Visual Analytics Tool Research Articles

STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies

Gene expression profiling has helped tremendously in the understanding of biological processes and diseases. However, interpreting processed data to gain insights into biological mechanisms remain challenging, especially to the non-bioinformaticians, as many of these data visualization and pathway analysis tools require extensive data formatting. To circumvent these challenges, we developed STAGEs (Static and Temporal Analysis of Gene Expression studies) that provides an interactive visualisation of omics analysis outputs. Users can directly upload data created from Excel spreadsheets and use STAGEs to render volcano plots, differentially expressed genes stacked bar charts, pathway enrichment analysis by Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or customized gene sets, clustergrams and correlation matrices. Moreover, STAGEs takes care of Excel gene to date misconversions, ensuring that every gene is considered for pathway analysis. Output data tables and graphs can be exported, and users can easily customize individual graphs using widgets such as sliders, drop-down menus, text boxes and radio buttons. Collectively, STAGEs is an integrative platform for data analysis, data visualisation and pathway analysis, and is freely available at https://kuanrongchan-stages-stages-vpgh46.streamlitapp.com/. In addition, developers can customise or modify the web tool locally based on our existing codes, which is publicly available at https://github.com/kuanrongchan/STAGES.

A Visual Analytic Tool (VIADS) to Assist the Hypothesis Generation Process in Clinical Research: Mixed Methods Usability Study

Visualization can be a powerful tool to comprehend data sets, especially when they can be represented via hierarchical structures. Enhanced comprehension can facilitate the development of scientific hypotheses. However, the inclusion of excessive data can make visualizations overwhelming. We developed a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS). In this study, we evaluated the usability of VIADS for visualizing data sets of patient diagnoses and procedures coded in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). We used mixed methods in the study. A group of 12 clinical researchers participated in the generation of data-driven hypotheses using the same data sets and time frame (a 1-hour training session and a 2-hour study session) utilizing VIADS via the think-aloud protocol. The audio and screen activities were recorded remotely. A modified version of the System Usability Scale (SUS) survey and a brief survey with open-ended questions were administered after the study to assess the usability of VIADS and verify their intense usage experience with VIADS. The range of SUS scores was 37.5 to 87.5. The mean SUS score for VIADS was 71.88 (out of a possible 100, SD 14.62), and the median SUS was 75. The participants unanimously agreed that VIADS offers new perspectives on data sets (12/12, 100%), while 75% (8/12) agreed that VIADS facilitates understanding, presentation, and interpretation of underlying data sets. The comments on the utility of VIADS were positive and aligned well with the design objectives of VIADS. The answers to the open-ended questions in the modified SUS provided specific suggestions regarding potential improvements for VIADS, and the identified problems with usability were used to update the tool. This usability study demonstrates that VIADS is a usable tool for analyzing secondary data sets with good average usability, good SUS score, and favorable utility. Currently, VIADS accepts data sets with hierarchical codes and their corresponding frequencies. Consequently, only specific types of use cases are supported by the analytical results. Participants agreed, however, that VIADS provides new perspectives on data sets and is relatively easy to use. The VIADS functionalities most appreciated by participants were the ability to filter, summarize, compare, and visualize data. RR2-10.2196/39414.

Metabolite Analysis of Alternaria Mycotoxins by LC-MS/MS and Multiple Tools

Alternaria fungi are widely distributed plant pathogens that invade crop products, causing significant economic damage. In addition, toxic secondary metabolites produced by the fungi can also endanger consumers. Many of these secondary metabolites are chemically characterized as mycotoxins. In this study, Q Exactive Orbitrap mass spectrometry was used for the non-targeted analysis of the metabolome of seven Alternaria isolates cultured on Potato Carrot Agar (PCA), Potato Dextrose Agar (PDA) and Potato Sucrose Agar (PSA) medium. Due to the difficulty of detecting modified toxins, an analytical strategy with multiple visual analysis tools was also used to determine the presence of sulfate conjugated toxins, as well as to visualize the molecular network of Alternaria toxins. The results show that PSA medium exhibits more advantageous properties for the culture of Alternaria, with more toxigenic species and quantities and more obvious metabolic pathways. Based on high-resolution tandem mass spectrometry (MS/MS) data, the mycotoxins and their metabolites were mainly clustered into four groups: alternariol (AOH)/alternariol monomethyl ether (AME)/altenusin (ALU)/altenuene (ALT)/dehydroaltenusin (DHA)/Desmethyldehydroaltenusin (DMDA) families, Altertoxin-I (ATX-I) family, tentoxin (TEN) family and tenuazonic acid (TeA) family. Moreover, the PSA medium is more suitable for the accumulation of AOH, AME, ALU, ALT, DHA and DMDA, while the PDA medium is more suitable for the accumulation of ATX-I, TEN and TeA. This research may provide theoretical support for the metabolomics study of Alternaria.

CoolTeD: A tool for co-labeling and visual analysis of textual dataset

High-quality labeled textual data are reported as an important type of research data in data-driven requirements engineering (RE), especially in automatic mining and analysis of massive textual data produced by software systems. Several tools have been designed to facilitate manual labeling of textual data at different levels of granularity. However, these tools neither aim to provide visualized statistics and analysis of labeled textual data, nor support collaboration among the coders to reduce the time cost in manual labeling and enhance the quality of labeling results. Besides, these tools seldom explicitly serve RE researchers. In this paper, we developed a Web-based labeling tool named CoolTeD (available at http://williamsriver.cn) for collaborative labeling of the textual datasets for RE purposes. Specifically, CoolTeD can be used to: (1) label textual data with the tag category based on ISO 25010 or other user-defined tag categories in a collaborative way; (2) review the labeling results with different confidence levels and contradictory labels, (3) identify contradictory labels and disagreements online; (4) automatically calculate the Cohen's Kappa coefficient of multiple coders, and (5) visualize the labeling results. The tool demo is available at https://youtu.be/KTVrLLenvLE.

Network-based Pharmacology and In vitro Validation Reveal that Galangin Induces Apoptosis in Bladder Cancer Cells by Promoting the P53 Signaling Pathway.

Galangin is one of the flavonoids in Alpinia officinarum. It has various anti-tumor activities, but its anti-bladder cancer effect is unclear. To investigate the mechanism of action of galangin against bladder cancer using a network pharmacology approach. The TCM Systematic Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction database, and the Targetnet database were used to predict the targets of action of galangin. Bladder cancer-related targets were obtained through the GeneCards database. The intersection of the two was taken as the target of galangin's action against bladder cancer. The intersecting targets were screened for core targets using the STRING database and Cytoscape 3.9.0 software to build a protein-protein interaction (PPI) network of targets. The core targets were subjected to gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the online annotation and visual integration analysis tool DAVIDBioinformaticsResources (2021Update). A drug-disease-target-pathway network was constructed using Cytoscape 3.9.0 software. The antibladder cancer effect of galangin was observed by cell proliferation, and plate cloning assay; apoptosis of bladder cancer cells induced by galangin was detected by Hoechst33342 staining and flow cytometry; protein immunoblotting (Western-blot) was used to detect the effect of galangin on apoptosis-related proteins Bax, Bcl-2, Cleaved-PARP, p53 signaling pathway p53 and cytc. A total of 115 genes were obtained from galangin against bladder cancer, and 16 core targets were screened. The kEGG pathway enrichment analysis included Pathways in cancer, PI3K-AKT signaling pathway, p53 signaling pathway, etc. In vitro experiments showed that galangin could inhibit bladder cancer cell proliferation, induce apoptosis, upregulate the expression of apoptosis-related proteins Bax and Cleaved-PARP and downregulate the expression of Bcl-2; meanwhile, galangin could promote the upregulation of the expression of p53 and cytc proteins by activating the p53 signaling pathway. Galangin induced apoptosis in bladder cancer cells by activating the p53 signaling pathway.

JOKOWI'S EMOTIONS ON INSTAGRAM FOLLOWING INDONESIA'S CANCELLATION AS U-20 WORLD CUP HOST

The cancellation of Indonesia as the host of the U-20 World Cup grabbed massive public attention. FIFA canceled due to rejection from various groups such as Bali Governor I Wayan Koster, Central Java Governor Ganjar Pranowo, several parties and mass organizations. President Jokowi also responded to this, starting from affirming that it is necessary to separate politics and sports, to expressing his enthusiasm after the cancellation by FIFA. This research seeks to find out what kind of emotions President Jokowi is trying to display on Instagram social media. Emotions and political communication have a close relationship, as both influence each other and shape public views on political issues. This qualitative research uses AWS Rekognition as a machine learning-based visual analysis tool to determine the emotions displayed. The result of the research is that Jokowi shows a calm emotion. One of the logical assumptions for showing calm emotions is to maintain stability and show that the President is in control of the situation.

The personalized cancer network explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

BackgroundPersonalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process.ResultsThe Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

Progress and prospects of international carbon peaking and carbon neutral research –based on bibliometric analysis (1991–2022)

Carbon peak and carbon neutrality is an important strategic measure to tackle climate change and is of great significance to global climate governance, human development, and scientific and technological progress. In this paper, we used 3,824 papers in the core collection of Web of Science from 1991 to 2022 as a sample, and used bibliometric analysis and CiteSpace, a visual knowledge network analysis tool, to sort out the current status of international carbon peak and carbon neutrality research in terms of publication trends, temporal distribution, spatial distribution, keyword co-occurrence mapping, and research basis. And then the current hot topics of research are explored,including carbon neutralization technology, climate policy impact and performance evaluation, carbon pricing and carbon finance, citizen participation attitude and behavior, and climate governance and global cooperation. Research prospects have been presented in terms of four aspects: building the integrated research framework of carbon peak and carbon neutral research, establishing a comprehensive and efficient environmental policy system, expanding research cooperation, deepening research and application of carbon peak carbon-neutral technology.

Research on Integrated Labor Education in Primary and Secondary Schools Based on CiteSpace Analysis of CNKI Database

The research uses CiteSpace knowledge visualization analysis tool to conduct an in-depth analysis of 63 academic papers on comprehensive labor education in primary and secondary schools published on the China Knowledge Network (CNKI) from 2016 to 2023. The research results show that in the past eight years, research in this field has mainly focused on core issues such as "integration", "labor education system", "combination of production, learning and research", "compulsory courses", and "overall construction". It is suggested that the integration of labor education in primary and secondary schools should strengthen the construction of goal system, clarify the content requirements , establish a guarantee system , and improve the evaluation system.

Autoimmune diseases and gut microbiota: a bibliometric and visual analysis from 2004 to 2022.

Many studies have shown that gut microbiota is closely related to autoimmune diseases (ADs). Studies on gut microbiota and ADs have also increased significantly, but no bibliometric analysis has summarized the association between gut microbiota and ADs. This study aimed to conduct a bibliometric and visual analysis of published studies on gut microbiota and ADs. Based on the Web of Science Core Collection SCI-expanded database, we utilize Excel 2019 and visualization analysis tools VOSviewer and co-occurrence13.2 (COOC13.2) for analysis. A total of 2516 related kinds of literature were included, and the number of papers presented an overall increasing trend. The country/region with the most publications is the USA, the institution is the Harvard Medical School, and the author is Mikael Knip from the USA. Hot research areas include intestinal regulation (such as dysbiosis, short chain fatty acids, and probiotics), multisystem ADs (such as multiple sclerosis, rheumatoid arthritis, and inflammatory bowel disease), and immune-related cells (such as T cells, and dendritic cells). Psoriasis, dysbiosis, autoimmune liver disease, and fecal microbiota transplantation may be the future research direction. Our research results can help researchers grasp the current status of ADs and gut microbiota research and find new research directions in the future.

LaSCA: A Visualization Analysis Tool for Microstructure of Complex Systems

Over the past few decades, plenty of visualization software for the structural analysis of disordered/complex systems has been developed, but the uniqueness and correctness of structural quantification for such systems are still challenging. This paper introduces a visualization analysis tool based on the largest standard cluster analysis (LaSCA), which satisfies the three essential requirements for general structural analysis: physical correctness, objective identification, and injective representation. The specific functionalities of LaSCA include the directed graph model of complex systems, novel structural parameters, topologically close-packed structures, arbitrary partial pair distribution functions, the identification of long-range ordered structures, the adaptive selection of graphical elements, the tracking display of atom ID, user-defined view angles, various options for atom selection, and so on. The program is efficiently based on OpenGL hardware acceleration, employing special algorithms to treat bonds as cylinders or lines and treat atoms as spheres, icosahedrons, tetrahedrons, or points. LaSCA can process more than 1.2 million atoms within 50 s on a PC with 1 GB memory and four cores (Intel Core i7-9700). It is robust and low-cost for surveying short-, medium-, and long-range ordered structures and tracking their evolutions.

Cistrome Explorer: an interactive visual analysis tool for large-scale epigenomic data.

The regulation of genes by cis-regulatory elements (CREs) is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights. We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of CREs across cell types and the identification of transcription factor binding underlying these patterns. Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/. Supplementary data are available at Bioinformatics online.

VisRuler: Visual analytics for extracting decision rules from bagged and boosted decision trees

Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.

CPViz: Visualizing clinical pathways represented in higher-order networks

To improve clinical care practice, it is important to understand the variability of clinical pathways executed in different contexts (e.g., pathways in different geographical locations, demographics, and phenotypic groups). A common way of representing clinical pathways is through network-based representations that capture trajectories of treatment steps. However, first-order networks, which are based on the Markovian property and the de facto standard model to represent transitions between steps, often fail to capture real trajectories. This paper introduces a visual analytic tool to explore and compare pathways represented in higher-order networks. Because each higher node in the network is a subtrajectory (i.e., partial or full history of treatment steps), the tool can display true sequences of treatment steps and compute the similarity of the two networks in a space of higher-order nodes. The tool also highlights areas in which the two networks are similar and dissimilar and how a certain subtrajectory is realized differently in different pathways. The paper demonstrates the tool's usefulness by applying it to multiple antidepressant pharmacotherapy pathways for veterans diagnosed with major depressive disorder and by illustrating heterogeneity in prescription patterns across pathways.

Visualizing and monitoring the process of injection molding

In injection molding machines the molds are rarely equipped with sensor systems. The availability of non-invasive ultrasound-based in-mold sensors provides better means for guiding operators of injection molding machines throughout the production process. However, existing visualizations are mostly limited to plots of temperature and pressure over time. In this work, we present the result of a design study created in collaboration with domain experts. The resulting prototypical application uses real-world data taken from live ultrasound sensor measurements for injection molding cavities captured over multiple cycles during the injection process. Our contribution includes a definition of tasks for setting up and monitoring the machines during the process, and the corresponding web-based visual analysis tool addressing these tasks. The interface consists of a multi-view display with various levels of data aggregation that is updated live for newly streamed data of ongoing injection cycles.

EVM: Incorporating Model Checking into Exploratory Visual Analysis.

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data-e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools.

Visualizing Historical Book Trade Data: An Iterative Design Study with Close Collaboration with Domain Experts.

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework [46] was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way.

A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID‐19 Pandemic

Visual analytics tools for spatiotemporal analysis can be used to manage and monitor the propagation of an epidemic. The problem is that dashboards encountered in the literature do not take into consideration how the geolocation characteristics, such as socioeconomic indicators, influence the infection risk or other epidemic variables. This analysis can support health officials in managing the outbreak to consider information about indicators in compartment models for propagation prediction and intervention simulation. The objective of this work was to bring widgets that offer a more profound exploration and analysis of the impact of the pandemic on socioeconomic indicators. In our approach, the association of epidemic variables and indicators can be explored with commonly adopted visualization plots. Also, we propose a way to gather the specialist’s risk perception, and as a result, a risk heatmap is produced, allowing the reduction of time series data cluttering. Finally, it is possible to use the risk heatmap information to compare neighbourhoods and socioeconomic indicators by ranking them according to a severity score. Some use cases were performed to demonstrate the use and capability of the proposed widgets.

Designing for Ambiguity in Visual Analytics: Lessons from Risk Assessment and Prediction.

Ambiguity is pervasive in the complex sensemaking domains of risk assessment and prediction but there remains little research on how to design visual analytics tools to accommodate it. We report on findings from a qualitative study based on a conceptual framework of sensemaking processes to investigate how both new visual analytics designs and existing tools, primarily data tables, support the cognitive work demanded in avalanche forecasting. While both systems yielded similar analytic outcomes we observed differences in ambiguous sensemaking and the analytic actions either afforded. Our findings challenge conventional visualization design guidance in both perceptual and interaction design, highlighting the need for data interfaces that encourage reflection, provoke alternative interpretations, and support the inherently ambiguous nature of sensemaking in this critical application. We review how different visual and interactive forms support or impede analytic processes and introduce "gisting" as a significant yet unexplored analytic action for visual analytics research. We conclude with design implications for enabling ambiguity in visual analytics tools to scaffold sensemaking in risk assessment.

A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias.

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.