Interactive Visualization Software Research Articles

Abstract 1201: GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics

Abstract In cancer genomics, integrative analysis of data obtained from biological samples of patient cohorts requires handling large groups of patients, each with molecular data of different type for thousands of genes, such as expression, mutation, copy number, and others. Currently available tools allow visualization and analysis of such complex data, however forcing the user to work with partial views due to the amount of information. Also, these systems usually involve somewhat laborious procedures, so that for a complete analysis of the information it is often necessary to use many tools in sequence. Here we present GenomeCruzer, a software for 3D interactive visualization and analysis of multidimensional omic data sets (e.g. gene expression, methylation, copy number alteration). GenomeCruzer is particularly useful to quickly achieve clinical and biological insight, providing easy integration and interactive visualization of genomic data of any kind. GenomeCruzer has the dual objectives of representing large amounts of genomic data in an easily readable way, allowing interactive analysis and providing visual results in real-time. Powerful graphics also allow simultaneous display of different types of values for the same objects, through multiple dimensions and customizable visual metaphors. The user can navigate this 3D environment and interact with the different elements, which constitute a representation of the starting datasets, building analysis paths and using statistical tools e.g. for the comparison of groups to highlight correlations. In conclusion, GenomeCruzer enables users without bioinformatics background to browse, analyse and interpret complex omics datasets for multiple groups of patients and genes simultaneously, extracting clinical correlates and biological knowledge. Citation Format: Claudio Isella, Riccardo Corsi, Jamal Elhasnaoui, Andrea Bertotti, Luca Vezzadini, Enzo Medico. GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1201.

Abstract 3814: Multiomic characterization of the tumor microenvironment in FFPE tissue by simultaneous protein and gene expression profiling

Abstract The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) staining has been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome-wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell-cell interactions within the TME, multiple layers of information are needed and must be studied together. Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome-wide gene expression and highly multiplexed protein expression profiling from the same formalin-fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E or immunofluorescence stained tissues were then imaged, followed by incubation with transcriptome-wide probes and a high-plex DNA-barcoded antibody panel containing intra- and extracellular markers. Transcriptome probes and antibody-barcodes were then spatially captured on the slide and converted into sequencing-ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E/immunofluorescence, RNA, protein) from the same tissue section. We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune-oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker-guided regional selection and differential gene expression analysis on the defined regions. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development. Citation Format: Cedric Uytingco, Jennifer Chew, Naishitha Anaparthy, Jun D. Chiang, Christina Galonska, Karthik Ganapathy, Ryo Hatori, Alexander Hermes, Layla Katiraee, Anna-Maria Katsori, William Nitsch, Patrick Roelli, Joe Shuga, Rapolas Spalinskas, Mesruh Turkekul, Benton Veire, Dan Walker, Neil Weisenfeld, Stephen R. Williams, Zachary Bent, Marlon Stoeckius. Multiomic characterization of the tumor microenvironment in FFPE tissue by simultaneous protein and gene expression profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3814.

Tumor Microenvironment Characterization using a Spatial Multiomic Assay to Simultaneously Profile Protein and Gene Expression in FFPE Tumors

The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) and immunohistochemistry staining have been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome‐wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell‐cell interactions within the TME, analysis of multiple analytes is necessary.Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome‐wide gene expression and highly multiplexed protein expression profiling from the same formalin‐fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E stained tissues were then imaged, followed by incubation with transcriptome‐wide probes and a high‐plex DNA‐barcoded antibody panel containing intra‐ and extracellular markers. Transcriptome probes and antibody‐barcodes were then spatially captured on the slide and converted into sequencing‐ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E morphology, RNA, protein) from the same tissue section.We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune‐oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker‐guided regional selection and differential gene expression analysis on the defined areas. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development.

Abstract 12220: Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue in CT Calcium Score Images

Introduction: Studies have linked the epicardial adipose tissue (EAT) volume with the risk of adverse cardiovascular (CV) events, suggesting a role for clinical decision making. Manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, thus limiting its routine use in clinical practice. Hypothesis: Deep-learning approach can facilitate EAT quantification on archival coronary artery calcium scoring (CAC) scans. Methods: We developed a deep learning semantic segmentation method using the DeepLab-v3 plus network and applied it to gated, non-contrast cardiac CT calcium score scans. Analysts manually identified the enclosing pericardial sac using interactive visualization software. Our algorithm was trained to segment the image region enclosed by the pericardial sac. To keep the sac-increasing curvature, we segmented the heart into two halves. The lower half was analyzed from bottom-to-middle, while the slices in the upper half were analyzed from top-to-middle. Each 2D slice was associated with two consecutive slices, giving a three-slice image slab. A window/level of 350/40 Hounsfield unit (HU) was applied to axial slices providing a uniform HU-attention-window. A median filter with kernel size 3mm was applied to reduce noise. After deep learning semantic segmentation of the enclosing region, we determined EAT using the standard CT fat window [-190, -30] HU. Results: Using 89 CT scans (50 training/39 testing), our algorithm showed excellent results compared to ground-truth manual labeling. The total average Dice score was (88.52%±3.35) with a high correlation against manually derived EAT (R=98.52%, p-value<0.001). The average Dice score per slice was 91.80% (with R=96.62%, p-value<0.001). The average percent EAT volume error was only 5.74%±5.04. Conclusions: Our developed end-to-end deep learning segmentation approach, using HU-attention-window and slab-of-slices gives accurate EAT volume segmentation. Our results provide an important avenue for automated analysis of EAT and its relationship with CV events in large data sets.

Automation of Healthcare-Associated Infections (HAIs) Areas for Opportunity Through the Use of Technology

Background: A large healthcare system in Georgia implemented an enhanced electronic infection surveillance system that is incorporated into the electronic medical record (EMR). Prior to the implementation of this electronic infection surveillance system, the infection prevention (IP) team performed healthcare-associated infection (HAI) surveillance through a locally created system that did not interface with their EMR. Each HAI identified undergoes a robust analysis for opportunities depending on the infection type by manual abstraction from the EMR, which is stored in a spreadsheet. One disadvantage of this spreadsheet is that only 1 person can enter data at a time. The coronavirus disease 2019 (COVID-19) pandemic has presented many challenges for healthcare facilities including shifting resources from HAI prevention programs. These programs include the investigations performed to identify risk factors that can aid in preventing future infections. Due to the necessity to increase efficiency in the current pandemic, the IP team proposed using technology to automate our HAI investigation process. Method: The IP team and the business intelligence (BI) team met to determine whether data completed in the electronic infection surveillance system could flow into an interactive data visualization software that is currently used for other HAI prevention dashboards. The existing spreadsheet was reviewed to select variables essential for HAI investigations and for which the data existed in the EMR. The BI team worked to find the correct data tables within the EMR so that the data could automatically refresh daily in the data visualization software. Result: The BI team was able to correctly identify variables used in the previously manual HAI investigation process within the EMR to interface with the data visualization software. This automation of investigations allows quick analysis of trends and areas of opportunity to prevent future HAIs. Conclusion: This utilization of technology can be applied to other healthcare facilities with similar software systems to streamline IP workflows. The automation of quickly and efficiently recognizing areas of opportunity allows IPs more time to facilitate the prevention of HAIs in other ways.Funding: NoDisclosures: None

Visual Analytics in Effects of Gross Domestic Product to Human Immunodeficiency Virus Using Tableau

As data becomes more accessible, visualization methods are needed to help make it easier to understand the information. Analyzing and visualizing data makes it easier to understand a dataset without having to read through it, and elucidate connections between two or more different datasets. Tableau is one of the most popular interactive data visualization software. By using Tableau, it is easy to find correlations between datasets, reorganize datasets through pivoting or joining them, and create visualizations such as geo map charts, geo bubble charts, table charts, line charts, pie charts, and treemap charts. This project aims to show the correlation between a country’s gross domestic product (GDP) and human immunodeficiency virus (HIV) through Tableau. Large data sets related to the GDP and HIV were gathered from open data sources. The data will be cleaned through Tableau and Excel, and correlations between datasets will be shown through variable charts with Tableau.

RelPath: an interactive tool to visualize branches of studies and quantify the expertise of authors by citation paths

Networks of scientific publications play a fundamental role in the search for relevant papers to a specific research topic, as well as in finding authors with expertise in different branches of study. Making a selection of the most important references of a specific paper to understand the evolution of a branch of study is an essential task in the academic world. A challenge for the scientific community has been to identify possible authors relevant to a target paper, for example, to select them as potential reviewers. A solution to find potential expert candidates can be based on the network of citations and on how relevant the authors of selecting papers are in relation to the target paper. With the motivation for developing interactive visual software that helps both researchers and editorial committees, in this paper we present RelPath, a system to help with the task of finding relevant papers and authors for a selected paper. RelPath includes the submission paper in a citations network and establishes relevance of the edges in the network, through which it is possible to build branches of studies and establish a ranking of authors. In this work, we introduce an index to quantify the expertise of the authors by citation paths and we propose a collaborative method to ranking references.

Computational Graphics Software for Interactive Docking and Visualization of Ligand-Protein Complementarity.

The Dockeye software is designed to complement automated docking protocols by allowing the user's chemical know-how and experience of what makes for good protein-ligand binding, knowledge that is not easily encoded into automated algorithms, to guide the docking. It allows the interactive manipulation of the ligand placement against a protein target. Real-time intuitively comprehensible feedback about the location, spatial density, and the extent of both favorable and unfavorable atomic interactions between ligand and protein is provided through a carefully designed graphical object. It is also a tool for the graphical analysis of the interactions of known protein-ligand complexes. Comparative docking of 58 protein-ligand complexes with Dockeye and Autodock Vina shows how this software can be used synergistically with automated docking programs to significantly improve the task of discovery of ligand placement.

DEVELOPMENT OF SOFTWARE FOR INTERACTIVE VISUALIZATION OF SYSTEMS AND UNITS OF TRANSPORT VEHICLES

Problem. Constant innovations in technology lead to the development of new methods and paradigms of human interaction with the digital world. An essential requirement that determines the usefulness of visual models for solving specific problems is, in addition to the automated construction of such models, the clarity of the obtained images and the convenience of manipulating their properties. Goal. The purpose of the article is to develop software for interactive visualization of systems and units of transport vehicles. Methodology. One of the promising areas of interactive visualization is the use of augmented reality. With the advent of mass mobile devices that have the necessary characteristics to run augmented reality applications, it has become possible to solve both the main shortcomings of the systems - the lack of mobility and mass distribution. Results. When developing a web application with augmented reality technology, the mathematical and algorithmic bases of augmented reality, tools for developing web applications, developed the structure and description of the web application, developed a database that stores information about models, markers and feedback and user questions. Originality. The peculiarity of this work is the focus on the mass introduction of augmented reality in the fields of learning and design. Mobile devices, tablets and computers, which should only have a camera and an Internet connection, can be used as platforms to apply the results of the study. Practical value. The developed techniques and web application can be used directly in the design of software products for use in the field of transport engineering, business processes, design, as well as in education and other fields.

Visual-X2: interactive visualization and analysis tool for protein crystallization

In high-throughput systems, the crystallization experiments require the inspection and analysis of a large number of trial images. The visualization and analysis tools are needed to view and analyze the experimental results, and recommend novel crystalline conditions by analyzing prior results. It is essential to integrate all these components into a single system. Therefore, we developed Visual-X2, an interactive visualization software developed to aid the user for quick and efficient visualization and analysis of the results of the experiments. Visual-X2 has a number of useful features for visualization and analysis: dual plate view (thumbnail and symbolic), detailed well view with scoring option, multiple-scan and time-course views, support for screening analysis based on multiple screens, three novel screen analysis methods (associative experimental design, GenScreen, and novelty methods), and generating pipetting file with a family of conditions varying concentrations based on stock concentration.

OpenSpace: An open-source astrovisualization framework

OpenSpace (2017; Bock et al. 2017)is an open source interactive data visualization software designed to visualize the entire known universe and portray our ongoing efforts to investigate the cosmos ...

Bringing the web up to speed with WebAssembly

The maturation of the Web platform has given rise to sophisticated and demanding Web applications such as interactive 3D visualization, audio and video software, and games. With that, efficiency and security of code on the Web has become more important than ever. Yet JavaScript as the only built-in language of the Web is not well-equipped to meet these requirements, especially as a compilation target. Engineers from the four major browser vendors have risen to the challenge and collaboratively designed a portable low-level bytecode called WebAssembly. It offers compact representation, efficient validation and compilation, and safe low to no-overhead execution. Rather than committing to a specific programming model, WebAssembly is an abstraction over modern hardware, making it language-, hardware-, and platform-independent, with use cases beyond just the Web. WebAssembly has been designed with a formal semantics from the start. We describe the motivation, design and formal semantics of WebAssembly and provide some preliminary experience with implementations.

Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time interactive simulation and visualization

In this paper, we introduce an interactive coastal wave simulation and visualization software, called Celeris. Celeris is an open source software which needs minimum preparation to run on a Windows machine. The software solves the extended Boussinesq equations using a hybrid finite volume–finite difference method and supports moving shoreline boundaries. The simulation and visualization are performed on the GPU using Direct3D libraries, which enables the software to run faster than real-time. Celeris provides a first-of-its-kind interactive modeling platform for coastal wave applications and it supports simultaneous visualization with both photorealistic and colormapped rendering capabilities. We validate our software through comparison with three standard benchmarks for non-breaking and breaking waves. Program summaryProgram title: CelerisProgram Files doi:http://dx.doi.org/10.17632/5djwvf5x5k.1Licensing provisions : GNU General Public License 3 (GPL)Programming language : C++, HLSLNature of problem : Boussinesq-type models provide the research-level accuracy needed for modeling wave propagation in coastal zones. However the current models, both commercial and open source, do not provide means for real-time computation, nor provide model interactivity and concurrent visualization. In order to achieve a real-time simulation speed in current parallelized models, dozens to hundreds of CPU cores are needed. Celeris is an interactive software which provides faster than real-time simulation and visualization speed on an average user laptop. The novelty of this software is its interactive environment, which allows the user to modify the model and field parameters as the model is running, and to see the effect of these changes immediately.Solution method : A hybrid finite volume-finite difference scheme is used to solve the extended Boussinesq equations. The solver is parallelized using shader programming with Direct3D libraries. Visualization is also performed with the same libraries.

Visual Twitter Analytics (Vista)

Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori. Originality/value – Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.

3D model of pterygium and corneal limbus: Investigating histopathology and stem cell distribution

PurposeThis study aims to create a complete histological 3D computer model of pterygium in situ, mapping the anatomy of the disease tissue, its relation to the corneal limbus and the distribution of limbal stem cells.MethodsOne human eye affected with pterygium was obtained from a cornea donor post mortem. The anterior part of the eye was cut into 900 consecutive horizontal sections. Every other section was stained with HE to be digitized, aligned and 3D reconstructed using interactive 3D visualization software. Immunohistochemistry targeting CK19, MMP‐1, p63 and VEGF was performed on the remaining sections alternating across the structure so as to create evenly distributed overlaying models.ResultsUsing the sections a high‐resolution model of the pterygium and limbus was created, and in aligning the immunostained sections to the model, a spatial map of the staining was created. Analyzsing the model we found a mostly normal temporal limbus with intact architecture, however nasally the limbus was found to be buried under the pterygial mass and only partly intact, showing a number of pathological changes.ConclusionsThe limbal degeneration underneath the pterygium appears to be a precondition for a consequence of the pterygial growth.

3D model of pterygium and corneal limbus: Investigating histopathology and stem cell distribution

PurposeThis study aims to create a complete histological 3D computer model of pterygium in situ, mapping the anatomy of the disease tissue, its relation to the corneal limbus and the distribution of limbal stem cells.MethodsOne human eye affected with pterygium was obtained from a cornea donor post mortem. The anterior part of the eye was cut into 900 consecutive horizontal sections. Every other section was stained with HE to be digitized, aligned and 3D reconstructed using interactive 3D visualization software. Immunohistochemistry targeting CK19, MMP‐1, p63 and VEGF was performed on the remaining sections alternating across the structure so as to create evenly distributed overlaying models.ResultsUsing the sections a high‐resolution model of the pterygium and limbus was created, and in aligning the immunostained sections to the model, a spatial map of the staining was created.Analyzsing the model we found a mostly normal temporal limbus with intact architecture, however nasally the limbus was found to be buried under the pterygial mass and only partly intact, showing a number of pathological changes.ConclusionsThe limbal degeneration underneath the pterygium appears to be a precondition for or a consequence of the pterygial growth.

Airborne single particle mass spectrometers (SPLAT II & miniSPLAT) and new software for data visualization and analysis in a geo-spatial context.

Understanding the effect of aerosols on climate requires knowledge of the size and chemical composition of individual aerosol particles-two fundamental properties that determine an aerosol's optical properties and ability to serve as cloud condensation or ice nuclei. Here we present our aircraft-compatible single particle mass spectrometers, SPLAT II and its new, miniaturized version, miniSPLAT that measure in-situ and in real-time the size and chemical composition of individual aerosol particles with extremely high sensitivity, temporal resolution, and sizing precision on the order of a monolayer. Although miniSPLAT's size, weight, and power consumption are significantly smaller, its performance is on par with SPLAT II. Both instruments operate in dual data acquisition mode to measure, in addition to single particle size and composition, particle number concentrations, size distributions, density, and asphericity with high temporal resolution. We also present ND-Scope, our newly developed interactive visual analytics software package. ND-Scope is designed to explore and visualize the vast amount of complex, multidimensional data acquired by our single particle mass spectrometers, along with other aerosol and cloud characterization instruments on-board aircraft. We demonstrate that ND-Scope makes it possible to visualize the relationships between different observables and to view the data in a geo-spatial context, using the interactive and fully coupled Google Earth and Parallel Coordinates displays. Here we illustrate the utility of ND-Scope to visualize the spatial distribution of atmospheric particles of different compositions, and explore the relationship between individual particle compositions and their activity as cloud condensation nuclei.

Development of In-Situ Visualization Tool for PIC Simulation

As the capability of a supercomputer is improved, the sizes of simulation and its output data also become larger and larger. Visualization is usually carried out on a researcher’s PC with interactive visualization software after performing the computer simulation. However, the data size is becoming too large to do it currently. A promising answer is in-situ visualization. For this case a simulation code is coupled with the visualization code and visualization is performed with the simulation on the same supercomputer. We developed an in-situ visualization tool for particle-in-cell (PIC) simulation and it is provided as a Fortran’s module. We coupled it with a PIC simulation code and tested the coupled code on Plasma Simulator supercomputer, and ensured that it works.

Development of a Virtual 3d Model of Denisova Cave in the Altai Mountains

The study addresses the implementation of the project “A Virtual Model of Denisova Cave in the Altai Mountains” generating a 3D model of the cave and pinpointing certain key finds. The project includes the elaboration of software for interactive visualization in the virtual space of the cave. Apart from information about this unique site, the model is aimed at solving certain research problems. This is the first Russian attempt to create a virtual 3D model of a cave site.

Sequence Bundles: a novel method for visualising, discovering and exploring sequence motifs.

BackgroundWe introduce Sequence Bundles--a novel data visualisation method for representing multiple sequence alignments (MSAs). We identify and address key limitations of the existing bioinformatics data visualisation methods (i.e. the Sequence Logo) by enabling Sequence Bundles to give salient visual expression to sequence motifs and other data features, which would otherwise remain hidden.MethodsFor the development of Sequence Bundles we employed research-led information design methodologies. Sequences are encoded as uninterrupted, semi-opaque lines plotted on a 2-dimensional reconfigurable grid. Each line represents a single sequence. The thickness and opacity of the stack at each residue in each position indicates the level of conservation and the lines' curved paths expose patterns in correlation and functionality. Several MSAs can be visualised in a composite image. The Sequence Bundles method is designed to favour a tangible, continuous and intuitive display of information.ResultsWe have developed a software demonstration application for generating a Sequence Bundles visualisation of MSAs provided for the BioVis 2013 redesign contest. A subsequent exploration of the visualised line patterns allowed for the discovery of a number of interesting features in the dataset. Reported features include the extreme conservation of sequences displaying a specific residue and bifurcations of the consensus sequence.ConclusionsSequence Bundles is a novel method for visualisation of MSAs and the discovery of sequence motifs. It can aid in generating new insight and hypothesis making. Sequence Bundles is well disposed for future implementation as an interactive visual analytics software, which can complement existing visualisation tools.