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34170.pdf (5 MB)
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Abstract Header
Controlling false positive rate in network analysis of transcriptomic data
Author Info
Xu, Huan
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin156267322069819
Abstract Details
Year and Degree
2019, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Abstract
Network analysis has been playing an important role in bioinformatics analysis of transcriptomic data. By identifying differentially expressed genes that are “connected” with other differentially expressed genes, or genes that are not differentially expressed but “connected” with differentially expressed genes, it is meant to associate genes and phenotypes that simple differential expression analysis cannot do. Despite its popularity, the false positive discoveries in network analysis of transcriptomic data has not been systematically reviewed to date. In this study, we define false positive rate for each gene as its probability of being implicated in the null datasets, and seek to identify potential factors that could contribute to the inflated false positive rate for network analysis. By evaluating representative subnetwork detection, network propagation, and heterogeneous network analysis methods for transcriptomic data, we first demonstrated the inter-gene correlation, network node degree, and heterogeneous node association as the potential factors. We then proposed and evaluated two methods to reduce the bias caused by node degree and inter-gene correlation respectively and demonstrated their utility. The node degree and the inter-gene correlation are intrinsic factors of the biologic network and the transcriptomic data. Our solution to reduce the false positive rate caused by these factors are generalizable to other network analysis methods and transcriptomic data.
Committee
Mario Medvedovic, Ph.D. (Committee Chair)
Anil Jegga, D.V.M. (Committee Member)
Liang Niu, Ph.D. (Committee Member)
Marepalli Rao, Ph.D. (Committee Member)
Pages
115 p.
Subject Headings
Bioinformatics
Keywords
Network analysis
;
transcriptomic data
;
false positive rate
;
inter-gene correlation
;
network node degree
;
heterogeneous node association
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Citations
Xu, H. (2019).
Controlling false positive rate in network analysis of transcriptomic data
[Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin156267322069819
APA Style (7th edition)
Xu, Huan.
Controlling false positive rate in network analysis of transcriptomic data.
2019. University of Cincinnati, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin156267322069819.
MLA Style (8th edition)
Xu, Huan. "Controlling false positive rate in network analysis of transcriptomic data." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin156267322069819
Chicago Manual of Style (17th edition)
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Document number:
ucin156267322069819
Download Count:
109
Copyright Info
© 2019, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.