Author

Mengsha Tong

Xiamen 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· 4

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 4 Organism:human· 4 Approach:statistical model· 4 Assay:scRNA-seq· 3 Research Focus:benchmarking· 2 Research Focus:clustering· 2 Research Focus:database / resource· 2 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Jialiang HuangiD· 3 shared Zheyang ZhangiD· 3 shared Chenyu Liang· 2 shared Jingyi Tian· 2 shared Rongshan YuiD· 2 shared Shijie LuoiD· 2 shared Yuxiang LiniD· 2 shared Danni Hong· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Jialiang HuangORCID

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4 papers

Recent·Most cited

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer

Mengsha Tong, Yuxiang Lin, Wenxian Yang, Jinsheng Song, Zheyang Zhang, Jiajing Xie, Jingyi Tian, Shijie Luo, Chenyu Liang, Jialiang Huang, Rongshan Yu

The paper identifies prognostic-associated subpopulations and develops recurrence risk signatures from scRNA-seq data of colorectal cancer using a rank-based framework.

Briefings in Bioinformatics · Mar 22, 2023Peer-reviewedBioinformatics

Integrated analysis of single-cell transcriptome of liver cancer and cirrhosis reveals cell lineage similarity and prognostic-associated subpopulations

Mengsha Tong, Shijie Luo, Lin Gu, Zheyang Zhang, Chenyu Liang, Jingyi Tian, Huaqiang Huang, Yuxiang Lin, Jialiang Huang

The paper integrates single-cell transcriptome data from liver samples to identify cell lineage similarities and prognostic subpopulations between cirrhosis and liver cancer.

bioRxiv · Nov 3, 2022 · PreprintBioinformatics

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Danni Hong, Hongli Lin, Lifang Liu, Muya Shu, Jianwu Dai, Falong Lu, Mengsha Tong, Jialiang Huang

The paper introduces eNet, a method to build enhancer networks from single-cell data, showing complex networks regulate cell identity and disease genes better than existing models.

Briefings in Bioinformatics · Oct 25, 2022Peer-reviewedBioinformatics

Application of personalized differential expression analysis in human cancer proteome

Liu Yachen, Lin Yalan, Yujuan Wu, Zheyang Zhang, Mengsha Tong, Rongshan Yu

The paper develops a tool called IDEP to perform personalized differential expression analysis on cancer proteomes, showing it can identify patient-specific deregulated proteins more effectively than group-based methods.

bioRxiv · Jul 18, 2021 · PreprintBioinformatics