About Me

Education

Publications

[1] Guo, Z.‡, Chen, D.‡, Stopsack, K. H., Soule, P., Ajit, D., Ramamoorthy, P., Hoffmann, T. J., Chan, J. M., Mucci L. A., Graff, R. E. (2025). Metabolomic Disparities Between Black and Non-Hispanic White Men with Metastatic Hormone-Sensitive Prostate Cancer: A Pilot Study. (Manuscript in preparation)

[2] Poster session presenter and first author, Causal effect of type II diabetes on prostate cancer in the East Asian population: A two-sample Mendelian randomization study, AACR Special Conference: Aging and Cancer, 2022. published ↗

‡ indicates co-first authorship.

Experience

Research

I was very fortunate to join the PsychENCODE project at Prof. Anshul Kundaje's lab. In this project, I analyzed scRNA-seq and scATAC-seq multiome data of 3 brain regions from PsychENCODE to investigate the pathogenesis of schizophrenia (SCZ) and bipolar disorder (BPD).

Since chromatin accessibility and gene regulation are highly cell-type-specific, this project aims to analyze them at single-cell resolution. The overarching goal is to understand how variations in chromatin accessibility and gene expression across different cell types contribute to the pathogeneses of SCZ and BPD compared to controls.

Here is what I have done:

• Pre-processed the single-cell multiome dataset using quality control, dimension reduction, annotation with reference, cell clustering, doublet removal, and marker gene detection.
• Conducted differential gene expression and peak accessibility analyses (pseudobulk: DESeq2; single-cell: Wilcoxon rank-sum test and MAST) across cell types by sex and disease.
• Creatively visualized and validated differential patterns for all detected genes and peaks, presenting findings to collaborators.

Here is what I will do:

• Perform gene ontology analysis to elucidate the biological pathways associated with the identified differentially expressed genes.
• Cross-reference cell-type-specific ChromBPNet ↗ outputs to address two key questions:
  • Which disease variants are causal for SCZ and BP
  • How mutations impact chromatin accessibility and, consequently, gene regulation

Representative packages I have used in this project:

• R: Seurat, Signac, Azimuth, DoubletFinder, EnsDb, SingleCellExperiment, DESeq2
• Python: CellBender, chrombpnet

Black men experience significantly higher incidence and mortality rates of prostate cancer compared to White men. This project aims to investigate this racial disparity through LC-MS metabolomics.

The cohort included 17 Black men and 17 White men in the United States, from the IRONMAN Registry ↗.

Here is what I have done:

• Discovered key contributors through chemical similarity enrichment analysis (ChemRICH) by designing and implementing three methods: sub-pathway information, correlation modules, and predicted Medical Subject Headings (MeSH) classes.
• Found corroborative contributing metabolites using Principle Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Random Forest, Support Vector Machine (SVM), logistic regression, quantitative set enrichment, and pathway analysis using MetaboAnalystR.
• Identified upregulated/downregulated compounds influenced by aging through differential expression analysis using limma, after network construction and module detection using weighted gene co-expression network analysis (WGCNA), motivating research into aging's role.

The manuscript is in preparation. It will also be my thesis for my Master degree.

Representative R packages: tidyverse, ggplot2, ChemRICH, WGCNA, MetaboAnalystR, limma

Teaching

Class Projects

to be updated...

Work

Technical Summary