Dr. Chen’s primary research is driven by the need to develop powerful statistical methods to address the complex challenges posed by emerging technologies in data analysis and interpretation, particularly in the context of biological and biomedical studies such as epigenetics and cancer genomics. Dr. Chen has developed novel methodologies for a range of analytical problems, including change point detection for identifying somatic copy number aberrations, nonparametric Bayesian methods for integrating somatic mutation heterogeneity into gene expression analysis, Gaussian graphical models for eQTL analysis, and approaches for analyzing single-cell sequencing data. The ultimate goal of Dr. Chen’s work is to create methods that integrate genomic features into the prediction of clinical outcomes, with the potential to advance personalized disease diagnosis and prognosis.
Yale University
New Haven, CT, USA
PhD - Computational Biology
2014
Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework.
Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework. STAR Protoc. 2026 Mar 20; 7(1):104306.
PMID: 41689808
Multi-region m6A epitranscriptome profiling of the human brain reveals spatial and temporal variation and enrichment of disease-associated loci.
Multi-region m6A epitranscriptome profiling of the human brain reveals spatial and temporal variation and enrichment of disease-associated loci. Nat Neurosci. 2026 Jan; 29(1):195-205.
PMID: 41366183
A novel gene expression stability metric to unveil homeostasis and regulation.
A novel gene expression stability metric to unveil homeostasis and regulation. Genome Biol. 2025 Oct 10; 26(1):351.
PMID: 41074190
scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.
scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework. Cell Genom. 2025 May 14; 5(5):100875.
PMID: 40373737
Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm.
Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm. Genome Biol. 2025 Mar 17; 26(1):58.
PMID: 40098192
Capturing cell-type-specific activities of cis-regulatory elements from peak-based single-cell ATAC-seq.
Capturing cell-type-specific activities of cis-regulatory elements from peak-based single-cell ATAC-seq. Cell Genom. 2025 Mar 12; 5(3):100806.
PMID: 40049167
A cell atlas of the human fallopian tube throughout the menstrual cycle and menopause.
A cell atlas of the human fallopian tube throughout the menstrual cycle and menopause. Nat Commun. 2025 01 03; 16(1):372.
PMID: 39753552
LABS: linear amplification-based bisulfite sequencing for ultrasensitive cancer detection from cell-free DNA.
LABS: linear amplification-based bisulfite sequencing for ultrasensitive cancer detection from cell-free DNA. Genome Biol. 2024 06 14; 25(1):157.
PMID: 38877540
A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.
A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening. Nat Methods. 2023 11; 20(11):1693-1703.
PMID: 37770710
RBFOX2 recognizes N6-methyladenosine to suppress transcription and block myeloid leukaemia differentiation.
RBFOX2 recognizes N6-methyladenosine to suppress transcription and block myeloid leukaemia differentiation. Nat Cell Biol. 2023 09; 25(9):1359-1368.
PMID: 37640841
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