DMS Colloquium: Steve Qin

Speaker: Steve Qin,  Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University

Title: Utilizing historical data to aid statistical inference of high throughput data with low sample size 

Abstract: Modern high-throughput biotechnologies such as microarray and next generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high throughput experiment, only limited amount of data are observed for each individual feature, thus the classical "large p, small n" problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical models, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the drawback and propose several alternative solutions. Our strategy is rooted in the facts that in the Big Data era, large amount of historical data are available which can and should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model.

This is a joint work with Ben Li.

Host: Peng Zeng