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Mengjie Chen

TitleAssistant 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 ORNG Applications 
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    Collapse Research 
    Collapse research activities and funding
    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

         (Chan Zuckerberg Initiative Human Seed Network Grant)Sep 1, 2019 - Aug 31, 2023
    CZI
    A Female Reproductive Cell Atlas
    Role: co-PI

    NSF 2016307     Sep 1, 2020 - Aug 31, 2023
    NSF
    Statistical Methods for Intra-tumor Heterogeneity Studies Using Sequencing Data
    Role: Principal Investigator

    R01HG011883     (CHEN, MENGJIE)Sep 16, 2021 - Jun 30, 2025
    NIH
    Developing new computational tools for spatial transcriptomics data
    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.
    List All   |   Timeline
    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.
      View in: PubMed
    2. Hu J, Chen M, Zhou X. Effective and scalable single-cell data alignment with non-linear canonical correlation analysis. Nucleic Acids Res. 2022 Feb 28; 50(4):e21. PMID: 34871454.
      View in: PubMed
    3. Kim TH, Zhou X, Chen M. Demystifying "drop-outs" in single-cell UMI data. Genome Biol. 2020 Aug 06; 21(1):196. PMID: 32762710.
      View in: PubMed
    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.
      View in: PubMed
    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.
      View in: PubMed
    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.
      View in: PubMed
    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.
      View in: PubMed
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