Ye Zheng, Ph.D.

NIH K99 Fellow and Postdoctoral Research Fellow

Fred Hutchinson Cancer Center

Biography

I am an NIH/NHGRI K99/R00 fellow and expect to join the Bioinformatics and Computational Biology Department at the University of Texas MD Anderson Cancer Center as a Tenure Track Assistant Professor in 2024. During my postdoctoral training at the Fred Hutchinson Cancer Center in Seattle, I am fortunate to be mentored by Dr. Steven Henikoff and Dr. Raphael Gottardo in terms of statistical modeling and computational analysis of single-cell transcriptomics, proteomics and epigenomics as well as training in wet lab experiment. Additionally, through training and close collaborating with Dr. Cameron Turtle and Dr. Evan Newell, I am also studying the CAR-T cell therapy clinical outcome association with genomic markers from the single-cell multi-omics perspective. Before joining Fred Hutchinson Cancer Center, I received the Ph.D. in Statistics from the University of Wisconsin-Madison in 2019 under the supervision of Dr. Sündüz Keleş and we studied the statistical modelings of three-dimensional chromatin structure for promoter-enhancer inference.

I am inherently drawn to problems at the interface of statistical, biological and biomedical sciences. My current research focuses on:

Statistical modeling and computational analysis of immunological and immunotherapeutic Studies using multi-omics bulk and single-cell genomics data, such as data generated from the CITE-seq, scCUT&Tag, scRNA-seq, flow cytometry, CUT&Tag, CUTAC and RNA-seq technologies. I have a particular interest in analyzing FFPE data.
Investigating the three-dimensional chromatin organization and the long-range gene regulation through multimodality integrative model and accompanying software, using data such as scHi-C, scRNA-seq, Paired-Tag, scCUT&Tag-pro.

My career goal is to solve biological and clinically important, and methodologically challenging problems by innovating cutting-edge statistical models. I have built a repertoire of collaborative research experience with statisticians, computational biologists, molecular biologists, and immunologists worldwide, working with established principal investigators who leverage the interplay of statistics, computation, and molecular genomics. I am open to discussion and collaboration and look forward to being inspired and motivated by the novel and intriguing problems in other disciplines.

Interests

Statistical Genomics
Computational Biology
Bioinformatics
Immunology and Immunotherapy

Education

Ph.D. in Statistics - Minor in Quantitative Biology, 2019

University of Wisconsin - Madison
B.E. in Statistics, 2014

Renmin University of China

Professional Experience

Postdoctoral Research Fellow

Fred Hutchinson Cancer Center

Nov 2019 – Present Seattle, WA, USA

Single-cell Transcriptomics, Epigenomics and Proteomics:

Developed statistical models and computational tools for integrative analysis of single-cell 3D genomics, transcriptomics and epigenomics for cis-regulatory mechanism discovery.
Constructed data processing and analysis interactive tutorial website for bulk-cell CUT&Tag data.
Developed normalization method for CITE-seq data to normalize the protein marker expression given the isotype controls and cluster cells to facilitate comparison across samples.

CAR-T Cell Immunotherapy:

Integrative analysis to understand genomic responses to chimeric antigen receptor T (CAR-T) cells therapy using bulk cell epigenomics data (RNA-seq and CUT&RUN) and single-cell technologies (CITE-seq).
Construct model to detect the single-cell transcriptomics and surface proteomics features that lead to distinct clinical phenotypes.

Research Assistant

Department of Statistics, Department of Biostatistics & Medical Informatics, University of Wisconsin - Madison

Sep 2014 – Oct 2019 Madison, WI, USA

Dissertation Research:

Developed biologically motivated hierarchical generative model to investigate 3D chromatin architectures using Hi-C data and investigated the genomic features involving repetitive regions of the genomes.
Developed a computational tool for fast simulation of 3D proximity ligation sequencing data.
Constructed hierarchical testing to detect differential 3D genome interactions with precise False Discovery Rate control.
Investigated protein-DNA interactions residing in repetitive regions and integrated multi-mapping reads into Encyclopedia of DNA Elements (ENCODE) ChIP-seq data processing pipeline.

Collaborative Work with the Bresnick Lab:

Leveraged multi-omics analysis, particularly using ATAC-seq and RNA-seq data, to reveals GATA/Heme regulation mechanism in controlling hemoglobin synthesis and erythrocyte development.
Investigated the impact of single nucleotide mutation in the Ets motif of GATA2 enhancer on its function to control hematopoiesis through a comprehensive transcriptomic differential analysis.

Data Scientist Intern

Business Analysis, IBM

Feb 2014 – Jun 2014 Beijing, China

Projects:

Developed dynamic text mining model using IBM communication database to infer topic networks.
Constructed a modified Latent Dirichlet Allocation model to optimize the CPU usage of IBM servers.

Project Assistant

School of Information, Renmin University of China

Feb 2012 – Jan 2014 Beijing, China

Developed multi-objectives operations research model to improve proposals grouping accuracy and efficiency utilizing Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for optimization.

Research Assistant

Department of Biology, Mathematics and Statistics, University of Ottawa

Jun 2013 – Sep 2013 Ottawa, Canada

Investigated Approximate Bayesian Computation (ABC), ABC–Markov Chain Monte Carlo and ABC– Sequential Monte Carlo samplers in estimating the transmission networks of viruses in human populations.

Exchange Study

Department of Statistics and Actuarial Science, The University of Hong Kong

Sep 2012 – Jan 2013 Hong Kong, China

Selected Publications

Full publication list.

+: Co-first author; ++: Co-corresponding author

Fiorenza S+, Zheng Y+, Sarthy J, Sheih AS, Wu Q, Hirayama AV, Kimble EL, Kirchmeier D, Gottardo R, Henikoff S, Turtle CJ (2022). High-throughput, high sensitivity mapping of human T cell and CAR-T cell epigenomic landscapes underscores the role of H3K27me3 in subset differences and therapeutic outcomes. Manuscript in preparation.

Code

Zheng Y, Seong-Hwan J, Conn D, Kim J, Tian Y, Florian M, Gottardo R (2022). Robust Normalization and Integration of Single-cell Protein Expression across CITE-seq Datasets. bioRxiv.

PDF Code DOI

Shen S, Zheng Y++, Keleş S++ (2022). scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data. Bioinformatics.

PDF Code DOI

Zheng Y+, Shen S+, Keleş S (2022). Normalization and De-noising of Single-cell Hi-C Data with BandNorm and scVI-3D. Accepted by Genome Biology.

PDF Code DOI

Hirayama AV, Zheng Y, Dowling MR, Sheih A, Phi TD, Kirchmeier DR, Chucka AW, Gauthier J, Maloney DG, Gottardo R, Turtle CJ (2021). Long-Term Follow-up and Single-Cell Multiomics Characteristics of Infusion Products in Patients with Chronic Lymphocytic Leukemia Treated with CD19 CAR-T Cells. Blood.

PDF DOI

Zheng Y++, Zhou P, Keleş S++ (2021). FreeHi-C Spike-in Simulations for Benchmarking Differential Chromatin Interaction Detection. Methods.

PDF Code DOI

Liao R+, Zheng Y+, Liu X, Zhang Y, Seim G, Tanimura N, Wilson G, Hematti P, Coon J, Fan J, Xu J, Keleş S++, Bresnick E++ (2020). Discovering How Heme Controls Genome Function Through Heme-omics. Cell Reports. Cell Reports.

PDF DOI

Zheng Y, Keleş S (2019). FreeHi-C simulates high-fidelity Hi-C data for benchmarking and data augmentation. Nature Methods.

PDF Code DOI

Zheng Y, Ay F, Keleş S (2019). Generative Modeling of Multi-mapping Reads with mHi-C Advances Analysis of High Throughput Genome-wide Conformation Capture Studies. eLife.

PDF Cite Code Source Document DOI

Research Projects

Jul 31, 2022

3D Chromatin Organization

Study the three-dimensional chromatin interactions and long-range gene regulation

Jul 25, 2022

CAR-T cell therapy genomic profiling and clinical association study.

CAR-T cell therapy cell atlas construction and clinical associated genomic markers detection.

Jul 22, 2022

Single-cell Multi-omics Integration and Modeling

Statistical modeling and computational analysis of single-cell multi-omics data.

Oct 26, 2019

Repetitive Regions of Genomes

Studies of repetitive regions of genomes from 2D and 3D perspectives

Software

ADTnorm: R package for normalization and integration tools for CITE-seq cell surface measurement.
scGAD: R package for extracting the three-dimensional chromatin interaction at the unit of genes and facilitate the integration of single-cell 3D genomcis with other single-cell modalities.
scVI-3D: Normalization and de-noising of single-cell Hi-C data using deep generative modeling using python pipline.
BandNorm: R package for fast band normalization for sing-cell Hi-C data. (Co-developer)
FreeHiC Spike-In: FreeHi-C python pipeline with a user/data-driven spike-in module to allow a comprehensive comparison of differential chromatin interaction detection methods where the ground truth differential chromatin interactions are known.
FreeHiC: Python pipeline using FRagment Interactions Empirical Estimation method for fast simulation of Hi-C and other 3D proximity ligation sequencing data. Major computing parts are accelerated by C.
mHiC: Python pipeline of multi-mapping strategy for Hi-C data by probabilistically assigning reads originatedfrom repetitive regions. Major computing parts are accelerated by C.
permseq: R package for mapping protein-DNA interactions in highly repetitive regions of the genomes with prior-enhanced read mapping.
permseqExample: R package for the permseq package illustration and demo runs. Smaller raw data and demo R scripts are provided for quick runs in order to get to know permseq package.

Computing Skills

R

Daily

shell

Daily

Python

Daily

C/Cython

Accelerting Pipielines

git

Daily

Grid/Distributed computing systems

Daily

Teaching and Mentoring

Teaching

STAT 877 - Statistical Methods for Molecular Biology (Fall 2020 Guest Lecturer):

Gave lecture to statistics and biostatistics graduate students about 3D Genomics and Long-range Gene Regulations.
STAT 998 - Statistical Consulting (Fall 2019 Guest Lecturer):

Lead lectures to discuss real-world consulting problem with statistics graduate students utilizing the traditional and modern statistical tools.
STAT 877 - Statistical Methods for Molecular Biology (Spring 2019 Guest Lecturer):

Gave lecture to statistics and biostatistics graduate students about 3D Genomics and Long-range Gene Regulations.
2017-2018 Single-cell Technologies Journal Club (Organizer and Instructor):

Gave lectures about single-cell related research topics, such as scRNA-seq, scATAC-seq and scHi-C, to graduate students and post-docs from statistics background, and led paper review discussions.
2017-2018 Three-dimensional Chromatin Interactions Journal Club (Organizer and Instructor):

Gave lectures about 3D chromatin architecture related research topics to graduate students and post-docs from statistics background, and led paper review discussions.
STAT301 - Introduction to Statistical Methods (Fall 2014 Guest Lecturer for Discussion Sections):

Led undergraduate students discussions for solving hypothesis testing and statistical estimation problems.

Mentoring

Feb. 2022 to Present, Long Nguyen, Bioinformatics Analyst I at Fred Hutchinson Cancer Center:

Single-cell transcriptomics and proteomics integrative analysis for cell atlas construction of CAR-T cell therapy CITE-seq data and association with gene and protein markers with clinical responses.
June 2020 to Present, Siqi Shen, Ph.D. Candidate at UW-Madison:

Co-mentor with Dr. Sunduz Keles on single-cell 3D chromatin organization normalization and integrative analysis with single-cell transcriptomics and epigenomics.
June 2020 to Sep. 2021, Fanding Zhou, VISP student at UW-Madison, Currently Ph.D. student at UC Berkeley:

Co-mentor with Dr. Sunduz Keles on constructing tree-based statistical models for the false discovery rate control of 3D chromatin organization differential detection.
Summer 2019, Olivia Rae Steidl, Summer Undergraduate Student at University of Wisconsin - Madison, Currently Ph.D. student at University of Wisconsin - Madison:

Co-mentor with Dr. Sunduz Keles on investigation of the poly(UG) tails at the end of RNAs and its function in human using eCLIP-seq data.