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Integration and Missing Data Handlin...

Fang, Zhou.

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Integration and Missing Data Handling in Multiple Omics Studies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integration and Missing Data Handling in Multiple Omics Studies./
作者:	Fang, Zhou.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	124 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-04(E), Section: B.
Contained By:	Dissertation Abstracts International80-04B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13804205
ISBN:	9780438719323

Integration and Missing Data Handling in Multiple Omics Studies.
Fang, Zhou.

Integration and Missing Data Handling in Multiple Omics Studies. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 124 p.

Source: Dissertation Abstracts International, Volume: 80-04(E), Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2018.

In modern multiple omics high-throughput data analysis, data integration and missingness data handling are common problems in discovering regulatory mechanisms associated with complex diseases and boosting power and accuracy. Moreover, in genotyping problem, the integration of linkage disequilibrium (LD) and identity-by-descent (IBD) information becomes essential to reach universal superior performance. In pathway analysis, when multiple studies of different conditions are jointly analyzed, simultaneous discovery of differential and consensual pathways is valuable for knowledge discovery. This dissertation focuses on the development of a Bayesian multi-omics data integration model with missingness handling, a novel genotype imputation method incorporating both LD and IBD information, and a comparative pathway analysis integration method.

ISBN: 9780438719323Subjects--Topical Terms:

1002712
Biostatistics.

Integration and Missing Data Handling in Multiple Omics Studies.
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500 $a Source: Dissertation Abstracts International, Volume: 80-04(E), Section: B.
500 $a Adviser: George C. Tseng.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2018.
520 $a In modern multiple omics high-throughput data analysis, data integration and missingness data handling are common problems in discovering regulatory mechanisms associated with complex diseases and boosting power and accuracy. Moreover, in genotyping problem, the integration of linkage disequilibrium (LD) and identity-by-descent (IBD) information becomes essential to reach universal superior performance. In pathway analysis, when multiple studies of different conditions are jointly analyzed, simultaneous discovery of differential and consensual pathways is valuable for knowledge discovery. This dissertation focuses on the development of a Bayesian multi-omics data integration model with missingness handling, a novel genotype imputation method incorporating both LD and IBD information, and a comparative pathway analysis integration method.
520 $a In the first paper of this dissertation, inspired by the popular Integrative Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that allows incorporation of samples with missing omics data as well as a self-learning cross-validation (CV) decision scheme. Simulations and a real application on child asthma dataset demonstrate superior performance of the CV decision scheme when various types of missing mechanisms are evaluated.
520 $a In the second paper, we propose a genotype inference method, namely LDIV, to integrate both LD and IBD information. Both simulation study with different family structures and real data results showed that LDIV greatly increases the genotype accuracy, especially when family structures are informative.
520 $a The third paper presents a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to discover consensual and differential enrichment patterns, reduce pathway redundancy, and assist explanation of the pathway clusters with novel text mining algorithm. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies and new enrichment patterns.
520 $a All three projects could have substantial public health importance. By handling missing data, a higher statistical power and accuracy in clinical prediction and biomarker selection can be retained by FBM given fixed budget and sample size. LDIV effectively increases genotyping accuracy. CPI simultaneously discovers biological processes that function differentially and consensually across studies. It will also assist scientists to explore pathway findings with reduced redundancy and more statistical backup.
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