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Statistical design and analysis of microarray experiments

Wang, Tao
http://rave.ohiolink.edu/etdc/view?acc_num=osu1117201363

Abstract Details

2005, Doctor of Philosophy, Ohio State University, Statistics.
Microarray, a bio-technology that allows monitoring of gene expressions for thousands of genes simultaneously, has revolutionized biological and genomic research and holds promising potentials in many real applications, such as drug targeting, gene profiling, disease diagnosis and prognosis, pharmacogenomics, etc. Along with its unprecedented potential, microarray technology presents miscellaneous challenges in statistical analysis of microarray gene expression data. Many sources of extraneous variations are present in a microarray experiment. Adjusting these extraneous variations is critical to the separation of biological signals from artifacts. Moreover, microarray gene expression data typically are of extremely large dimension, consisting of tens of thousands of observations. Computational efficiency in statistical analysis is therefore crucial. For testing the significance of biological signal, multiplicity adjustment is indispensable. We propose a modeling approach that allows flexible experimental design, while providing accurate estimation and easy multiplicity-adjusted inferences. This modeling approach is suitable for various types of microarrays, including both cDNA and oligonucleotide microarrays. The statistical modeling and multiplicity-adjusted inference are integrated into an R package, MultiArray , as a computationally efficient environment. Real microarray experiment examples show that our modeling approach and MultiArray outperform other popular packages in both detecting differences and establishing equivalence in gene expressions.
Jason Hsu (Advisor)
146 p.
Statistics
microarray; gene expression; statistical analysis; modeling; statistical design

Recommended Citations

Citations

  • Wang, T. (2005). Statistical design and analysis of microarray experiments [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1117201363

    APA Style (7th edition)

  • Wang, Tao. Statistical design and analysis of microarray experiments. 2005. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1117201363.

    MLA Style (8th edition)

  • Wang, Tao. "Statistical design and analysis of microarray experiments." Doctoral dissertation, Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1117201363

    Chicago Manual of Style (17th edition)

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© 2005, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.