Profile:

I conduct research in the field of computational systems biology, with focus on identification ofcollaborative patterns between genes, cells, andtissue substructure using single-cell and spatial omics data. As an independent first author, my previous studies were published in high-impact journals includingNature Methods(2023, IF=48, CytoCommunity algorithm),Science Advances(2021, IF=14, CytoTalk algorithm), andNature Communications(2019, IF=17, OptiCon algorithm). Among them,

(1) The CytoCommunity algorithm is the first computational tool that can perform both unsupervised and supervised analysis of single-cell spatial omics data. It enables the discovery of condition-specific tissue cellular neighborhoods and their communication patterns across variable spatial scales.

(2) The CytoTalk algorithm has been rated as one of the representative methods for cell-cell communication analysis and has been selected as an expert-recommended method for single-cell data analysis in the top review journalNature Review Genetics.

(3) The OptiCon algorithm enablesde novoidentification of combination therapy targets and has been successfully applied in a pre-clinical study at the Children's Hospital in Philadelphia.

Research Interests:

(1) Cell-Cell Communication Modeling

Based on single-cell multi-omics data, we use graph mining and machine learningtechniquesto define and identify cell-cell communication patterns, such as construction of cell-cell communication signaling networks.

(2) Tissue Architecture Discovery

Different cells communicate and collaborate within a tissue based on some "unknown rules" to form multiple organized tissue cellular neighborhoods (TCNs), thereby implementing the function of the entire tissue or organ. Therefore, accurate identification of TCNs is of great importance for a deeper understanding of life systems. We use cutting-edge artificial intelligence technologies such as deep learning to digitally decode the spatial composition rules of tissues based on spatial multi-omics data.

(3) Quantitative Analysis of Tumor Immunity

Using computational tools of cell-cell communication modeling and tissue architecture analysis to quantify the immune system of tumor patients and identify biomarkers for tumor diagnosis and prognosis, as well as potential targets for combination immunotherapy.

Selected Publications:

• [1]Yuxuan Hu, Jiazhen Rong, Yafei Xu, Runzhi Xie, Jacqueline Peng, Lin Gao, Kai Tan. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.Nature Methods, 2023, Accepted.

• [2]Yuxuan Hu, Tao Peng, Lin Gao, Kai Tan. CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data.Science Advances, 2021, 7: eabf1356.[Full Text]

• [3]Yuxuan Hu, Chia-Hui Chen, Yang-Yang Ding, Xiao Wen, Bingbo Wang, Lin Gao, Kai Tan. Optimal control nodes in disease-perturbed networks as targets for combination therapy.Nature Communications, 2019, 10: 2180.[Full Text]

• [4]高琳,胡宇轩,叶育森, 张世雄.单细胞数据驱动的关键问题与挑战,中国计算机学会通讯, 2022, 18(4): 28-35.[Full Text]

• [5] Ran Duan, Lin Gao, Yong Gao,Yuxuan Hu, Han Xu, Mingfeng Huang, Kuo Song, Hongda Wang, Yongqiang Dong, Chaoqun Jiang, Chenxing Zhang, Songwei Jia. Evaluation and comparison of multi-omics data integration methods for cancer subtyping.PLoS Computational Biology, 2021, 17: e1009224.[Full Text]

• [6] Yang-Yang Ding, Hannah Kim, Kellyn Madden, Joseph P Loftus, Gregory M Chen, David Hottman Allen, Ruitao Zhang, Jason Xu, Chia-Hui Chen,Yuxuan Hu, Sarah K Tasian, Kai Tan. Network analysis reveals synergistic genetic dependencies for rational combination therapy in Philadelphia chromosome–like acute lymphoblastic leukemia.Clinical Cancer Research, 2021, 27(18): 5109-5122.[Full Text]

• A full list of publications:https://scholar.google.com/citations?user=SWlJrycAAAAJ&hl=zh-CN

Algorithm implementation and softwares are available at the GitHub page: https://github.com/huBioinfo