Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
@cite_37: GAP is a Java-designed exploratory data analysis (EDA) software for matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects. Various matrix permutation algorithms and clustering methods with validation indices are implemented for extracting embedded information. GAP has a friendly graphical user interface for easy handling of data and proximity matrices. It is more powerful and effective than conventional graphical methods when dimension reduction techniques fail or when data is of ordinal, binary, and nominal type.
@cite_25: Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data
@cite_32: The Hierarchical Clustering Explorer provides a dendrogram and color mosaic linked to two-dimensional scattergrams, a variety of visualization options, and dynamic query controls for use in genomic microarray data analysis.
@cite_49: Summary: PermutMatrix is a work space designed to graphically explore gene expression data. It relies on the graphical approach introduced by Eisen and also offers several methods for the optimal reorganization of rows and columns of a numerical dataset. For example, several methods are proposed for optimal reorganization of the leaves of a hierarchical clustering tree, along with several seriation or unidimensional scaling methods that do not require any preliminary hierarchical clustering. This program, developed for MS Windows, with MS-Visual C++, has a clear and efficient graphical interface. Large datasets can be thoroughly and quickly analyzed. Availability: http: www.lirmm.fr caraux PermutMatrix Contact: [email protected]
Both Matchmaker and VisBricks focus on numerical matrices and facilitate the comparison of different groupings or clusterings. Other techniques, such as the hierarchical cluster explorer @cite_32 , @cite_37 , @cite_49 , and @cite_25 , focus on visualizing clustered matrices. Taggle, in contrast, supports both numerical matrices and heterogeneous tables in a single visualization.