Welcome to the Zhang Lab
Single-Cell Data-Driven Computational Omics and Systems Immunology (COSI)
We develop computational machine learning methods for single-cell omics to study inflammatory disease for translational medicine in the Department of Medicine Division of Rheumatology and Department of Biomedical Informatics Center for Health Artificial Intelligence, the University of Colorado Anschutz Medical Campus.
Recent lab news
📚 2025 March - Congrats Inamo, J., et al on our recent publication of Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis, JCI (Journal of Clinical Investigation), 2025. This study was featured at CU DOM News: “Leveraging Data Science for Disease Prediction in the Fight Against Rheumatoid Arthritis” 🎉
💰 2025 March - Congrats Fan to receive the Arthritis Foundation grant 🎉 - check out the featured post of the grant.
📚 2025 Feb - Congrats Reynoso, S., et al on our recent manuscript STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance at bioRxiv!
📚 2025 Jan - Congrats Young, J., et al on our recent manuscript CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics at bioRxiv!
👏🏻 2024 Dec - Congrats, Fan, on being named one of the 2024 Next Generation Leaders by the Allen Institute 🎉
Our Science
🖥 We develop novel computational machine learning methods for single-cell omics
Guo, et al, Bioinformatics Advances, 2024 [Code]
Fan, Slowikowski, Zhang, Nature EMM, 2020
Zhang, et al., BMC Bioinformatics, 2017 [Code]
⭐️ We dedicate to the field of single-cell multi-omics data modeling using computational AI methods for both primary and public datasets.
🧬 We decipher pathogenic cells and molecular mechanisms for immune-mediated diseases
Zhang*, Wei*, Slowikowski*, Fonseka*, Rao*, et al, Nature Immunology, 2019 [Code]
⭐️ We have long-term interests in defining connections between tissue pathology and blood markers to link tissue-level heterogeneity to clinical subphenotypes.
💊 We integrate cross-disease cross-tissue cross-cohort data to uncover novel phenotypes for translational medicine
Zhang*, Jonsson*, et al, Nature, 2023 [Data Browser]
Jonsson*, Zhang*, et al, Science Translational Medicine, 2022 [Code]
Zhang, et al. Genome Medicine, 2021 [Code | Single-cell reference download]
⭐️ We are interested in myeloid, T cell, and stromal cell interactions in inflammatory conditions.
