Computational Analysis of Genome-Wide DNA Copy Number Changes

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dc.contributor.authorSong, Leien
dc.contributor.committeechairWang, Yue J.en
dc.contributor.committeememberLu, Chang-Tienen
dc.contributor.committeememberXuan, Jianhuaen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:35:55Zen
dc.date.adate2011-06-01en
dc.date.available2014-03-14T20:35:55Zen
dc.date.issued2011-05-03en
dc.date.rdate2011-06-01en
dc.date.sdate2011-05-09en
dc.description.abstractDNA copy number change is an important form of structural variation in human genome. Somatic copy number alterations (CNAs) can cause over expression of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale, with high resolution. Quantitative analysis of somatic CNAs on genes has found broad applications in cancer research. Most tumors exhibit genomic instability at chromosome scale as a result of dynamically accumulated genomic mutations during the course of tumor progression. Such higher level cancer genomic characteristics cannot be effectively captured by the analysis of individual genes. We introduced two definitions of chromosome instability (CIN) index to mathematically and quantitatively characterize genome-wide genomic instability. The proposed CIN indices are derived from detected CNAs using circular binary segmentation and wavelet transform, which calculates a score based on both the amplitude and frequency of the copy number changes. We generated CIN indices on ovarian cancer subtypes' copy number data and used them as features to train a SVM classifier. The experimental results show promising and high classification accuracy estimated through cross-validations. Additional survival analysis is constructed on the extracted CIN scores from TCGA ovarian cancer dataset and showed considerable correlation between CIN scores and various events and severity in ovarian cancer development. Currently our methods have been integrated into G-DOC. We expect these newly defined CINs to be predictors in tumors subtype diagnosis and to be a useful tool in cancer research.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05092011-161650en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05092011-161650/en
dc.identifier.urihttp://hdl.handle.net/10919/32462en
dc.publisherVirginia Techen
dc.relation.haspartSong_L_T_2011.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDNA Copy Number Changesen
dc.subjectCircular Binary Segmentationen
dc.subjectHaar Wavelet Transformen
dc.subjectChromosome Instability Indexen
dc.subjectGeorgetown Database of Canceren
dc.titleComputational Analysis of Genome-Wide DNA Copy Number Changesen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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