My primary research interest is in statistical analysis of genomic data from high-throughput sequencing experiments.


Publications

  • Diffsig: Associating Risk Factors With Mutational Signatures, JE Park, M A. Smith, S C. Van Alsten, A Walens, D Wu, K A. Hoadley, M A. Troester, M I. Love, 2023, DOI: 10.1101/2023.02.09.527740


Research Projects

Recent

  • Diffsig: Associating Risk Factors With Mutational Signatures
    • Diffsig is a Bayesian hierarchical modeling R package that allows to estimate associations between multiple risk factors and mutational signatures. Our package allows to test associations on various types of risk factors - binary, continuous, categorical -

Past

  • PM2.5 effect on brain function based on mouse RNAseq
    • Analyze the effects of PM2.5 on mice brain function with RNAseq in areas including: Cerebellum, Cortex, etc.
  • Microbiome Project 2 - aVISTA + Cytoxan effect on breast cancer mice microbiome
    • Microbiome research based on mouse fecal samples DNA extraction and cutaneous humanbreast microbiome samples
    • Aim to determine the effects of aVISTA, Cyclophosphamide and Radiation Treatment effectson human and mice microbiome to understand the treatment effects on breast cancer.
  • Hypertension Research
    • Aim to determine the association of salt intake and hypertension, metabolic syndrome, andARB treatment effect from a 10K+ hypertension patients data attained from a K-MetS study
  • Myocardial Infarction(MI) - Stroke Research
    • Use Cox models to identify effect modification by demographics, stroke risk factors e.g. hypertension, diabetes, etc., and Charlson comorbidities
  • Hypertension Research
    • Aim to determine the association of salt intake and hypertension, metabolic syndrome, andARB treatment effect from a 10K+ hypertension patients data attained from a K-MetS study
  • Myocardial Infarction(MI) - Stroke Research
    • Survival analysis to identify effect modification by demographics, stroke risk factors e.g. hypertension, diabetes, etc., and Charlson comorbidities
  • Microbiome Project 1 - Workflow development of microbiome data analysis
    • Develop a comprehensive workflow for microbiome data analysis based on our analysis
    • Analyze 16S rRNA gene sequence data from a study to understand the effect of fructose consumption on mouse gut microbiome
  • Comparing clustering methods for single-cell RNA sequencing data (advisor: Davide Risso)
    • Compared clustering methods for analyzing Single-Cell RNA Sequencing Data
    • Performed data processing, simulation based on gamma-poisson distribution, normalization,dimensionality reduction (PCA) with R packages scater, splatter, clusterExperiment, etc.