Shiyue Tao
PhD student
Contact
Hi! I'm Shiyue Tao, a new PhD student in Biostatistics at the University of Pittsburgh. I'm currently part of Prof. Wei Chen's research group, engaged in statistical genetics and genomics research. My focus is on developing statistical methods for genetic and genomic datasets and collaborating with biomedical scientists to analyze high-dimensional data related to lung and allergic diseases. This includes working with omics data like Whole-Genome/Exome Sequencing (WGS/WES), bulk and single-cell RNA-seq, ATAC-seq, spatial transcriptomics, and single-cell multi-modal datasets such as Multiome and DOGMA-seq, to identify the biological mechanisms underlying disease etiology.
Before joining the University of Pittsburgh, I obtained my Bachelor of Science degree in Biostatistics from Nanjing Medical University's Department of Biostatistics, School of Public Health. In this period, I worked with Prof. Sheng Yang for three years on research projects of statistical genetics and genomics. My work included developing statistical methods and computational tools for post-GWAS studies, such as fine-mapping, Polygenic Risk Scores, and Mendelian Randomization. We also worked with clinicians, analyzing single-cell and bulk RNA-seq and spatial transcriptomics to address critical biological problems in diseases like asthma, breast cancer, lung squamous cell carcinoma, and Alzheimer's.
My research goal during the Ph.D. will be to enhance the understanding of non-coding regions by integrating multi-omics data at the single-cell level. This focus is driven by the crucial role non-coding regions play in gene regulation and expression. I plan to develop novel data analysis strategies using advanced statistical methods like mixed-effects models, Bayesian models, and machine learning. I believe this integrative approach will reveal new aspects of cellular functions, underpinning precision medicine and personalized treatments. Stay tuned for updates on my academic journey!
Before joining the University of Pittsburgh, I obtained my Bachelor of Science degree in Biostatistics from Nanjing Medical University's Department of Biostatistics, School of Public Health. In this period, I worked with Prof. Sheng Yang for three years on research projects of statistical genetics and genomics. My work included developing statistical methods and computational tools for post-GWAS studies, such as fine-mapping, Polygenic Risk Scores, and Mendelian Randomization. We also worked with clinicians, analyzing single-cell and bulk RNA-seq and spatial transcriptomics to address critical biological problems in diseases like asthma, breast cancer, lung squamous cell carcinoma, and Alzheimer's.
My research goal during the Ph.D. will be to enhance the understanding of non-coding regions by integrating multi-omics data at the single-cell level. This focus is driven by the crucial role non-coding regions play in gene regulation and expression. I plan to develop novel data analysis strategies using advanced statistical methods like mixed-effects models, Bayesian models, and machine learning. I believe this integrative approach will reveal new aspects of cellular functions, underpinning precision medicine and personalized treatments. Stay tuned for updates on my academic journey!
Research Interests
My research primarily focuses on developing efficient statistical methods and computational tools for high-dimensional genetics and genomics data, including WGS/WES, single-cell RNA-seq, single-cell ATAC-seq, spatial transcriptomics, and single-cell multi-modal datasets like Multiome and DOGMA-seq. A significant part of my work involves integrating single-cell multi-omics data and GWAS summary statistics for heritability enrichment analysis, particularly in identifying disease-enriched cell types.
In addition, my research engages in joint efforts with biomedical scientists to utilize statistical methods for the analysis of high-dimensional genetics and genomics data. This work focuses on understanding the mechanisms of disease progression, particularly in lung-related diseases such as asthma, influenza, and COVID-19. The goal is to identify underlying biological mechanisms mediating disease etiology and pathology, thereby contributing to a deeper understanding of these complex diseases.
