Speaker: Prof. Stott Parker

Abstract:

A consequence of the abundance of data of all forms is that scientific research efforts are increasingly cutting across disciplines. Interdisciplinary research is difficult for many reasons, but among these are the difficulties of analyzing heterogeneous data and the lack of methods for collaborative construction of hypotheses. This is particularly true in fields like neuroscience, where the data is complex and ranges over many orders of magnitude in scale — and no single individual can hope to master it all.

In this talk I describe a system for exploration of hypotheses in phenotype data, implemented with a database obtained from several studies at UCLA. ViVA is a web-based system for analyzing hypotheses about variance structure, permitting exploratory analysis of GLMs. It permits visual identification of phenotype profiles (patterns of values across phenotypes) that characterize groups (subpopulations), and includes a variety of methods for visualization of variance. Visualization supports interdisciplinary collaboration, and enables screening and refinement of hypotheses about sets of phenotypes. With several examples we illustrate how this approach supports “natural selection” on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data.

ViVA was designed for investigation of data concerning the biological bases of traits such as memory and response inhibition phenotypes — to explore whether they can aid in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. The hypotheses and data are increasingly trans-disciplinary and sophisticated, and the impact of better methods can be enormous.