Author

Yongjiang Luo

Southwest University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Organism:mouse· 2 Assay:spatial transcriptomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Yi WangiD· 3 shared Fang LuiD· 2 shared Jiahe Sun· 2 shared Tingting XiaiD· 2 shared Zijie JiangiD· 2 shared Anqiang JiaiD· 1 shared Boli Ran· 1 shared Cheng LuoiD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBioinformatics

Yi WangORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics

Cheng Luo, Yuyan Lyu, Xinning Guo, Li Cheng, Qiantong Liang, S Q Wang, Yanzhi Wang, Shikuan Zhang, Tianyu Liu, Yongjiang Luo, Fang Lu, Boli Ran, Yaou Zhang, Xiao Liu, Gangjian Qin, , Qing Lyu

This paper introduces 3D-VirtualCM, a method to reconstruct full cardiomyocyte transcriptomes from spatial transcriptomic sections, revealing spatial and transcriptional heterogeneity in infarcted mouse hearts.

bioRxiv · Sep 10, 2026 · PreprintBioinformatics

Empowering Integrative and Collaborative Exploration of Single-Cell and Spatial Multimodal Data with SGS

Tingting Xia, Jiahe Sun, Fang Lu, Yongjiang Luo, Yudi Mao, Ling Xu, Yi Wang

SGS provides a collaborative browser for visualizing single-cell and spatial multimodal data with advanced comparative visualization tools.

bioRxiv · Jul 19, 2024 · PreprintBioinformatics

AutoHiC: a deep-learning method for automatic and accurate chromosome-level genome assembly

Zijie Jiang, Zhixiang Peng, Yongjiang Luo, Lingzi Bie, Yi Wang

AutoHiC improves chromosome-level genome assembly accuracy using deep learning to detect and correct errors in Hi-C data.

bioRxiv · Aug 27, 2023 · PreprintBioinformatics

Molecular atlas reveals the tri-sectional spinning mechanism of spider dragline silk

Wenbo Hu, Anqiang Jia, Sanyuan Ma, Guoqing Zhang, Zhaoyuan Wei, Fang Lü, Yongjiang Luo, Zhi‐Sheng Zhang, Jiahe Sun, Tianfang Yang, Tingting Xia, Qinhui Li, Ting Yao, Jiangyu Zheng, Zijie Jiang, , Yi Wang

The paper creates a molecular atlas of spider dragline silk production in Trichonephila clavata, revealing a tri-sectional biosynthesis mechanism and cell-type organization in the major ampullate gland.

Nature Communications · Feb 15, 2023Peer-reviewedGenomics