Title Talk: “How do you see if you don’t see”: using randomisation with plots to explore ‘omics data
The volume of high-throughput data makes it a daunting prospect to plot, but relying primarily on false discovery rate adjusted p-values is not enough. Making plots of the data is essential to diagnose the models and understand the results. We will discuss several topics: (1) the idea of visual inference in relation to multiple testing to explore effect size in comparison to statistical significance, (2) multivariate plots for examining within gene dependence, and (3) interactive graphics to visually sift through lists of significant genes reported by tests. The focus is on RNA-Seq data.
Joint work with Michelle Graham, Eric Hare, Michael Lawrence, Stuart Lee, Mahbubul Majumder, Niladri Roy Chowdhury, Lindsay Rutter, Tengfei Yin
Professor Dianne Cook, Business Analytics, Department of Econometrics and Business Statistics, Monash University
Di Cook is a Professor of Business Analytics, in the Department of Econometrics and Business Statistics at Monash University. Di’s research is in data science, data visualisation, exploratory data analysis, data mining, high-dimensional methods and statistical computing. She enjoys engaging in research, working with data, teaching, advising students and developing open source software.
Much of her work has been on developing interactive statistical graphics for high-dimensional data, and the implementation has been in these software packages: xgobi, ggobi, cranvas. The primary methods include tours, projection pursuit, manual controls for tours, pipelines for interactive graphics, a grammar of graphics for biological data, and visualizing boundaries in high-d classifiers. She has also experimented with visualizing data in virtual environments, and found that people do see clusters better in that environment than on a single computer screen.