| dc.identifier.uri | http://hdl.handle.net/11401/77521 | |
| 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 | With the recent advances in high resolution microarrays and next generation sequencing, DNA copy number can now be profiled in a high throughput global manner. This has enabled the systematic study of DNA copy number alterations in tumors, as well as the profiling of inherited population-wide copy number variants. Studies of DNA copy number usually involve many samples that fall into different groups, e.g. tumor subtype or ethnic group. It is often of interest to find recurrent alterations within each group. We develop a stochastic segmentation model for detecting recurrent DNA copy number alterations in grouped array-CGH data. In our model, the parameter in each regime is a random variable following specific regime-specific distribution. Explicit formulas for posterior means can be used to estimate the signal directly without performing segmentation. We give a linear-time algorithm for fitting this model and for estimating its parameters by expectation maximization. Simulation studies and applications to real grouped array-CGH data illustrate the advantages of the proposed model. | |
| dcterms.available | 2017-09-20T16:52:51Z | |
| dcterms.contributor | Xing, Haipeng | en_US |
| dcterms.contributor | Zhu, Wei | en_US |
| dcterms.contributor | Wu, Song | en_US |
| dcterms.contributor | Xu, Jinfeng. | en_US |
| dcterms.creator | Cai, Ying | |
| dcterms.dateAccepted | 2017-09-20T16:52:51Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:51Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 117 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/77521 | |
| dcterms.issued | 2013-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:51Z (GMT). No. of bitstreams: 1
Cai_grad.sunysb_0771E_11633.pdf: 20220251 bytes, checksum: 474d07d78126a4af81d6bb94efa32984 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Statistics | |
| dcterms.subject | Bounded Complexity Mixture Approximation, Expectation Maximization, Hidden Markov Model, Recurrent Copy Number Alterations | |
| dcterms.title | A Stochastic Segmentation Model for Recurrent Copy Number Alterations in Grouped array-CGH Data | |
| dcterms.type | Dissertation | |
