Utilizing high throughput gene expression data stored in public archives not only saves research time and cost but also enhances the
power of its statistical support. However, gene expression profiling data can be obtained from many different technical platforms. Same gene
expressions quantified by different platforms have different distributional properties, which makes the data integration across multiple
platforms challenging. Several cross-platform normalization methods developed and tried to remove the differences caused by the platform
discrepancy but they also remove the important biological signals as well. Zhang and Jiang (2015) introduced a new method focusing on
eliminating platform effect among systematic effects by employing matched samples which are measured by different platforms for getting a
benchmark model. Since the matched sample have no biological difference, their approach is robust to get rid of solely the platform effect. They
showed that the new method performs better than Distance Weighted Discrimination (DWD) method. This paper is a follow-up study of their work and
we attempt to improve the new method by incorporating Fast Linear Mixed Regression (FLMER) model. The result indicates that the FLMER model
works better than the original proposed model, OLS (Ordinary Least Squares) model in after-normalization concordance comparison and Differential
Expression(DE) analysis. Also, we compare our methods to other existing cross-platform normalization methods not only DWD but also Empirical
Bayes methods, XPN and GQ methods. The results showed that the proposed method performs much better than other cross-platform normalization
methods for removing platform differences and keeping the biological information. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the
degree of Doctor of Philosophy. / Fall Semester 2018. / October 15, 2018. / Cross-platform normalization / Includes bibliographical references. / Jinfeng Zhang, Professor Directing Dissertation; Qing-Xiang Amy Sang, University Representative; Wei Wu,
Committee Member; Xu-Feng Niu, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_661218 |
| Contributors | Zhang, Se Rin (author), Zhang, Jinfeng (professor directing dissertation), Sang, Qing-Xiang (university representative), Wu, Wei (committee member), Niu, Xufeng, 1954- (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (75 pages), computer, application/pdf |
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