| dc.identifier.uri | http://hdl.handle.net/11401/76294 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.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. | |
| dcterms.available | 2017-09-20T16:49:58Z | |
| dcterms.contributor | Wu, Song | en_US |
| dcterms.contributor | Xing, Haipeng | en_US |
| dcterms.contributor | Wang, Xuefeng | en_US |
| dcterms.contributor | Zhu, Wei | en_US |
| dcterms.contributor | Jia, Jiangyong. | en_US |
| dcterms.creator | Qi, Huan | |
| dcterms.dateAccepted | 2017-09-20T16:49:58Z | |
| dcterms.dateSubmitted | 2017-09-20T16:49:58Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 119 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/76294 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:49:58Z (GMT). No. of bitstreams: 1
Qi_grad.sunysb_0771E_12627.pdf: 7163577 bytes, checksum: b5a7780355507e5e8692c5794ac4c0c2 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Statistics | |
| dcterms.subject | Change-Point | |
| dcterms.title | High-resolution Detection of Change-Point with Low Coverage Single-cell Sequencing Data | |
| dcterms.type | Dissertation | |
