Статті в журналах: "Application Shiny"

1

Shirokovskikh, Margarita S. "THE APPLICATION OF SHINY MATERIALS IN TAPESTRY: FROM DECOR TO CONCEPT." Architecton: Proceedings of Higher Education, no. 2(70) (June 29, 2020): 21. http://dx.doi.org/10.47055/1990-4126-2020-2(70)-21.

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This article is the first ever attempt to study and summarize the extensive material on the use of shiny threads in tapestry art. The circle of artists using shine as an expressive medium in their artworks is identified. Methods of creating shiny surfaces in this art form and their symbolic meanings are outlined.

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2

Morota, Gota. "79 Statistical Graphics and Interactive Visualization in Animal Science." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 45. http://dx.doi.org/10.1093/jas/skab235.079.

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Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.

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3

Morota, Gota. "312 Statistical graphics and interactive visualization in animal science." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 45–46. http://dx.doi.org/10.1093/jas/skaa278.083.

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Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.

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4

Jagla, Bernd, Valentina Libri, Claudia Chica, Vincent Rouilly, Sebastien Mella, Michel Puceat, and Milena Hasan. "SCHNAPPs - Single Cell sHiNy APPlication(s)." Journal of Immunological Methods 499 (December 2021): 113176. http://dx.doi.org/10.1016/j.jim.2021.113176.

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5

Kemperman, Lauren, and Matthew N. McCall. "miRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms." F1000Research 6 (November 23, 2017): 2046. http://dx.doi.org/10.12688/f1000research.13098.1.

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The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part of the miRcomp package (version 1.3.3 and later) available through the Bioconductor project at: http://bioconductor.org/packages/miRcomp. The web application is hosted at https://laurenkemperman.shinyapps.io/mircomp/. A detailed description of how to use the web application is available at: http://lkemperm.github.io/miRcomp_shiny_app

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6

Li, Jianfeng, Bowen Cui, Yuting Dai, Ling Bai, and Jinyan Huang. "BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform." PeerJ 6 (October 31, 2018): e5853. http://dx.doi.org/10.7717/peerj.5853.

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The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).

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7

Oberti, Mauricio, and Iosif Vaisman. "shiny-pred: a server for the prediction of protein disordered regions." F1000Research 8 (February 28, 2019): 230. http://dx.doi.org/10.12688/f1000research.17669.1.

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Intrinsically disordered proteins or intrinsically disordered regions (IDR) are segments within a protein chain lacking a stable three-dimensional structure under normal physiological conditions. Accurate prediction of IDRs is challenging due to their genome wide occurrence and low ratio of disordered residues, making them a difficult target for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy, which is time consuming and computationally expensive. The shiny-pred application is an ab initio sequence-only disorder predictor implemented in R/Shiny language. In order to make predictions, it uses convolutional neural network models, trained using PDB sequence data. It can be installed on any operating system on which R can be installed and run locally. A public version of the web application can be accessed at https://gmu-binf.shinyapps.io/shiny-pred

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8

Bernardo, Nicholas D., and Gretchen A. Macht. "Learning ‘Learning Curves’ with R Shiny." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 1567–71. http://dx.doi.org/10.1177/1071181321651119.

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Learning curves are fundamental in understanding individual task performance, with ubiquitous implementation in task assignments, worker scheduling, team formulation, etc., in domains bridging from manufacturing to healthcare. With a broad range of applicability, it is critical that students conceptualize, visualize, and build learning curves to activate that knowledge for effective decision-making. This paper describes a hands-on experiential approach for teaching learning curves that utilizes building LEGO® sets with mathematical formulation and data visualization in an open-source R Shiny application. The R Shiny application was designed to educate and inform students of their curve status while automating the power curve fitting calculations. The proposed methodology appeals and applies to students of all ages and was preliminarily field-tested in two collegiate courses and a K-4 after-school program. This paper introduces this approach and the R Shiny app, while future work includes quantifying improved learning.

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9

Marusich, Laura R., and Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation." F1000Research 10 (November 8, 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.2.

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We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.

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10

Marusich, Laura R., and Jonathan Z. Bakdash. "rmcorrShiny: A web and standalone application for repeated measures correlation." F1000Research 10 (July 30, 2021): 697. http://dx.doi.org/10.12688/f1000research.55027.1.

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We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.

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11

Germani, Elodie, Hugues Lelouard, and Mathieu Fallet. "SAPHIR: a Shiny application to analyze tissue section images." F1000Research 9 (April 8, 2021): 1276. http://dx.doi.org/10.12688/f1000research.27062.2.

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Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurements when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation. It offers many features, such as filtering of spurious data points, selection of cell subsets on scatterplot, visualization of scatterplot selections back into the image, statistics of selected data and data annotation. Our application allows to characterize labeled cells, from their phenotype to their number and location in the tissue, as well as their interaction with other cells. SAPHIR is available from: https://github.com/elodiegermani/SAPHIR

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12

Germani, Elodie, Hugues Lelouard, and Mathieu Fallet. "SAPHIR: a Shiny application to analyze tissue section images." F1000Research 9 (October 27, 2020): 1276. http://dx.doi.org/10.12688/f1000research.27062.1.

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Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurement when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation. It offers many features, such as filtering of spurious data points, selection of cell subsets on scatterplot, visualization of scatterplot selections back into the image, statistics of selected data and data annotation. Our application allows to quickly characterize labeled cells, from their phenotype to their number and location in the tissue, as well as their interaction with other cells. SAPHIR is available from: https://github.com/elodiegermani/SAPHIR

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13

Oktavia, Cintika, Budi Warsito, and Vincensius Gunawan Slamet Kadarrisman. "Development of Customer Loyalty Measurement Application using R Shiny." E3S Web of Conferences 448 (2023): 02038. http://dx.doi.org/10.1051/e3sconf/202344802038.

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Shopee is a successful e-commerce platform in Southeast Asia today, becoming the leading choice for customers who want to shop online and has built loyal relationships with customers. Apart from Shopee, many other e-commerce companies also emphasize the importance of building customer loyalty as their primary goal. This research will explore the main factors that play a role in creating Shopee customer loyalty. By measuring customer loyalty, which refers to the variables of customer satisfaction, promotions, data security, and customer service. Through the Structural Equation Modeling Partial Least Squares (SEM-PLS) method. Research data was obtained by distributing questionnaires to 180 customers who purchased on Shopee, distributed via TikTok. The research results show that two hypotheses were tested to understand the influence of variables on customer loyalty. The results show that customer satisfaction has a significant impact that exceeds other variables in creating customer loyalty. Promotion and data security also contribute positively, while customer service, although it has a positive effect, is not significant in influencing customer loyalty. The research conclusion emphasizes customer satisfaction as the primary strategy for maintaining and increasing customer loyalty, especially in the tight competitive environment in the e-commerce industry.

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14

Uttiramerur, Arvind. "Streamlining Clinical Trial Data from Raw to Regulatory Submission with R Shiny and Pharmaverse." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (October 5, 2024): 1–15. http://dx.doi.org/10.55041/ijsrem36907.

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In recent years, the pharmaceutical and biotech industries have increasingly adopted open-source tools to enhance the efficiency, transparency, and reproducibility of clinical trial data analysis. R Shiny, a web application framework for R, has become a key component in creating interactive dashboards and visualizations that support real-time data exploration and decision-making. As part of the Pharmaverse ecosystem, R Shiny plays a vital role in regulatory submissions, data analysis, and reporting, offering dynamic, user-friendly interfaces for clinical trial monitoring, patient safety reporting, and efficacy analysis. This abstract explores the application of R Shiny within the Pharmaverse, emphasizing its seamless integration with tools such as Tplyr and admiral for generating CDISC-compliant SDTM and ADaM datasets. Metadata management and validation are facilitated through OAK, ensuring consistency and traceability. The automated creation of Define.xml using tools like defineR further enhances transparency and regulatory compliance. Through case studies and practical examples, this abstract highlights how R Shiny and related tools improve data transparency, accelerate decision-making, and support regulatory compliance in clinical trials. By leveraging these open-source technologies, clinical trial teams can streamline data workflows, enhance collaboration, and improve outcomes, ultimately driving innovation within the pharmaceutical industry. The integration of these tools into a cohesive framework significantly enhances the submission process to regulatory bodies like the FDA and EMA, ensuring that clinical data meets both industry and regulatory standards.

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15

Johnson, Olatunji, Claudio Fronterre, Peter J. Diggle, Benjamin Amoah, and Emanuele Giorgi. "MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists." PLOS ONE 16, no. 12 (December 31, 2021): e0262145. http://dx.doi.org/10.1371/journal.pone.0262145.

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User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.

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16

Gu, Zuguang, and Daniel Hübschmann. "Make Interactive Complex Heatmaps in R." Bioinformatics 38, no. 5 (December 2, 2021): 1460–62. http://dx.doi.org/10.1093/bioinformatics/btab806.

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Abstract Summary Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ComplexHeatmap package. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one additional line of code. InteractiveComplexHeatmap also provides flexible functionalities for integrating interactive heatmap widgets to build more complex and customized Shiny web applications. Availability and implementation The InteractiveComplexHeatmap package and documentations are freely available from the Bioconductor project: https://bioconductor.org/packages/InteractiveComplexHeatmap/. A complete and printer-friendly version of the documentation can also be found in Supplementary File S1. Supplementary information Supplementary data are available at Bioinformatics online.

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17

Rudenok, V. A., O. M. Kanunnikova, G. N. Aristova, and O. S. Tikhonova. "The design and properties of galvanic anticorrosive coatings for important precision parts of farming equipment." IOP Conference Series: Earth and Environmental Science 949, no. 1 (January 1, 2022): 012113. http://dx.doi.org/10.1088/1755-1315/949/1/012113.

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Abstract The paper explores the possibility of using a number of nickel alloys in multilayer coatings to decrease nickel consumption and preserve the functional effect of the coating. The following is proved by the graphical calculation technique using experimental data on the galvanic properties of the multilayer coating parts. Nickel-iron, nickel-phosphorus and nickel-tin alloy can be applied as a lower coating layer rather than semi-shiny, shiny or composite nickel. It is advisable to use a nickel-iron alloy as the middle (second) layer, and the concentration of iron depends on the composition of the first and third layers. If a nickel-iron alloy is applied as the material of the first layer, then the second layer may be semi-shiny (Ns-sh) or shiny (Nsh) nickel. The substitution of nickel layers for nickel alloys allows to considerably (about 10%) decrease the cost of a multilayer coating, while the protective properties are remaining the same. The application of the same nickel-containing alloys as single-layer anticorrosive coatings shows a lower level of protective properties.

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18

Danger, Richard, Quentin Moiteaux, Yodit Feseha, Estelle Geffard, Gérard Ramstein, and Sophie Brouard. "FaDA: A web application for regular laboratory data analyses." PLOS ONE 16, no. 12 (December 20, 2021): e0261083. http://dx.doi.org/10.1371/journal.pone.0261083.

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Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.

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19

Class, Caleb A., Min Jin Ha, Veerabhadran Baladandayuthapani, and Kim-Anh Do. "iDINGO—integrative differential network analysis in genomics with Shiny application." Bioinformatics 34, no. 7 (November 29, 2017): 1243–45. http://dx.doi.org/10.1093/bioinformatics/btx750.

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20

Rigby, James, and Zach Traylor. "Capturing Trends in Industrial-Organizational Psychology: A Shiny Web Application." Human Performance 33, no. 4 (April 15, 2020): 302–6. http://dx.doi.org/10.1080/08959285.2020.1751165.

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21

Yu, Chi-Lin, and Ching-Fan Sheu. "EFAshiny: An User-Friendly Shiny Application for Exploratory Factor Analysis." Journal of Open Source Software 3, no. 22 (February 12, 2018): 567. http://dx.doi.org/10.21105/joss.00567.

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22

Okour, Malek. "DosePredict: A Shiny Application for Generalized Pharmacokinetics‐Based Dose Predictions." Journal of Clinical Pharmacology 60, no. 11 (June 15, 2020): 1502–8. http://dx.doi.org/10.1002/jcph.1649.

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23

Uttiramerur, Arvind. "Managing Adverse Events in Clinical Trials with R and Shiny." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (October 7, 2024): 1–15. http://dx.doi.org/10.55041/ijsrem36769.

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Adverse events (AEs) are critical occurrences that can significantly impact patient safety and the overall success of clinical trials. Effective management of AEs is essential for ensuring regulatory compliance and safeguarding participants' well-being. This paper explores the integration of R and Shiny as innovative tools for the real-time monitoring, visualization, and analysis of AEs in clinical trials. By leveraging R’s robust statistical capabilities and Shiny’s interactive interface, researchers can enhance data management processes, facilitate informed decision- making, and foster collaboration among stakeholders. A case study is presented to illustrate the practical application of R Shiny in tracking AEs, demonstrating its effectiveness in improving trial outcomes and ensuring patient safety. The findings emphasize the need for adopting dynamic data analysis tools to optimize adverse event management in the evolving landscape of clinical research. KEYWORDS Adverse Events, Clinical Trials, R, Shiny, Data Management, Visualization, Real-Time Monitoring, Patient Safety.

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24

Zhang, Xiao-Fei, Le Ou-Yang, Shuo Yang, Xing-Ming Zhao, Xiaohua Hu, and Hong Yan. "EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning." Bioinformatics 35, no. 22 (May 24, 2019): 4827–29. http://dx.doi.org/10.1093/bioinformatics/btz435.

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Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

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25

Aswi, Aswi, Muhammad Arif Tiro, Sudarmin Sudarmin, Sukarna Sukarna, Awi Awi, Nurwan Nurwan, and Susanna Cramb. "MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION." MEDIA STATISTIKA 16, no. 2 (December 29, 2023): 148–59. http://dx.doi.org/10.14710/medstat.16.2.148-159.

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Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.

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26

Obermayer, Alyssa, Li Dong, Qianqian Hu, Michael Golden, Jerald D. Noble, Paulo Rodriguez, Timothy J. Robinson, Mingxiang Teng, Aik-Choon Tan, and Timothy I. Shaw. "DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets." Biology 11, no. 2 (February 8, 2022): 260. http://dx.doi.org/10.3390/biology11020260.

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High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

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27

Gaythorpe, Katy, Aaron Morris, Natsuko Imai, Miles Stewart, Jeffrey Freeman, and Mary Choi. "Chainchecker: An application to visualise and explore transmission chains for Ebola virus disease." PLOS ONE 16, no. 2 (February 19, 2021): e0247002. http://dx.doi.org/10.1371/journal.pone.0247002.

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2020 saw the continuation of the second largest outbreak of Ebola virus disease (EVD) in history. Determining epidemiological links between cases is a key part of outbreak control. However, due to the large quantity of data and subsequent data entry errors, inconsistencies in potential epidemiological links are difficult to identify. We present chainchecker, an online and offline shiny application which visualises, curates and verifies transmission chain data. The application includes the calculation of exposure windows for individual cases of EVD based on user defined incubation periods and user specified symptom profiles. It has an upload function for viral hemorrhagic fever data and utility for additional entries. This data may then be visualised as a transmission tree with inconsistent links highlighted. Finally, there is utility for cluster analysis and the ability to highlight nosocomial transmission. chainchecker is a R shiny application which has an offline version for use with VHF (viral hemorrhagic fever) databases or linelists. The software is available at https://shiny.dide.imperial.ac.uk/chainchecker which is a web-based application that links to the desktop application available for download and the github repository, https://github.com/imperialebola2018/chainchecker.

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28

Lacroix, Laurent. "G4HunterApps." Bioinformatics 35, no. 13 (November 16, 2018): 2311–12. http://dx.doi.org/10.1093/bioinformatics/bty951.

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Abstract Motivation In order to help G4Hunter users and make it more accessible, I have developed a set of small applications within the Shiny/R framework. Results Each application fulfils simple tasks ranging from computing the G4Hunter score for a sequence or a list of sequence to extracting sequences with a G4Hunter score above a threshold for a sequence up to 5 Mb or a list of short sequences. The application can be installed either on the user computer within Rstudio or on a Rstudio server. Availability and implementation The source code for the ShinyApps is available on GitHub (https://github.com/LacroixLaurent).

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Sakhteman, Amirhossein, Arindam Ghosh, and Vittorio Fortino. "EDTox: an R Shiny application to predict the endocrine disruption potential of compounds." Bioinformatics 38, no. 7 (February 3, 2022): 2066–69. http://dx.doi.org/10.1093/bioinformatics/btac045.

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Abstract Purpose Endocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new classifiers based on new available toxicogenomic data. This functionality helps users to explore the ED potential of chemicals in new, untested exposure scenarios. Availability and implementation This tool is available as web application (www.edtox.fi) and stand-alone software on GitHub and Zenodo (https://doi.org/10.5281/zenodo.5817093). Supplementary information Supplementary data are available at Bioinformatics online.

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Terzi di Bergamo, Lodovico, Francesca Guidetti, Davide Rossi, Francesco Bertoni, and Luciano Cascione. "HTGQC and shinyHTGQC: an R package and shinyR application for quality controls of HTG EDGE-seq protocols." Gigabyte 2022 (December 2, 2022): 1–5. http://dx.doi.org/10.46471/gigabyte.74.

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Extraction-free HTG EdgeSeq protocols are used to profile sets of genes and measure their expression. Thus, these protocols are frequently used to characterise tumours and their microenvironments. However, although positive and control genes are provided, little indication is given concerning the assessment of the technical success of each sample within the sequencing run. We developed HTGQC, an R package for the quality control of HTG EdgeSeq protocols. Additionally, shinyHTGQC is a shiny application for users without computing knowledge, providing an easy-to-use interface for data quality control and visualisation. Quality checks can be performed on the raw sequencing outputs, and samples are flagged as FAIL or ALERT based on the expression levels of the positive and negative control genes. Availability & Implementation The code is freely available at https://github.com/LodovicoTerzi/HTGQC (R package) and https://lodovico.shinyapps.io/shinyHTGQC/ (shiny application), including test datasets.

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Andersen, Nico, and Fabian Zehner. "shinyReCoR: A Shiny Application for Automatically Coding Text Responses Using R." Psych 3, no. 3 (August 16, 2021): 422–46. http://dx.doi.org/10.3390/psych3030030.

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In this paper, we introduce shinyReCoR: a new app that utilizes a cluster-based method for automatically coding open-ended text responses. Reliable coding of text responses from educational or psychological assessments requires substantial organizational and human effort. The coding of natural language in responses to tests depends on the texts’ complexity, corresponding coding guides, and the guides’ quality. Manual coding is thus not only expensive but also error-prone. With shinyReCoR, we provide a more efficient alternative. The use of natural language processing makes texts utilizable for statistical methods. shinyReCoR is a Shiny app deployed as an R-package that allows users with varying technical affinity to create automatic response classifiers through a graphical user interface based on annotated data. The present paper describes the underlying methodology, including machine learning, as well as peculiarities of the processing of language in the assessment context. The app guides users through the workflow with steps like text corpus compilation, semantic space building, preprocessing of the text data, and clustering. Users can adjust each step according to their needs. Finally, users are provided with an automatic response classifier, which can be evaluated and tested within the process.

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Lakshmanan, Karthick, Arul Prakasham Peter, Shylajanaciyar Mohandass, Sangeetha Varadharaj, Uma Lakshmanan, and Prabaharan Dharmar. "SynRio: R and Shiny based application platform for cyanobacterial genome analysis." Bioinformation 11, no. 9 (September 30, 2015): 422–25. http://dx.doi.org/10.6026/97320630011422.

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Yu, Yiming, Yidan Ouyang, and Wen Yao. "shinyCircos: an R/Shiny application for interactive creation of Circos plot." Bioinformatics 34, no. 7 (November 24, 2017): 1229–31. http://dx.doi.org/10.1093/bioinformatics/btx763.

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McMurdie, P. J., and S. Holmes. "Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking." Bioinformatics 31, no. 2 (September 26, 2014): 282–83. http://dx.doi.org/10.1093/bioinformatics/btu616.

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Nieuwenhuijse, David F., Bas B. Oude Munnink, and Marion P. G. Koopmans. "viromeBrowser: A Shiny App for Browsing Virome Sequencing Analysis Results." Viruses 13, no. 3 (March 9, 2021): 437. http://dx.doi.org/10.3390/v13030437.

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Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.

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Contrino, Bruno, Eric Miele, Ronald Tomlinson, M. Paola Castaldi, and Piero Ricchiuto. "DOSCHEDA: a web application for interactive chemoproteomics data analysis." PeerJ Computer Science 3 (August 28, 2017): e129. http://dx.doi.org/10.7717/peerj-cs.129.

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Background Mass Spectrometry (MS) based chemoproteomics has recently become a main tool to identify and quantify cellular target protein interactions with ligands/drugs in drug discovery. The complexity associated with these new types of data requires scientists with a limited computational background to perform systematic data quality controls as well as to visualize the results derived from the analysis to enable rapid decision making. To date, there are no readily accessible platforms specifically designed for chemoproteomics data analysis. Results We developed a Shiny-based web application named DOSCHEDA (Down Stream Chemoproteomics Data Analysis) to assess the quality of chemoproteomics experiments, to filter peptide intensities based on linear correlations between replicates, and to perform statistical analysis based on the experimental design. In order to increase its accessibility, DOSCHEDA is designed to be used with minimal user input and it does not require programming knowledge. Typical inputs can be protein fold changes or peptide intensities obtained from Proteome Discover, MaxQuant or other similar software. DOSCHEDA aggregates results from bioinformatics analyses performed on the input dataset into a dynamic interface, it encompasses interactive graphics and enables customized output reports. Conclusions DOSCHEDA is implemented entirely in R language. It can be launched by any system with R installed, including Windows, Mac OS and Linux distributions. DOSCHEDA is hosted on a shiny-server at https://doscheda.shinyapps.io/doscheda and is also available as a Bioconductor package (http://www.bioconductor.org/).

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Messori, Andrea, Vera Damuzzo, Melania Rivano, Luca Cancanelli, Lorenzo Di Spazio, Andrea Ossato, Marco Chiumente, and Daniele Mengato. "Application of the IPDfromKM-Shiny Method to Compare the Efficacy of Novel Treatments Aimed at the Same Disease Condition: A Report of 14 Analyses." Cancers 15, no. 6 (March 7, 2023): 1633. http://dx.doi.org/10.3390/cancers15061633.

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In the area of evidence-based medicine, the IPDfromKM-Shiny method is an innovative method of survival analysis, midway between artificial intelligence and advanced statistics. Its main characteristic is that an original software investigates the Kaplan-Meier graphs of trials so that individual-patient data are reconstructed. These reconstructed patients represent a new form of original clinical material. The typical objective of investigations based on this method is to analyze the available evidence, especially in oncology, to perform indirect comparisons, and determine the place in therapy of individual agents. This review examined the most recent applications of the IPDfromKM-Shiny method, in which a new web-based software—published in 2021—was used. Reported here are 14 analyses, mostly focused on oncological treatments. Indirect comparisons were based on overall survival or progression free survival. Each of these analyses provided original information to compare treatments with one another and select the most appropriate depending on patient characteristics. These analyses can also be useful to assess equivalence from a regulatory viewpoint. All investigations stressed the importance of heterogeneity to better interpret the evidence generated by IPDfromKM-Shiny investigations. In conclusion, these investigations showed that the reconstruction of individual patient data through this online tool is a promising new method for analyzing trials based on survival endpoints. This new approach deserves further investigation, particularly in the area of indirect comparisons.

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Zhao, Zunlan, Shouhang Chen, Hongzhao Wei, Weile Ma, Weili Shi, Yixin Si, Jun Wang, Liuyi Wang, and Xiqing Li. "Online application for the diagnosis of atherosclerosis by six genes." PLOS ONE 19, no. 4 (April 10, 2024): e0301912. http://dx.doi.org/10.1371/journal.pone.0301912.

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Background Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS. Methods We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction. Results Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app’s server, making it easy for users to distinguish AS samples from normal samples. Conclusions A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

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Savini, Lara, Luca Candeloro, Samuel Perticara, and Annamaria Conte. "EpiExploreR: A Shiny Web Application for the Analysis of Animal Disease Data." Microorganisms 7, no. 12 (December 11, 2019): 680. http://dx.doi.org/10.3390/microorganisms7120680.

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Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities.

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Smith, Robert, and Paul Schneider. "Making health economic models Shiny: A tutorial." Wellcome Open Research 5 (April 14, 2020): 69. http://dx.doi.org/10.12688/wellcomeopenres.15807.1.

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Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and it might make it difficult to directly communicate results with decisions makers and other stakeholders. The R package Shiny has the potential to resolve this limitation. It allows programmers to embed health economic models developed in R into interactive web browser based user interfaces. Users can specify their own assumptions about model parameters and run different scenario analyses, which, in the case of regular a Markov model, can be computed within seconds. This paper provides a tutorial on how to wrap a health economic model built in R into a Shiny application. We use a four-state Markov model developed by the Decision Analysis in R for Technologies in Health (DARTH) group as a case-study to demonstrate main principles and basic functionality. A more extensive tutorial, all code, and data are provided in a GitHub repository.

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Smith, Robert, and Paul Schneider. "Making health economic models Shiny: A tutorial." Wellcome Open Research 5 (July 31, 2020): 69. http://dx.doi.org/10.12688/wellcomeopenres.15807.2.

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Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and it might make it difficult to directly communicate results with decisions makers and other stakeholders. The R package Shiny has the potential to resolve this limitation. It allows programmers to embed health economic models developed in R into interactive web browser based user interfaces. Users can specify their own assumptions about model parameters and run different scenario analyses, which, in the case of regular a Markov model, can be computed within seconds. This paper provides a tutorial on how to wrap a health economic model built in R into a Shiny application. We use a four-state Markov model developed by the Decision Analysis in R for Technologies in Health (DARTH) group as a case-study to demonstrate main principles and basic functionality. A more extensive tutorial, all code, and data are provided in a GitHub repository.

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ARSLAN, Ahmet Kadir, Şeyma YAŞAR, Cemil ÇOLAK, and Saim YOLOĞLU. "WSSPAS: An Interactive Web Application for Sample Size and Power Analysis with R Using Shiny." Turkiye Klinikleri Journal of Biostatistics 10, no. 3 (2018): 224–46. http://dx.doi.org/10.5336/biostatic.2018-62787.

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Benítez, Rafael, Vicente Coll-Serrano, and Vicente J. Bolós. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis." Sustainability 13, no. 12 (June 15, 2021): 6774. http://dx.doi.org/10.3390/su13126774.

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In this paper, we describe an interactive web application (deaR-shiny) to measure efficiency and productivity using data envelopment analysis (DEA). deaR-shiny aims to fill the gap that currently exists in the availability of online DEA software offering practitioners and researchers free access to a very wide variety of DEA models (both conventional and fuzzy models). We illustrate how to use the web app by replicating the main results obtained by Carlucci, Cirà and Coccorese in 2018, who investigate the efficiency and economic sustainability of Italian regional airport by using two conventional DEA models, and the results given by Kao and Liu in their papers published in 2000 and 2003, who calculate the efficiency scores of university libraries in Taiwan by using a fuzzy DEA model because they treat missing data as fuzzy numbers.

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Fernandez Coves, A., L. van Schaik, B. Ramaekers, S. Grimm, M. Joore, and V. Retel. "HTA40 Making MCDA 'Shiny' With an Adaptive Support Tool for Healthcare Decision-Making: An Application in Broad Molecular Testing With R Shiny." Value in Health 26, no. 12 (December 2023): S327—S328. http://dx.doi.org/10.1016/j.jval.2023.09.1725.

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Fu, Yu, N. Snelder, P. H. van der Graaf, and J. G. C. van Hasselt. "Hemodynamic simulator: A Shiny web application for predicting drug effect on hemodynamics." Journal of Pharmacological and Toxicological Methods 105 (September 2020): 106753. http://dx.doi.org/10.1016/j.vascn.2020.106753.

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Lynch, Andy G. "Crambled: A Shiny application to enable intuitive resolution of conflicting cellularity estimates." F1000Research 4 (December 7, 2015): 1407. http://dx.doi.org/10.12688/f1000research.7453.1.

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It is now commonplace to investigate tumour samples using whole-genome sequencing, and some commonly performed tasks are the estimation of cellularity (or sample purity), the genome-wide profiling of copy numbers, and the assessment of sub-clonal behaviours. Several tools are available to undertake these tasks, but often give conflicting results – not least because there is often genuine uncertainty due to a lack of model identifiability. Presented here is a tool, "Crambled", that allows for an intuitive visual comparison of the conflicting solutions. Crambled is implemented as a Shiny application within R, and is accompanied by example images from two use cases (one tumour sample with matched normal sequencing, and one standalone cell line example) as well as functions to generate the necessary images from any sequencing data set. Through the use of Crambled, a user may gain insight into why each tool has offered its given solution and combined with a knowledge of the disease being studied can choose between the competing solutions in an informed manner.

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Chen, Rory, Vibeke S. Catts, Ashleigh Vella, Juan Carlo San Jose, Sarah Bauermeister, Joshua Bauermeister, Emma Squires, Simon Thompson, John Gallacher, and Perminder S. Sachdev. "DataRepExp: a R shiny Application that makes Data FAIR for Data Repositories." Journal of Open Source Software 9, no. 101 (September 30, 2024): 6693. http://dx.doi.org/10.21105/joss.06693.

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Irawan, Dasapta Erwin, Muhammad Aswan Syahputra, Prana Ugi, and Deny Juanda Puradimaja. "Thermostats: an Open Source Shiny App for Your Open Data Repository." JOIV : International Journal on Informatics Visualization 3, no. 2-2 (August 17, 2019): 233. http://dx.doi.org/10.30630/joiv.3.2-2.282.

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Hydrochemical analysis has emerged as a powerful methodology in geothermal system profiling. Indonesia is the capital of geothermal energy with its more than 100 active volcanoes. Therefore we need to have an analytical, data-driven, and user-focused online application of geothermal water quality. Proudly we introduce Thermostats (https://aswansyahputra.shinyapps.io/thermostats/). We collected water quality from 416 geothermal sites across Indonesia. Three main objectives are to provide an online open-free to use data repository, to visualize the dataset to suit user’s needs, and to help users understand the geothermal system of each particular site. At the end, we hope they like this system and donate their own dataset to make it better for future users. We designed this online app using Shiny, because it’s open source, lightweight and portable. It’s very intuitive to load our descriptive, bivariate and multivariate statistics. We selected Principal Component Analysis and Cluster Analysis as two strong statistics for water sample classification. Users could add their own dataset by making a pull request on Github (https://github.com/dasaptaerwin/thermostats) or sending it to us by email to make it visible in the application and included in the visualization. We make this application portable, so it can be installed on a local computer or a server, to enable an easy and fluid way of data sharing between collaborators.

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Rodríguez, Dania M., Kyle Ryff, Liliana Sánchez-Gonzalez, Vanessa Rivera-Amill, Gabriela Paz-Bailey, and Laura Adams. "HTrack: A new tool to facilitate public health field visits and electronic data capture." PLOS ONE 15, no. 12 (December 15, 2020): e0244028. http://dx.doi.org/10.1371/journal.pone.0244028.

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Many applications have been developed for electronic data collection. However, offline field navigation tools incorporating secure electronic data capture and field visit tracking are currently scarce. We created an R-Shiny application, HTrack (Household Tracking), for use on encrypted Android devices in the field. The application was implemented in the Communities Organized to Prevent Arboviruses (COPA) project, a study beginning in 2018 to better understand arboviral disease incidence in 38 communities in Puerto Rico. The application was used to navigate to randomly selected structures and capture visit outcomes after conducting multiple visits for participant recruitment. It also served as a bridge to an alternate software, Epi Info, to collect participant-level questionnaire data. This application successfully captured each visit outcome and improved the logistics of field level activities for the COPA project, eliminating the use of paper maps for navigation. We show the development of HTrack and comment on the limitations and strengths of this application and further improvements.

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Patil, Abhijeet R., Gaurav Kumar, Huanyu Zhou, and Liling Warren. "scViewer: An Interactive Single-Cell Gene Expression Visualization Tool." Cells 12, no. 11 (May 27, 2023): 1489. http://dx.doi.org/10.3390/cells12111489.

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Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer’s disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.

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