Jun Zhang of the Department of Biostatistics defends her dissertation on "Interpretable Analysis of Multivariate Functional Data".
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
Harness the Heterogeneity in Omics Studies
As high-throughput technologies being developed and widely applied in biomedical research, omics studies and datasets have been accumulated in public domain. Although these studies generated numerous biological findings, omics studies have been criticized for low reproducibility and inconsistent results due to high heterogeneity among studies addressing the same or similar biological questions. In this talk, I will discuss a Bayesian hierarchical model we recently developed to draw robust conclusion by combining multiple studies, while controlling for the heterogeneity among them. In addition, this method can also explore the potential biological meaning of the heterogeneity by a clustering analysis. I will demonstrate the usage of this method using real data examples. In the second part of my talk, I will discuss another method that we developed to accommodate the heterogeneity in effect sizes among different biomarkers when conducting SNV-set analysis in genome-wide association studies. I will discuss the application of this method in detecting dense and sparse signals in various studies.
Last Updated On Friday, January 3, 2020 by Tang, Lu
Created On Wednesday, December 18, 2019
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