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Yun Zhang, PhD, is an assistant professor in the Informatics Department at the J. Craig Venter Institute (JCVI). She received an MMath in Mathematics and Statistics from the University of Oxford, UK, and a PhD in Statistics from the University of Rochester Medical Center. She also has industrial and research experience in Novartis Oncology and Mayo Clinic.
Dr. Zhang’s research interest includes statistical modeling and methodology development for big data produced by advanced biotechnologies. She is experienced in analyzing time-course microarray data, DNA methylation data, and microRNA sequencing data. She is also a professional developer of R and Bioconductor packages. Her recent focus is on applying statistical approaches to single cell RNA sequencing (scRNAseq) data.
Research Priorities
Mapping cell populations in scRNAseq data
- Development of statistical approach for comparing new experimental data with cell type reference definitions to determine if new data represent existing or novel cell types
- Development of statistically-comparable representation of reference cell types for the Human Cell Atlas
Gene set enrichment analysis (GSEA) pipelines with overlapping genes
- Established pipeline FUNNEL-GSEA for time-course gene expression data using functional data analysis techniques
- Development of data-driven method to empirically decompose the gene membership among multiple overlapped pathways
- Extension of FUNNEL to data with limited time points, e.g. cross-sectional data, pre-post study, etc.
Investigation of tissue composition on gene co-expression
- Investigation of the effect of composite cellular types on reconstruction of gene co-expression network
- Application of deconvolution algorithm to tissue composition problems
Publications
A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.
Genome research. 2021-10-01; 31.10: 1767-1780.
PMID: 34088715
FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test.
Briefings in bioinformatics. 2020-11-30;
PMID: 33249453
Epidemiology and Sequence-Based Evolutionary Analysis of Circulating Non-Polio Enteroviruses.
Microorganisms. 2020-11-25; 8.12:
PMID: 33255654
Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable.
Proceedings of the National Academy of Sciences of the United States of America. 2020-06-09; 117.23: 12522-12523.
PMID: 32381734
Longitudinal Study of Oral Microbiome Variation in Twins.
Scientific reports. 2020-05-14; 10.1: 7954.
PMID: 32409670
A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2.
Cell host & microbe. 2020-04-08; 27.4: 671-680.e2.
PMID: 32183941
The effect of tissue composition on gene co-expression.
Briefings in bioinformatics. 2019-12-08;
PMID: 31813949
Host-Microbial Interactions in Systemic Lupus Erythematosus and Periodontitis.
Frontiers in immunology. 2019-11-12; 10.2602.
PMID: 31781106
Highly efficient hypothesis testing methods for regression-type tests with correlated observations and heterogeneous variance structure.
BMC bioinformatics. 2019-04-15; 20.1: 185.
PMID: 30987598
Statistical method evaluation for differentially methylated CpGs in base resolution next-generation DNA sequencing data.
Briefings in bioinformatics. 2018-05-01; 19.3: 374-386.
PMID: 28040747
FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.
Bioinformatics (Oxford, England). 2017-07-01; 33.13: 1944-1952.
PMID: 28334094
Research Priorities
Mapping cell populations in scRNAseq data
- Development of statistical approach for comparing new experimental data with cell type reference definitions to determine if new data represent existing or novel cell types
- Development of statistically-comparable representation of reference cell types for the Human Cell Atlas
Gene set enrichment analysis (GSEA) pipelines with overlapping genes
- Established pipeline FUNNEL-GSEA for time-course gene expression data using functional data analysis techniques
- Development of data-driven method to empirically decompose the gene membership among multiple overlapped pathways
- Extension of FUNNEL to data with limited time points, e.g. cross-sectional data, pre-post study, etc.
Investigation of tissue composition on gene co-expression
- Investigation of the effect of composite cellular types on reconstruction of gene co-expression network
- Application of deconvolution algorithm to tissue composition problems
17-Mar-2020
Blog
Coronavirus Pandemic: Putting Comprehensive Genomic Data in the Hands of Frontline Researchers Worldwide is Paramount
12-Mar-2020
Collaborator Release