Personal Profile

Our team conducts research in the field of computational systems biology, with focus on “intelligent analysis of cellular interactions and digital organ construction”, to provide algorithmic tools for the development of targeted/immunotherapy (combination) treatments for cancer. Our previous studies were published in high-impact journals including Nature Methods (2024, CytoCommunity), Science Advances (2021, CytoTalk), Nature Communications (2019, OptiCon; 2026, HRCHY-CytoCommunity), and PLoS Computational Biology (2026, Driver2Comm). Among them,

(1) CytoCommunity: 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) HRCHY-CytoCommunity: a computational tool that performs hierarchical partition of tissue spatial architecture: coarse-grained tissue compartments and fine-grained cellular neighborhoods.

(3) CytoTalk: a representative method for cell-cell communication analysis and has been selected as an expert-recommended method for single-cell data analysis in the top review journal Nature Review Genetics.

(4) Driver2Comm: for the first time to introduce the concept of Intrinsic-Extrinsic (IE) pathways, linking cancer driver genes to intercellular communication events in the tumor microenvironment. It provides a new perspective for understanding tumors as a complex ecosystem and offers potential clues for "targeted-immunotherapy" combination treatments.

(5) OptiCon: de novo identification 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 learning techniques to 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.

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

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

Selected Publications:

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• [6] Runzhi Xie, Zekun Wang, Jianrui Liu, ..., Yuxuan Hu*, Lin Gao*. HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps. Nature Communications, 2026, Accepted.

• [5] Runzhi Xie, Junping Li, Yuxuan Hu*, Lin Gao*. Bridging cancer cell-intrinsic driver genes and -extrinsic cell-cell communication with Driver2Comm. PLoS Computational Biology, 2026, 22:e1013973. [Full Text]

• [4] Han Xu, Yusen Ye, Ran Duan, Yong Gao, Yuxuan Hu*, Lin Gao*. Beaconet: A reference-free method for integrating multiple batches of single-cell transcriptomic data in original molecular space. Advanced Science, 2024, 2306770. [Full Text]

• [3] Yuxuan Hu*, Jiazhen Rong, Yafei Xu, ..., Jacqueline Peng, Lin Gao, Kai Tan*. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nature Methods, 2024, 21: 267-278. (Featured Article) [Full Text]

• [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]

• [1] Yuxuan Hu, Chia-Hui Chen, Yang-Yang Ding, ..., 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]