Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, February 1 from 12.00 - 1.30 pm in CGIS Knafel Room 354. John Quackenbush, Professor of Biostatistics and Computational Biology and Director of the Center for Cancer Computational Biology at the Dana-Farber Cancer Institute, will give a presentation entitled "Moving Beyond the Mean: The Role of Variation in Determining Phenotype". As always, a light lunch will be provided.

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. 'Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited, in large part because the high-dimensional they produce cannot be fully constrained by the limited number of measurements and in part because the data themselves represent only a small part of the complete story. To circumvent these limitations, we have developed methods that combine 'omic data with other sources of information in an effort to leverage, more completely, the compendium of information that we have been able to amass. Here we will present a number of approaches we have developed, with an emphasis on the how those methods have provided into the role that particular cellular pathways play in driving differentiation, and the role that variation in gene expression patterns influences the development of disease states. In particular, we will challenge the basic analytical that have been used in biomedical research and argue that one should move beyond a simple comparison of the means relative to variance (the t-test) but instead also consider how variance itself changes between phenotypes. Looking forward, we will examine more abstract state-space models that may have potential to lead us to a more general predictive, theoretical biology.