Karsten Klein

3D-stereoscopic immersive analytics projects at Monash University and University of Konstanz

Immersive Analytics investigates how novel interaction and display technologies may support analytical reasoning and decision making. The Immersive Analytics initiative of Monash University started early 2014. Over the last few years, a number of projects have been developed or extended in this context to meet the requirements of semi- or full-immersive stereoscopic environments. Different technologies are used for this purpose: CAVE2™ (a 330 degree large-scale visualization environment which can be used for educative and scientific group presentations, analyses and discussions), stereoscopic Powerwalls (miniCAVEs, representing a segment of the CAVE2 and used for development and communication), Fishtanks, and/or HMDs (such as Oculus, VIVE, and mobile HMD approaches). Apart from CAVE2™ all systems are or will be employed on both the Monash University and the University of Konstanz side, especially to investigate collaborative Immersive Analytics. In addition, sensiLab extends most of the previous approaches by involving all senses, 3D visualization is combined with multi-sensory feedback, 3D printing, robotics in a scientific-artistic-creative environment.

Scaffold Hunter: a comprehensive visual analytics framework for drug discovery

The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold Hunter is a flexible visual analytics framework for the analysis of chemical compound data and combines techniques from several fields such as data mining and information visualization. The framework allows analyzing high-dimensional chemical compound data in an interactive fashion, combining intuitive visualizations with automated analysis methods including versatile clustering methods. Originally designed to analyze the scaffold tree, Scaffold Hunter is continuously revised and extended. We describe recent extensions that significantly increase the applicability for a variety of tasks.

Immersive Analytics (Dagstuhl Seminar 16231)

This report documents the program and the outcomes of Dagstuhl Seminar 16231 Immersive Analytics. Close to 40 researchers and practitioners participated in this seminar to discuss and define the field of Immersive Analytics, to create a community around it, and to identify its research challenges. As the participants had a diverse background in a variety of disciplines, including Human-Computer-Interaction, Augmented and Virtual Reality, Information Visualization, and Visual Analytics, the seminar featured a couple of survey talks on the first days, followed by plenary and working group discussions that were meant to shape the field of Immerswive Analytics. As an outcome, a book publication is planned with book chapters provided by the participants.

3D Modelling and Visualisation of Heterogeneous Cell Membranes in Blender

Chlamydomonas reinhardtii cells have been in the focus of research for more than a decade, in particular due to its use as alternative source for energy production. However, the molecular processes in these cells are still not completely known, and 3D visualisations may help to understand these complex interactions and processes. In previous work, we presented the stereoscopic 3D (S3D) visualisation of a complete Chlamydomonas reinhardtii cell created with the 3D modelling framework Blender. This animation contained already a scene showing an illustrative membrane model of the thylakoid membrane. During discussion with domain experts, shortcomings of the visualisation for several detailed analysis questions have been identified and it was decided to redefine it. A new modelling and visualisation pipeline based on a Membrane Packing Algorithm was developed, which can be configured via a user interface, enabling the composition of membranes employing published material. An expert user study was conducted to evaluate this new approach, with half the participants having a biology and the other half having an informatics background. The new and old Chlamydomonas thylakoid membrane models were presented on a S3D back projection system. The evaluation results reveal that the majority of participants preferred the new, more realistic membrane visualisation. However, the opinion varied with the expertise, leading to valuable conclusions for future visualisations. Interestingly, the S3D presentation of molecular structures lead to a positive change in opinion regarding S3D technology.

Visualisation and analysis of RNA-Seq assembly graphs

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging. Here we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

Immersive Analytics Applications in Life and Health Sciences

Life and health sciences are key application areas for immersive analytics. This spans a broad range including medicine (e.g., investigations in tumour boards), pharmacology (e.g., research of adverse drug reactions), biology (e.g., immersive virtual cells) and ecology (e.g., analytics of animal behaviour). We present a brief overview of general applications of immersive analytics in the life and health sciences, and present a number of applications in detail, such as immersive analytics in structural biology, in medical image analytics, in neurosciences, in epidemiology, in biological network analysis and for virtual cells.

Immersive Analytics: An Introduction

Immersive Analytics is a new research initiative that aims to remove barriers between people, their data and the tools they use for analysis and decision making. Here we clarify the aims of immersive analytics research, its opportunities and historical context, as well as providing a broad research agenda for the field. In addition, we review how the term immersion has been used to refer to both technological and psychological immersion, both of which are central to immersive analytics research.

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer’s disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

A Note on the Practicality of Maximal Planar Subgraph Algorithms

Given a graph G, the NP-hard Maximum Planar Subgraph problem (MPS) asks for a planar subgraph of G with the maximum number of edges. There are several heuristic, approximative, and exact algorithms to tackle the problem, but—to the best of our knowledge—they have never been compared competitively in practice.