Jinwook Seo

Interactively exploring hierarchical clustering results [gene identification]

To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts.

A rank-by-feature framework for interactive exploration of multidimensional data

Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems often are a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. We offer a set of principles and a novel rank-by-feature framework that could enable users to better understand distributions in one (1D) or two dimensions (2D), and then discover relationships, clusters, gaps, outliers, and other features. Users of our framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods users can systematically examine the most important 1D and 2D axis-parallel projections. We summarize our Graphics, Ranking, and Interaction for Discovery (GRID) principles as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. We implemented the rank-by-feature framework in the Hierarchical Clustering Explorer, but the same data exploration principles could enable users to organize their discovery process so as to produce more thorough analyses and extract deeper insights in any multidimensional data application, such as spreadsheets, statistical packages, or information visualization tools.

A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections

Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features

Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework

Knowledge discovery in high-dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our three-year effort to develop versions of the hierarchical clustering explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the hierarchical clustering explorer (HCE) using three case studies and an e-mail user survey (n=57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods

Interactively Exploring Hierarchical Clustering Results

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.

Visualizing set concordance with permutation matrices and fan diagrams

Scientific problem solving often involves concordance (or discordance) analysis among the result sets from different approaches. For example, different scientific analysis methods with the same samples often lead to different or even conflicting conclusions. To reach a more judicious conclusion, it is crucial to consider different perspectives by checking concordance among those result sets by different methods. In this paper, we present an interactive visualization tool called ConSet, where users can effectively examine relationships among multiple sets at once. ConSet provides an overview using an improved permutation matrix to enable users to easily identify relationships among sets with a large number of elements. Not only do we use a standard Venn diagram, we also introduce a new diagram called Fan diagram that allows users to compare two or three sets without any inconsistencies that may exist in Venn diagrams. A qualitative user study was conducted to evaluate how our tool works in comparison with a traditional set visualization tool based on a Venn diagram. We observed that ConSet enabled users to complete more tasks with fewer errors than the traditional interface did and most users preferred ConSet.

Automatic Extraction of Inferior Alveolar Nerve Canal Using Feature-Enhancing Panoramic Volume Rendering

Dental implant surgery, which involves the surgical insertion of a dental implant into the jawbone as an artificial root, has become one of the most successful applications of computed tomography (CT) in dental implantology. For successful implant surgery, it is essential to identify vital anatomic structures such as the inferior alveolar nerve (IAN), which should be avoided during the surgical procedure. Due to the ambiguity of its structure, the IAN is very elusive to extract in dental CT images. As a result, the IAN canal is typically identified in most previous studies. This paper presents a novel method of automatically extracting the IAN canal. Mental and mandibular foramens, which are regarded as the ends of the IAN canal in the mandible, are detected automatically using 3-D panoramic volume rendering (VR) and texture analysis techniques. In the 3-D panoramic VR, novel color shading and compositing methods are proposed to emphasize the foramens and isolate them from other fine structures. Subsequently, the path of the IAN canal is computed using a line-tracking algorithm. Finally, the IAN canal is extracted by expanding the region of the path using a fast marching method with a new speed function exploiting the anatomical information about the canal radius. In experimental results using ten clinical datasets, the proposed method identified the IAN canal accurately, demonstrating that this approach assists dentists substantially during dental implant surgery.

miRTarVis: an interactive visual analysis tool for microRNA-mRNA expression profile data

BackgroundMicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Recently, other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. Some web-based tools are also introduced to help researchers predict targets of miRNAs from miRNA-mRNA expression profile data. A demand for a miRNA-mRNA visual analysis tool that features novel miRNA prediction algorithms and more interactive visualization techniques exists.ResultsWe designed and implemented miRTarVis, which is an interactive visual analysis tool that predicts targets of miRNAs from miRNA-mRNA expression profile data and visualizes the resulting miRNA-target interaction network. miRTarVis has intuitive interface design in accordance with the analysis procedure of load, filter, predict, and visualize. It predicts targets of miRNA by adopting Bayesian inference and MINE analyses, as well as conventional correlation and mutual information analyses. It visualizes a resulting miRNA-mRNA network in an interactive Treemap, as well as a conventional node-link diagram. miRTarVis is available at http://hcil.snu.ac.kr/~rati/miRTarVis/index.html.ConclusionsWe reported findings from miRNA-mRNA expression profile data of asthma patients using miRTarVis in a case study. miRTarVis helps to predict and understand targets of miRNA from miRNA-mRNA expression profile data.

Exploratory Data Analysis With Categorical Variables: An Improved Rank-by-Feature Framework and a Case Study

Multidimensional data sets often include categorical information. When most dimensions have categorical information, clustering the data set as a whole can reveal interesting patterns in the data set. However, the categorical information is often more useful as a way to partition the data set: gene expression data for healthy versus diseased samples or stock performance for common, preferred, or convertible shares. We present novel ways to utilize categorical information in exploratory data analysis by enhancing the rank-by-feature framework. First, we present ranking criteria for categorical variables and ways to improve the score overview. Second, we present a novel way to utilize the categorical information together with clustering algorithms. Users can partition the data set according to categorical information vertically or horizontally, and the clustering result for each partition can serve as new categorical information. We report the results of a longitudinal case study with a biomedical research team, including insights gained and potential future work.

Fast High-Quality Volume Ray Casting with Virtual Samplings

Volume ray-casting with a higher order reconstruction filter and/or a higher sampling rate has been adopted in direct volume rendering frameworks to provide a smooth reconstruction of the volume scalar and/or to reduce artifacts when the combined frequency of the volume and transfer function is high. While it enables high-quality volume rendering, it cannot support interactive rendering due to its high computational cost. In this paper, we propose a fast high-quality volume ray-casting algorithm which effectively increases the sampling rate. While a ray traverses the volume, intensity values are uniformly reconstructed using a high-order convolution filter. Additional samplings, referred to as virtual samplings, are carried out within a ray segment from a cubic spline curve interpolating those uniformly reconstructed intensities. These virtual samplings are performed by evaluating the polynomial function of the cubic spline curve via simple arithmetic operations. The min max blocks are refined accordingly for accurate empty space skipping in the proposed method. Experimental results demonstrate that the proposed algorithm, also exploiting fast cubic texture filtering supported by programmable GPUs, offers renderings as good as a conventional ray-casting algorithm using high-order reconstruction filtering at the same sampling rate, while delivering 2.5x to 3.3x rendering speed-up.