Articles

  • Feb 16, 2025 | nature.com | Jingyang Qian |Xin Shao |Yin Fang |Wenbo Guo |Chengyu Li |Xiaohui Fan | +1 more

    Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data. Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, the authors develop scNiche, a computational framework to identify and characterise cell niches from spatial omics data at single-cell resolution.

  • Mar 31, 2024 | mdpi.com | Jingjing Gao |Wenbo Guo |Qingwei Liu |Meige Liu

    All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.

  • Sep 8, 2023 | about.honywen.com | Wenbo Guo

    An Empirical Study of Malicious Code In PyPI EcosystemWenbo Guo, Zhengzi Xu, Chengwei Liu, Cheng Huang, Yong Fang, Yang LiuJuly, 2023AbstractWe conducted an empirical study to understand the characteristics and current state of the malicious code lifecycle in the PyPI ecosystem. We first built an automated data collection framework and collated a multi-source malicious code dataset containing 4,669 malicious package files.

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