Our Faculty

Mengjie Chen, PhD

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.

Yale University
New Haven, CT, USA
PhD - Computational Biology
2014

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies.
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

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.
Chen M, Zhou X. Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes. Sci Rep. 2017 10 19; 7(1):13587.
PMID: 29051597

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations.
Chang J, Tan W, Ling Z, Xi R, Shao M, Chen M, Luo Y, Zhao Y, Liu Y, Huang X, Xia Y, Hu J, Parker JS, Marron D, Cui Q, Peng L, Chu J, Li H, Du Z, Han Y, Tan W, Liu Z, Zhan Q, Li Y, Mao W, Wu C, Lin D. Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations. Nat Commun. 2017 05 26; 8:15290.
PMID: 28548104

SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling.
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

Comprehensive analysis of The Cancer Genome Atlas reveals a unique gene and non-coding RNA signature of fibrolamellar carcinoma.
Dinh TA, Vitucci EC, Wauthier E, Graham RP, Pitman WA, Oikawa T, Chen M, Silva GO, Greene KG, Torbenson MS, Reid LM, Sethupathy P. Comprehensive analysis of The Cancer Genome Atlas reveals a unique gene and non-coding RNA signature of fibrolamellar carcinoma. Sci Rep. 2017 03 17; 7:44653.
PMID: 28304380

Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification.
Allott EH, Geradts J, Sun X, Cohen SM, Zirpoli GR, Khoury T, Bshara W, Chen M, Sherman ME, Palmer JR, Ambrosone CB, Olshan AF, Troester MA. Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification. Breast Cancer Res. 2016 06 28; 18(1):68.
PMID: 27349894

Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.
Chen M, Ren Z, Zhao H, Zhou H. Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. J Am Stat Assoc. 2016 Mar; 111(513):394-406.
PMID: 27499564

CHANGE POINT ANALYSIS OF HISTONE MODIFICATIONS REVEALS EPIGENETIC BLOCKS LINKING TO PHYSICAL DOMAINS.
Chen M, Lin H, Zhao H. CHANGE POINT ANALYSIS OF HISTONE MODIFICATIONS REVEALS EPIGENETIC BLOCKS LINKING TO PHYSICAL DOMAINS. Ann Appl Stat. 2016 Mar; 10(1):506-526.
PMID: 27231496

Global copy number profiling of cancer genomes.
Wang X, Chen M, Yu X, Pornputtapong N, Chen H, Zhang NR, Powers RS, Krauthammer M. Global copy number profiling of cancer genomes. Bioinformatics. 2016 03 15; 32(6):926-8.
PMID: 26576652

A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.
Xu Z, Zhang G, Jin F, Chen M, Furey TS, Sullivan PF, Qin Z, Hu M, Li Y. A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data. Bioinformatics. 2016 03 01; 32(5):650-6.
PMID: 26543175

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Junior Faculty Development Award
University of North Carolina - Chapel Hill
2015

Student Marshal
Yale Graduate School of Arts and Sciences
2014

China Scholarship Council-Yale World Scholarship
Yale University
2009 - 2012

National Scholarship
HUST, China
2007 - 2008