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
m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome.
m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat Biotechnol. 2022 08; 40(8):1210-1219.
PMID: 35288668
Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.
Effective and scalable single-cell data alignment with non-linear canonical correlation analysis. Nucleic Acids Res. 2022 02 28; 50(4):e21.
PMID: 34871454
Demystifying "drop-outs" in single-cell UMI data.
Demystifying "drop-outs" in single-cell UMI data. Genome Biol. 2020 08 06; 21(1):196.
PMID: 32762710
REPIC: a database for exploring the N6-methyladenosine methylome.
REPIC: a database for exploring the N6-methyladenosine methylome. Genome Biol. 2020 04 28; 21(1):100.
PMID: 32345346
RADAR: differential analysis of MeRIP-seq data with a random effect model.
RADAR: differential analysis of MeRIP-seq data with a random effect model. Genome Biol. 2019 12 23; 20(1):294.
PMID: 31870409
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies.
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.
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
Alfred P. Sloan Research fellowship in Computational and Molecular Evolutionary Biology
the University of Chicago
2019
Junior Faculty Development Award
University of North Carolina - Chapel Hill
2015
Student Marshal
Yale Graduate School of Arts and Sciences
2014