Yanjie Chen

Office 601 Schermerhorn Hall

Columbia University

New York, NY 10027

I am a Research Staff Associate in the Dinh Lab at Columbia’s Irving Institute for Cancer Dynamics. My work applies statistics and machine learning to cancer genomics.

Email: yc4594@columbia.edu

Want to have a chat? See my availability and book a meeting.

Research interests

Probabilistic modeling of tumor evolution
Subclonal inference from bulk DNA sequencing
Generative models for genomic data
Scalable algorithms for large-scale multi-omic datasets

Education

B.Sc. in Mathematics with First Class Honours, University of Liverpool, 2024
M.A. in Statistics, Columbia University, 2025

I have also collaborated across several laboratories at Columbia on statistical modeling and machine learning projects.

news

Aug 21, 2026	I collaborated with Dr. Bogdan A. Czerniak’s group at MD Anderson Cancer Center and took on supporting work on mutation characterization.
Aug 05, 2026	At the invitation of Prof. Marek Kimmel, I presented DECODE results in a topic-contributed session at JSM 2026 in Boston. Talk: Reliable Inference of Tumor Expansion Rate and Clonality from DNA-Sequencing Data.
Jun 19, 2026	We posted our DECODE (Deciphering Cancer Origin from DNA Evolution) manuscript as a preprint (PubMed). DECODE infers tumor clonality and ongoing expansion mode from bulk DNA-seq data.
Jan 15, 2026	I started as a Research Staff Associate at the Irving Institute for Cancer Dynamics, now working full-time on bulk DNA-seq clustering with Dr. Khanh N. Dinh.
Aug 26, 2025	I joined Dr. Li-Xuan Qin’s lab at Memorial Sloan Kettering Cancer Center to improve data-augmentation methods for miRNA- and RNA-seq, and to follow up with downstream analyses.
Jan 02, 2025	I joined the Irving Institute for Cancer Dynamics as a part-time Research Intern, advised by Dr. Khanh N. Dinh, to develop clustering methods for bulk DNA sequencing data.

selected publications

bioRxiv
Accurate detection of tumor clonality and ongoing expansion mode from genomic data

Yanjie Chen, Roman Jaksik, Peter Terranova, and 6 more authors

bioRxiv, Jun 2026

Abs DOI Bib HTML

Recent evidence shows that despite considerable effort, currently available algorithms for estimating intra-tumor heterogeneity (ITH) remain limited. We developed DECODE (Deciphering Cancer Origin from DNA Evolution), a novel mutation clustering method that incorporates the impact of sample-specific sequencing coverage and mutation calling biases. On synthetic data, DECODE outperformed existing methods across multiple clonality metrics and accurately detected and characterized the neutral tail in the site frequency spectrum (SFS), which encodes the tumor’s ongoing expansion mode. In acute myeloid leukemia, accounting for the neutral tail enabled DECODE to yield more parsimonious clonal decompositions that align more closely with known subclonal dynamics that drive relapse. Applied to data from The Cancer Genome Atlas, DECODE not only detected a neutral SFS tail in most samples across tumor types but also uncovered a clinically meaningful link between ITH and survival in low-grade glioma. By jointly inferring clonality and expansion mode, DECODE provides two complementary and prognostically relevant readouts of tumor evolution from single tumor genomic samples.
@article{chen2026accurate, title = {Accurate detection of tumor clonality and ongoing expansion mode from genomic data}, author = {Chen, Yanjie and Jaksik, Roman and Terranova, Peter and El Baghdadi, Sara and Koval, Andrew and Kurpas, Monika K. and Tavar{\'e}, Simon and Kimmel, Marek and Dinh, Khanh N.}, journal = {bioRxiv}, year = {2026}, month = jun, publisher = {Cold Spring Harbor Laboratory}, doi = {10.64898/2026.06.15.732415}, url = {https://pubmed.ncbi.nlm.nih.gov/42367953/}, }