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Issues in information integration of omics data: microarray meta-analysis for candidate marker and module detection and genotype calling incorporating family information

Chang, Lun-Ching (2014) Issues in information integration of omics data: microarray meta-analysis for candidate marker and module detection and genotype calling incorporating family information. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Nowadays, more and more high-throughput genomic data sets are publicly available; therefore, performing meta-analysis to combine results from independent studies becomes an essential approach to increase the statistical power, for example, in the detection of differentially expressed genes in microarray studies. In addition to meta-analysis, researchers also incorporate pathway or clinical information from external databases to perform integrative analysis. In this thesis, I will present three projects which encompass three types of integrative analysis. First, we perform a comprehensive comparative study to evaluate 12 microarray meta-analysis methods in simulation studies and real examples by using four quantitative criteria: detection capability, biological association, stability and robustness, and we propose a practical guideline for practitioners to choose the most appropriate meta-analysis method in real applications. Second, we develop a meta-clustering method to construct co-expressed modules from 11 major depressive disorder transcriptome datasets, incorporated with GWAS and pathway information from external databases. Third, we propose a computationally feasible algorithm to call genotypes with higher accuracy by considering family information from next generation sequencing data for two purposes: (1) to propose a new genotype calling algorithm for complex families, and (2) to extend our algorithm to incorporate external reference panels to analyze family-based sequence data with a small sample size. In conclusion, we develop several integrative methods for omics data analysis and the result improves public health significance for biomarker detection in biomedical research and provides insights to help understand the underlying disease mechanisms.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Chang, Lun-Chingluc15@pitt.eduLUC15
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairTseng, Georgectseng@pitt.eduCTSENG
Committee CoChairChen,
Committee MemberSibille,
Committee MemberWeeks, Daniel E.weeks@pitt.eduWEEKS0000-0001-9410-7228
Committee MemberPark, Yongseokyongpark@pitt.eduYONGPARK
Date: 29 September 2014
Date Type: Publication
Defense Date: 17 April 2014
Approval Date: 29 September 2014
Submission Date: 4 June 2014
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 128
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Microarray, GWAS, NGS
Date Deposited: 29 Sep 2014 21:32
Last Modified: 30 Jun 2022 15:53


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