Yun (Renee) Zhang, Ph.D.

Investigator

Computational Biology Branch

NLM

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Building 38A, Room 6N605
8600 Rockville Pike
Bethesda, MD 20892

301-480-6480

yun.zhang@nih.gov

Research Topics

Cells are the fundamental unit of life. Different cells play different functional and physiological roles across tissues and organs in the human body and other multi-cellular organisms. Single cell genomics technologies are revolutionizing our understanding of the diversity of cell types and their selective genetic profiles. Traditionally, cell types are classified by their morphological and histological properties, resulted in limited resolution of cell types. Advances in single cell/nucleus RNA sequencing (sc/snRNA-seq) and spatial transcriptomics have enabled unbiased transcriptomic profiling of individual cells in tissue samples, revealing unprecedented number of distinct cell types as well as their unique 3D spatial organization.

Advancements in single cell technologies pose greater computational challenges. The high-throughput, large-scale datasets exhibit highly complex data distributions and structures, calling for new computational approaches and advanced analytical methods for data processing and statistically-rigorous analysis. Artificial intelligence and machine learning have emerged as powerful tools for single cell data analysis. However, off-the-shelf machine learning techniques do not address the unique challenges from biological data, leading to sub-optimal performance and lack of interpretability.

Dr. Zhang's lab develops computational strategies utilizing machine learning and advanced statistical methods for analyzing and integrating high-throughput, large-scale, and multi-modal single cell and spatial transcriptomics data. The Zhang lab uses explainable artificial intelligence approaches to identify data-driven biomarkers related to health and disease. Her group conducts data science focused research and closely collaborates with experimental investigators to understand and characterize the diverse cell phenotypes at single-cell resolution utilizing various single-cell genomics approaches.

Biography

Dr. Yun "Renee" Zhang is a Principal Investigator in the Division of Intramural Research (DIR) of the National Library of Medicine (NLM).

Prior to joining NLM, Dr. Zhang was an Assistant Professor at the J. Craig Venter Institute (JCVI) in La Jolla, CA. JCVI is a non-profit research institute focused on the application of genomics technologies for biological and biomedical research. At JCVI, Dr. Zhang was a recipient of multiple NIH extramural research grants, including R01 and R03.

She received her Master of Mathematics degree in Mathematics and Statistics from the University of Oxford, MS in Statistics from the University of Pennsylvania, and PhD in Statistics from the University of Rochester Medical Center.

Selected Publications

  1. Liu A, Peng B, Pankajam AV, Duong TE, Pryhuber G, Scheuermann RH, Zhang Y. Discovery of optimal cell type classification marker genes from single cell RNA sequencing data. BMC Methods. 2024;1.
  2. Tan SZK, Puig-Barbe A, Goutte-Gattat D, Eastwood C, Aevermann B, Avola A, Balhoff JP, Bayindir IU, Belfiore J, Caron AR, Fischer DS, George N, Gyori BM, Haendel MA, Hoyt CT, Kir H, Lubiana T, Matentzoglu N, Overton JA, Peng B, Peters B, Quardokus EM, Ray PL, Roncaglia P, Rivera AD, Stefancsik R, Teh WK, Toro S, Vasilevsky N, Xu C, Zhang Y, Scheuermann RH, Mungall CJ, Diehl AD, Osumi-Sutherland D. The Cell Ontology in the age of single-cell omics. Sci Data. 2026;13(1).
  3. Börner K, Blood PD, Silverstein JC, Ruffalo M, Satija R, Teichmann SA, Pryhuber GJ, Misra RS, Purkerson JM, Fan J, Hickey JW, Molla G, Xu C, Zhang Y, Weber GM, Jain Y, Qaurooni D, Kong Y, HRA Team, Bueckle A, Herr BW 2nd. Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage. Nat Methods. 2025;22(4):845-860.
  4. Zhang Y, Miller JA, Park J, Lelieveldt BP, Long B, Abdelaal T, Aevermann BD, Biancalani T, Comiter C, Dzyubachyk O, Eggermont J, Langseth CM, Petukhov V, Scalia G, Vaishnav ED, Zhao Y, Lein ES, Scheuermann RH. Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain. Sci Rep. 2023;13(1):9567.
  5. Hawrylycz M, Martone ME, Ascoli GA, Bjaalie JG, Dong HW, Ghosh SS, Gillis J, Hertzano R, Haynor DR, Hof PR, Kim Y, Lein E, Liu Y, Miller JA, Mitra PP, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray PL, Sanchez R, Regev A, Ropelewski A, Scheuermann RH, Tan SZK, Thompson CL, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey TL, Baker C, Baker KS, Baker PM, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D'Orazi F, Degatano K, Dichter B, Ding SL, Dolbeare T, Ecker JR, Fang R, Fillion-Robin JC, Fliss TP, Gee J, Gillespie T, Gouwens N, Zhang GQ, Halchenko YO, Harris NL, Herb BR, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan EA, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews KL, Mezias C, Miller MI, Mollenkopf T, Mufti S, Mungall CJ, Orvis J, Puchades MA, Qu L, Receveur JP, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven CTJ, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang YR, Zheng WJ, Zingg B. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biol. 2023;21(6):e3002133.

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This page was last updated on Friday, September 11, 2026