Author

Hung‐Ching Chang

University of PittsburghORCID 0000-0002-4847-2547Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:imaging· 1 Organism:mouse· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

George C. TsengiD· 3 shared Jia-Jun LiuiD· 2 shared Silvia LiuiD· 2 shared Zachary FreybergiD· 2 shared Aidan CarneyiD· 1 shared Antonia M GilesiD· 1 shared Black McCourt· 1 shared Caroline WardiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersBioinformatics

George C. TsengORCID

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3 papers

Recent·Most cited

AI-guided analysis of human pancreatic islet sociology reveals distinct cell compositional changes in type 1 diabetes

Caroline Ward, Tabitha Banks‐Tibbs, Henry Thorpe, Antonia M Giles, Samuel J. Mabry, Jia-Jun Liu, Hung‐Ching Chang, Jinting Yang, Aidan Carney, Halla M Shaikh, Lauren M. Woolley, Paul Joseph, Jenesis Gayden-Kozel, Emily M. Rocha, Guy A. Rutter, , Zachary Freyberg

The paper uses AI to analyze pancreatic islet composition in type 1 diabetes, finding age, sex, and region-dependent changes and beta-cell loss with reciprocal cell expansion.

bioRxiv · Jun 9, 2026 · PreprintPathology

Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans

Mostofa Rafid Uddin, Zhiqian Zheng, Kashish Gandhi, Hung‐Ching Chang, Jenesis Kozel, Yaswanth Gali, Silas A. Buck, Jill R. Glausier, George C. Tseng, Zachary Freyberg, Min Xu

The paper develops a computational pipeline to link nuclear morphology to mRNA expression in neurons across aging in mice and humans, revealing subtype-specific morphology differences and age-related interactions.

bioRxiv · Oct 16, 2025 · PreprintBioinformatics

Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating

Peng Liu, Yuchen Pan, Hung‐Ching Chang, Wenjia Wang, Yusi Fang, Xiangning Xue, Jian Zou, Jessica M. Toothaker, Oluwabunmi O. Olaloye, Eduardo G. Gonzalez Santiago, Black McCourt, Vanessa Mitsialis, Pietro Presicce, Suhas G. Kallapur, Scott B. Snapper, , Silvia Liu

This study evaluates manual gating, unsupervised clustering, and auto-gating methods for high-dimensional cytometry data using six datasets and provides practical guidelines for selecting appropriate methods.

Briefings in Bioinformatics · Nov 22, 2024Peer-reviewedBioinformatics