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

explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI) on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots.

MetNet: Systems Biology Tools for Arabidopsis

MetNet (http://metnetdb.org) is an emerging open-source software platform for exploration of disparate experimental data types and regulatory and metabolic networks in the context of Arabidopsis systems biology. The MetNet platform features graph visualization, interactive displays, graph theoretic computations for determining biological distances, a unique multivariate display and statistical analysis tool, graph modeling using the open source statistical analysis language, R, and versatile text mining. The use of these tools is illustrated with data from the bio1 mutant of Arabidopsis

Exploring Causal Influences

Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques

explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI) on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots

Network Coincidence Analysis: The netCoin R Package

The aim of the R package netCoin is to explore data structures using a number of statistical techniques that share the handling of interdependent variables. The main objective of this analysis is to detect events, characters, objects, attributes or characteristics that tend to appear together within a given set of scenarios. Its most notable feature is the combination of traditional multivariate statistical analysis and network analysis supported by topological graph theory. In addition, netCoin produces HTML graphs using the D3.js visualization library to support the interactive exploration of networked data. Among its many applications, netCoin can be used for the analysis of multiple responses in questionnaires to explore relevant associations, for the development of textual networks, for the study of ecological communities, for audience analysis, for mining large databases or for basket market analysis

Visual and interactive exploration of point data

Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data

Interactive analysis of high-dimensional association structures with graphical models

Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models which is not only planned to automatically carry out the model search but also to visualize the corresponding graph at each stage of the model fit on request by the user. It additionally allows to modify the graph and the model fit interactively