UT MD Anderson Cancer Center, 1400 Pressler St, Houston, Houston, TX 77030, United States.
4Center 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, United States.
5Center for Design and Analysis, Amgen, One Amgen Center Drive, Thousand Oaks, CA 91320, United States.
6Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, United States.
7Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, United States.
8Department of Statistics, Rice University, 6100 Main St MS 364, Houston, TX 77005, United States.
9Department of Computer Science and Engineering, Texas A&M University, 435 Nagle St, College Station, TX 77843, United States.
10Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, United States.
11Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Av, Nashville, TN 37203, United States.
PubMed PMC DOI PDF

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) framewor…

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