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Activity Number: 180 - Statistical Methods for Functional Genomic and Epigenomic Data
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305139
Title: A Change-Point Approach to Identify Hierarchical Organization of Topologically Associated Domains in Hi-C Data
Author(s): Yingru Wu* and Haipeng Xing and Yong Chen and Michael Q. Zhang
Companies: SUNY Stony Brook and SUNY Stony Brook and UT Dallas and UT Dallas
Keywords: Hi-C data; chromosome interaction; change-point; hierarchical clustering; generalized likelihood-ratio test; Gaussian process

In recent years, a new high throughput method of chromosome conformation capture (Hi-C) has revealed that genomes can be partitioned into topologically associating domains (TADs) with hierarchical structures. To identify organizations of TADs which is a novel clustering problem, we propose generalized likelihood ratio tests for change-points in Hi-C data and study their asymptotic distributions by Gaussian process. We further develop a top-down and bottom-up hierarchical clustering procedure to decipher TAD structures. Comprehensive tests of our method demonstrated its superiority in estimating hierarchical TADs. Our approach also discovered that TAD boundaries are enriched in active chromosomal regions than repressive ones, indicating more precise controlling and regulation functions of active chromosome regions.

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

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