Type
Text
Type
Dissertation
Advisor
Wu, Song | Xing, Haipeng | Wang, Xuefeng | Zhu, Wei | Jia, Jiangyong.
Date
2015-12-01
Keywords
Change-Point | Statistics
Department
Department of Applied Mathematics and Statistics.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/76294
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Advances in next-generation sequencing technologies are revolutionizing our ability to detect copy number variations (CNVs). Single-cell sequencing technology allows for the genome wide copy number analysis within a single nucleus which is isolated form mixed population of cells. It can avoid the disadvantage of genomic differences in complex mixtures of cells. Many statistical methods and tools have been developed for CNVs detection using high-throughput sequencing data, but most methods are not designed for low-coverage sequencing data. In this article, we present a new Bayesian based change-point Model which has never been used for CNVs detection before and propose two similarity scores to discover DNA CNVs with low-coverage single-cell sequencing data and compare with other popular methods. | 119 pages
Recommended Citation
Qi, Huan, "High-resolution Detection of Change-Point with Low Coverage Single-cell Sequencing Data" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 2219.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/2219