Representation and Hypothesis Testing

报告摘要：Tree-shaped data set is increasingly common in real world, such as gene expression data measured on a cell lineage tree. Due to its complex structure, especially changing correlation along the tree branch, classical formula of Pearson correlation coefficient cannot be applied directly to calculate correlation between tree-shaped data sets. In our study, a statistical model with correlation-gradually-weaken mechanism for tree-shaped data is proposed first, and then a Bayesian approach is applied to estimate the tree correlation; secondly, a simple and intuitive graph representation is used to demonstrate the geometric significance of tree correlation; finally,  and  tests are constructed to test the hypotheses on tree correlations. Extensive simulations are completed to demonstrate the validity and compatibility of our model and algorithm. Furthermore, the application to public datasets of gene expression measured on cell lineage tree and academic information on pedigree tree show that our method can capture the correlation between tree-shaped data sets well.

主讲人：胡杰，助理教授

报告时间：2020年12月1日 16:00

报告地点：综合楼8楼810

个人简介：胡杰，本科毕业于北京大学数学科学学院，2015年博士毕业于香港中文大学统计系。同年入职厦门大学数学科学学院，任职助理教授至今。主要研究方向为生物信息学、统计计算、应用统计等领域。在国际知名期刊Bioinformatics, Annals of Applied Statistics, Journal of Theoretical Biology等发表论文多篇。