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Matched Sample Based Approach for Cross-Platform Normalization on Gene Expression Data

Title: Matched Sample Based Approach for Cross-Platform Normalization on Gene Expression Data.
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Name(s): Shao, Jiang, author
Zhang, Jinfeng, professor directing dissertation
Sang, Qing-Xiang Amy, university representative
Wu, Wei, committee member
Niu, Xufeng, committee member
Florida State University, degree granting institution
College of Arts and Sciences, degree granting college
Department of Statistics, degree granting department
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2015
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (82 pages)
Language(s): English
Abstract/Description: Gene-expression data profile are widely used in all kinds of biomedical studies especially in cancer research. This dissertation work focus on solving the problem of how to combine datasets arising from different studies. Of particular interest is how to remove platform effect alone. The matched sample based cross-platform normalization method we developed are designed to tackle data merging problem in two scenarios: The first is affy-agilent cross-platform normalization which are belong to classic microarray gene expression profile. The second is the integration of microarray data with Next Generation Sequencing genome data. We use several general validation measures to assess and compare with the popular Distance-weighted discrimination method. With the public web-based tool NCI-60 CellMiner and The Cancer Genome Atlas data portal supported, our proposed method outperformed DWD in both cross-platform scenarios. It can be further assessed by the ability of exploring biological features in the studies of cancer type discrimination. We applied our method onto two classification problem: One is Breast cancer tumor/normal status classification on microarray and next generation sequencing datasets; The other is Breast cancer patients chemotherapy response classification on GPL96 and GPL570 microarray datasets. Both problems show the classification power are increased after our matched sample based cross-platform normalization method.
Identifier: FSU_2015fall_Shao_fsu_0071E_12833 (IID)
Submitted Note: A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Fall Semester 2015.
Date of Defense: September 1, 2015.
Bibliography Note: Includes bibliographical references.
Advisory Committee: Jinfeng Zhang, Professor Directing Dissertation; Qing-Xiang (Amy) Sang, University Representative; Wei Wu, Committee Member; Xufeng Niu, Committee Member.
Subject(s): Statistics
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_2015fall_Shao_fsu_0071E_12833
Owner Institution: FSU

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Shao, J. (2015). Matched Sample Based Approach for Cross-Platform Normalization on Gene Expression Data. Retrieved from http://purl.flvc.org/fsu/fd/FSU_2015fall_Shao_fsu_0071E_12833