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Mengjie Chen

TitleAssociate Professor
InstitutionUniversity of Chicago
DepartmentMedicine-Genetic Medicine
AddressChicago IL 60637
Email
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    Collapse Overview 
    Collapse overview
    My primary research is driven by the need for powerful statistical methods to address the challenges those technologies have posed for data analysis and interpretation, particularly for data emerging from biological and biomedical studies, such as epigenetic and cancer genomics related research. I have developed novel methodologies for a variety of problems, including change point detection methods for identifying somatic copy number aberration, nonparametric Bayesian methods to integrate the heterogeneity in somatic mutations into gene expression analysis, Gaussian graphical models for eQTL analysis and methods for the analysis of single cell sequencing data. My ultimate goal is to develop methods that can integrate genomic features into the prediction of clinical outcomes, which will potentially shed new lights on personalized disease diagnosis and prognosis.

    Collapse Biography 
    Collapse education and training
    Yale University, New Haven, CT, USAPhD05/2014Computational Biology
    Collapse awards and honors
    2014Student Marshal, Yale Graduate School of Arts and Sciences
    2015 Junior Faculty Development Award, University of North Carolina - Chapel Hill
    2019Alfred P. Sloan Research fellowship in Computational and Molecular Evolutionary Biology , the University of Chicago

    Collapse Research 
    Collapse research activities and funding
    R01HG012927     (CHEN, MENGJIE)May 15, 2023 - Feb 28, 2027
    NIH
    Develop new bioinformatics infrastructures and computational tools for epitranscriptomics data
    Role: Principal Investigator
    R01HG011883     (CHEN, MENGJIE ;ZHOU, XIANG)Sep 16, 2021 - Jun 30, 2025
    NIH
    Developing new computational tools for spatial transcriptomics data
    Role: Principal Investigator
    NSF 2016307     Sep 1, 2020 - Aug 31, 2023
    NSF
    Statistical Methods for Intra-tumor Heterogeneity Studies Using Sequencing Data
    Role: Principal Investigator
         (Chan Zuckerberg Initiative Human Seed Network Grant)Sep 1, 2019 - Aug 31, 2023
    CZI
    A Female Reproductive Cell Atlas
    Role: co-PI
    1R01GM126553-01     (Mengjie Chen)Aug 1, 2017 - May 31, 2022
    NIH/NIGMS
    COLLABORATIVE RESEARCH: ADVANCED STATISTICAL METHODS FOR SINGLE CELL RNA SEQUENCING STUDIES
    Role Description: Single cell RNA sequencing has emerged as a powerful tool in genomics. The technology is essential for understanding the heterogeneity of tissue compositions and the genetic architecture of complex traits and diseases. This project aims to develop new methods to address statistical and computational challenges in scRNAseq data analysis and facilitate the usage of scRNAseq technology.
    Role: Principal Investigator
    R01GM126553     (CHEN, MENGJIE)Aug 1, 2017 - May 31, 2022
    NIH
    Collaborative Research: Advanced statistical methods for single cell RNA sequencing studies
    Role: Principal Investigator

    Collapse Bibliographic 
    Collapse selected publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
    Newest   |   Oldest   |   Most Cited   |   Most Discussed   |   Timeline   |   Field Summary   |   Plain Text
    PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
    1. Hu L, Liu S, Peng Y, Ge R, Su R, Senevirathne C, Harada BT, Dai Q, Wei J, Zhang L, Hao Z, Luo L, Wang H, Wang Y, Luo M, Chen M, Chen J, He C. m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat Biotechnol. 2022 08; 40(8):1210-1219. PMID: 35288668; PMCID: PMC9378555.
      Citations: 75     Fields:    Translation:HumansAnimalsCells
    2. Hu J, Chen M, Zhou X. Effective and scalable single-cell data alignment with non-linear canonical correlation analysis. Nucleic Acids Res. 2022 02 28; 50(4):e21. PMID: 34871454; PMCID: PMC8887421.
      Citations: 7     Fields:    
    3. Kim TH, Zhou X, Chen M. Demystifying "drop-outs" in single-cell UMI data. Genome Biol. 2020 08 06; 21(1):196. PMID: 32762710; PMCID: PMC7412673.
      Citations: 43     Fields:    
    4. Liu S, Zhu A, He C, Chen M. REPIC: a database for exploring the N6-methyladenosine methylome. Genome Biol. 2020 04 28; 21(1):100. PMID: 32345346; PMCID: PMC7187508.
      Citations: 52     Fields:    Translation:HumansAnimalsCells
    5. Zhang Z, Zhan Q, Eckert M, Zhu A, Chryplewicz A, De Jesus DF, Ren D, Kulkarni RN, Lengyel E, He C, Chen M. RADAR: differential analysis of MeRIP-seq data with a random effect model. Genome Biol. 2019 12 23; 20(1):294. PMID: 31870409; PMCID: PMC6927177.
      Citations: 33     Fields:    Translation:HumansAnimalsCells
    6. Chen M, Zhou X. VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biol. 2018 11 12; 19(1):196. PMID: 30419955; PMCID: PMC6233584.
      Citations: 45     Fields:    
    7. Silva GO, Siegel MB, Mose LE, Parker JS, Sun W, Perou CM, Chen M. SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol. 2017 04 08; 18(1):66. PMID: 28390427; PMCID: PMC5385048.
      Citations: 18     Fields:    Translation:Humans
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