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Yun Zhang, PhD, is a Staff Scientist-Biostatistician 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
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