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
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
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
Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination.
Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination. Nat Biotechnol. 2023 07; 41(7):993-1003.
PMID: 36593412
Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome.
Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat Biotechnol. 2023 Jan; 41(1):150.
PMID: 36450851
A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing.
A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing. Cell Rep. 2022 12 20; 41(12):111838.
PMID: 36543131
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
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