A Ranksum Statistics Based Framework to Decipher Transcription Regulation

Luan-Bor Chen
2012
The unbiased generation of specific and meaningful hypotheses from the deluge of data generated by modern genomic methods remains a challenge. These datasets require increasing level of expertise to analyze fully, and are often underutilized even in the originating lab. It would be desirable to have a computational strategy that is easy to implement, robust against outliers and missing data, and broadly applicable to diverse experimental designs. In this dissertation, I present a set of ranksum
more » ... nt a set of ranksum statistics-based analytical methods as a framework to extract testable hypotheses from large and complex datasets. To illustrate its utility, this framework was applied to two clinically important biological questions. In both instances, my method yielded novel molecular mechanisms that were subsequently validated with both in vitro and in vivo experiments. In the first study, gene expression profiles from multiple mouse models of cardiac hypertrophy were analyzed to reveal a novel interaction between transcription factors Nkx2-5 and Egr1, providing mechanistic insight into how Nkx2-5 haploinsufficiency leads to exacerbated cardiac hypertrophy and poor survival in these mice. In the second study, thousands of microarray samples acquired from public data repositories were analyzed to quantitatively define tissue-specific expression pattern for every gene represented on a microarray platform. The tissue-specific expression data was then used to identify novel transcriptional regulators of brown fat gene expression program in adipocytes. The successful application of the analytical framework in these examples, regardless of their differing experimental design, highlights its adaptability in facilitating discoveries in a wide array of biological problems. iii Acknowledgments The thesis work presented here would not be possible without the support of my supervisor and good friend Patrick Jay. Pat is the very model of a modern physicianscientist. He is tireless and fearless in pursuit of ever-grander scientific challenges. I can't thank Pat enough for being the perfect guide and saving me from many pitfalls through the course of my thesis work. On a personal note, I wish to thank Pat and his wife Kathleen for treating me like family. They have shown me warmth and generosity that will not be soon forgotten. The great team Pat has assembled in the lab over the years is another reason I feel very fortunate to call the Jay lab my scientific home. Suk Regmi, Vinay Rathi, Diana DeAndrade, Yali Lu, and Min Li have chipped in countless hours of experiments and discussion that propelled me towards the finish line. They are my friends and my teachers, and they will always have my gratitude. I also wish to thank my current and past thesis committee members Drs. have each shared their equipment, reagents and expertise with me in my thesis research. I also owe my gratitude to the administrative staff at the Division of Biology & Biomedical Sciences, the Office for International Students and Scholars, and the Department of Pediatrics, iv especially Melanie Puhar and Barbara Kloeckener, who magically made all the paperwork invisible. Graduate study would have been a very lonely road without the camaraderie of fellow DBBS graduate students. Drs. and future Drs. have my eternal gratitude for our collaborations and friendship. I want to also thank many good friends St. Louis has blessed me with. I thank Anatoly, Raj,
doi:10.7936/k70v8b75 fatcat:zzxvrafl7jdelgcjcgsqdj5jxm