Visualization of the Phosphoproteomic Data from AfCS with the Google Motion Chart Gadget

Results from multivariate molecular biological experiments become increasingly complex. Hence, the challenge of projecting high-dimensional data onto few dimensions for effective data visualization is becoming increasingly important in Systems Biology. Effective data visualization can summarize the activity of many variables over time as well as display relationships between variables. Dynamic interactive visualization tools can provide scientists with ways of visually identifying relationship and patterns, and improve communication of results on the web and in presentations. For this, interactive systems with animation have great potential since they add dimensions to static images limited to two dimensions. Interactivity and animation is particularly useful for showing time-series trends in multi-dimensional data. The Flash-based Motion Chart Google Gadget available through GoogleDocs is a recent advance in multi-dimensional data visualization. The Motion Chart Gadget is a component of the Trendalyzer software, which was developed for web-based animation of statistical results. Here we demonstrate the use of this Gadget to visualize molecular biological data, the phosphoproteomics results published on the Data Center of the Signaling Gateway web-site

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

A visual language to characterise transitions in narrative visualization

We use a taxonomy of panel-to-panel transitions in comics, refined the definition of its components to reflect the nature of data-stories in information visualization, and then, use the taxonomy in coding a number of VAST challenges videos from the last four years. We represent the use of transitions in each video graphically with a diagram that shows how the information was added incrementally in order to tell a story that answers a particular question. A number of issues have been taken into account when coding transitions in each video as well as in designing and creating the visual diagram such as, nested transitions, the use of sub-topics, and delayed transitions

Functional Data Analysis in Electronic Commerce Research

This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Teaching Data Science

We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language with an emphasis on data preparation, processing, and presentation. The course had no prerequisites, and students were not expected to have any programming experience. This introductory course was designed to cover a wide range of topics, from the nature of data, to storage, to visualization, to probability and statistical analysis, to cloud and high performance computing, without becoming overly focused on any one subject. We conclude this article with a discussion of lessons learned and our plans to develop new data science courses.Comment: 10 pages, 4 figures, International Conference on Computational Science (ICCS 2016