| One of the central challenges in systems biology research is disentangling relevant and irrelevant sources of variation. While the relevant quantities are always context dependent, an important distinction can be drawn between variability due to biological processes and variability due measurement error. Biological variability includes variability between mRNA or protein abundances within a well defined condition, variability of these abundances across conditions (physiological variability), and between species or between subject variability. Technical variability includes measurement error, technological bias, and variability due to missing data. In this dissertation, we explore statistical challenges associated with disentangling sources of variability, both biological and technical, in the analysis of high-throughput biological data. In the first chapter, we present a careful meta-analysis of 27 yeast data sets supported by a multilevel model to separate biological variability from structured technical variability. In the second chapter, we suggest a simple and general approach for deconvolving the contributions of orthogonal sources of biological variability, both between and within molecules, across multiple physiological conditions. The results discussed in these two chapters elucidate the relative importance of transcriptional and post-transcriptional regulation of protein levels. Finally, in the third chapter we introduce a novel approach for modeling non-ignorable missing data. We illustrate the utility of this methodology on missing data in mRNA and protein measurements. |
