Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
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.
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.
Biostatistics guest speaker, Zhixiang Lin, Stanford University, will present, "Statistical methods for high-throughput genomic data."
In the first part of the talk, a dimension reduction method will be introduced where we extend Principal Component Analysis to propose AC-PCA for simultaneous dimension reduction and Adjustment for Confounding variation. We show that AC-PCA can adjust for variations across individual donors present in a human brain dataset. For gene selection purposes, we extend AC-PCA with sparsity constraints, and propose and implement an efficient algorithm. The second part of the talk will be focused on clustering methods in single cell genomics. In single cell genomics, it is technically challenging to obtain chromatin accessibility and gene expression data for the same cell. We have developed a computational approach to this problem, where a model-based clustering method is proposed to match cell sub-populations in these two data types. We also demonstrate that using one data type can guide clustering of the other data type. Our proposed Bayesian model accounts for the stochasticity due to biological and technical effects. Last, methodologies motivated by spatial temporal modeling of gene expression dynamics during human brain development will be briefly discussed.
Last Updated On Friday, January 12, 2018 by Kapko, Bernadette E
Created On Friday, January 12, 2018