Online Program Home
My Program

Abstract Details

Activity Number: 347
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #319176
Title: Analysis of Distributional Variation: A Multi-Resolution Scanning Approach with Applications to DNase-Seq Analysis
Author(s): Li Ma*
Companies: Duke University
Keywords: Bayesian inference ; scan statistic ; nonparametrics ; gene regulation ; sequencing data ; k-sample test
Abstract:

An inference task that holds key to numerous applications is the comparison of multiple data sets to identify the underlying difference. A fundamental challenge in modern multi-sample comparison problems is the presence of many potential confounders, or extraneous sources of variation that contribute to the difference across the distributions even within the same condition, resulting in false positives in many applications. We consider the ANOVA design that allows the intrinsic (i.e., scientifically interesting) variation in the probability distributions to be identified from the extraneous ones, under which replicate data sets are collected under each experiment setting. We introduce a flexible multi-resolution model-based framework for cross-group comparison that takes into account the experimental design using local hierarchical Binomial testing defined on scanning windows of a cascade of resolutions. We introduce a tree-structured graphical model hyperprior to incorporate spatial-scale dependency among the scanning windows thereby allowing effective borrowing of strength among them. We apply the method to DNase-seq for identifying differences in transcriptional factor binding.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association