Author

Wenxuan Deng

Yale 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· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Biqing ZhuiD· 2 shared Hongyu ZhaoiD· 2 shared Naftali KaminskiiD· 2 shared Avraham UntermaniD· 1 shared Carrie L. LucasiD· 1 shared Charles S. Dela CruziD· 1 shared Chen LiniD· 1 shared Daiwei TangiD· 1 shared

Activity by Year

2018-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Biqing ZhuORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

Wei Liu, Wenxuan Deng, Ming Chen, Zihan Dong, Biqing Zhu, Zhaolong Yu, Daiwei Tang, Maor Sauler, Chen Lin, Louise V. Wain, Michael H. Cho, Naftali Kaminski, Hongyu Zhao

This paper introduces a statistical framework, cWAS, to identify cell types with genetically regulated proportions associated with complex diseases using integrated genetic and transcriptomics data.

PLoS Genetics · Jul 31, 2023Peer-reviewedGenetic & Genomic Medicine

ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

Wenxuan Deng, Biqing Zhu, Seyoung Park, Tomokazu S. Sumida, Avraham Unterman, David A. Hafler, Charles S. Dela Cruz, Naftali Kaminski, Carrie L. Lucas, Hongyu Zhao

ProtAnno automates cell type annotation for single cell proteomics data by integrating multiple reference sources, achieving accurate and robust results on manually labeled datasets.

bioRxiv · Sep 13, 2021 · PreprintBioinformatics

GEM: A manifold learning based framework for reconstructing spatial organizations of chromosomes

Guangxiang Zhu, Wenxuan Deng, Hailin Hu, Rui Ma, Sai Zhang, Jinglin Yang, Jian Peng, Tommy Kaplan, Jianyang Zeng

GEM reconstructs 3D chromatin structures from Hi-C data using manifold learning and conformational energy, outperforming existing methods and recovering long-range interactions.

Nucleic Acids Research · Jan 25, 2018Peer-reviewedBioinformatics