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Background: The dilution-replicate experimental design for qPCR assays is especially efficient. It is based on multiple linear regression of multiple 3-point standard curves that are derived from the experimental samples themselves and thus obviates the need for a separate standard curve produced by serial dilution of a standard. The method minimizes the total number of reactions and guarantees that Cq values are within the linear dynamic range of the dilution-replicate standard curves. However, the lack of specialized software has so far precluded the widespread use of the dilution-replicate approach. Results: Here we present repDilPCR, the first tool that utilizes the dilution-replicate method and extends it by adding the possibility to use multiple reference genes. repDilPCR offers extensive statistical and graphical functions that can also be used with preprocessed data (relative expression values) obtained by usual assay designs and evaluation methods. repDilPCR has been designed with the philosophy to automate and speed up data analysis (typically less than a minute from Cq values to publication-ready plots), and features automatic selection and performance of appropriate statistical tests, at least in the case of one-factor experimental designs. Nevertheless, the program also allows users to export intermediate data and perform more sophisticated analyses with external statistical software, e.g. if two-way ANOVA is necessary. Conclusions: repDilPCR is a user-friendly tool that can contribute to more efficient planning of qPCR experiments and their robust analysis. A public web server is freely accessible at https://repdilpcr.eu without registration. The program can also be used as an R script or as a locally installed Shiny app, which can be downloaded from https://github.com/deyanyosifov/repDilPCR where also the source code is available. [ABSTRACT FROM AUTHOR]

Background: Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning. Results: Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background. Conclusions: OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning. [ABSTRACT FROM AUTHOR]

Abstract Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Background Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). These technologies have transformed the analysis of pathological images, enhancing the accuracy of diagnosing and classifying cellular anomalies. Digital pathology methodologies, including image analysis, have improved cervical cancer diagnostics. However, existing commercial platforms are often costly and restrictive, limiting customization and scalability. Methods CINNAMON-GUI is an open-source digital pathology tool based on CNNs for classifying Pap smear images. Transitioning to a Shiny app in Python, it offers enhanced user interface and interactivity. The application supports dynamic web interactions, advanced features for image analysis, and state-of-the-art CNN models tailored for digital pathology. Key features include intuitive UI components, real-time image and plot generation, memory-efficient data handling, and robust training capabilities with customizable CNN architectures. The tool also integrates with Labelme for defining regions of interest and allows testing on external biospecimens. Results Model A (seed 42, 100 epochs) and Model B (same architecture with adjusted augmentation parameters) were compared. Model A stabilized with training accuracy around 0.88 and validation accuracy around 0.913. Model B showed improved performance with training accuracy around 0.91 and validation accuracy around 0.95. Feature mapping highlighted critical morphological aspects, improving classification accuracy. Model B reduced misclassification errors significantly compared to Model A. Conclusions CINNAMON-GUI demonstrates the potential of an open-source platform in digital pathology, providing transparency and collaborative opportunities. The tool enhances diagnostic accuracy through feature map analysis and optimized CNN training. Future development aims to extend its application to other cancer types, leveraging its dynamic and user-friendly interface for broader use in diagnostics.

Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p‐value. Null hypothesis Bayesian testing and its so‐called Bayes factor are now becoming increasingly popular. Although the Bayes factor is often introduced as being the Bayesian counterpart to the p‐value, its computation, use, and interpretation are quite distinct from the p‐value. There is now evidence confirming that the application of the Bayes factor in applied research is ill‐devised. To improve the current status quo, we have created a Shiny/R app called the Bayes factor, which offers a dynamic tutorial for learning all the basics about the Bayes factor. In this paper, we explain how the app works and we offer suggestions on how to use it in class or self‐study settings. The app is freely available at https://statsedge.org/shiny/LearnBF/. [ABSTRACT FROM AUTHOR]

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly. [ABSTRACT FROM AUTHOR]

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS. [ABSTRACT FROM AUTHOR]

Abstract Background Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. Methods Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. Results A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. Conclusions OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/ (Alternate URL: http://robinl-lab.com/OralExplorer ).

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the 'plotly' and 'DT' packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as 'rle' and 'read.csv' with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.

Background: Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. Methods: Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. Results: A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. Conclusions: OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/ (Alternate URL: http://robinl-lab.com/OralExplorer). [ABSTRACT FROM AUTHOR]

The presented information and analytical system is designed to provide convenient management of the processes of collecting, processing and analyzing air pollution data. The system is a web application built on the basis of the R programming language using the Shiny web service and works with data from Vaisala monitoring stations. The created service receives data from the monitoring stations on various air parameters such as wind speed, temperature, and levels of pollutants such as PM2.5, CO, and NO2.The main functions of the web service include uploading data to a dedicated server, processing data with the ability to round time ranges, interactive data visualization using graphs and charts, building air quality charts, and presenting AQI, which can be used to classify pollution levels and generate recommendations for different sensitivity groups of the population.The system allows users to download data from the server in various formats or access the original data from the Vaisala server. In addition, users can convert data to different units of measurement. The web service can be of interest to both household users and professionals in various fields, including environmental researchers in Ukraine, governmental environmental control organizations, municipal and public organizations interested in a clean environment. By improving the availability and analysis of air quality data, this service contributes to the preservation of the environment and public health.

Phase-type distributions (PHDs), which are defined as the distribution of the lifetime up to the absorption in an absorbent Markov chain, are an appropriate candidate to model the lifetime of any system, since any non-negative probability distribution can be approximated by a PHD with sufficient precision. Despite PHD potential, friendly statistical programs do not have a module implemented in their interfaces to handle PHD. Thus, researchers must consider others statistical software such as R, Matlab or Python that work with the compilation of code chunks and functions. This fact might be an important handicap for those researchers who do not have sufficient knowledge in programming environments. In this paper, a new interactive web application developed with shiny is introduced in order to adjust PHD to an experimental dataset. This open access app does not require any kind of knowledge about programming or major mathematical concepts. Users can easily compare the graphic fit of several PHDs while estimating their parameters and assess the goodness of fit with just several clicks. All these functionalities are exhibited by means of a numerical simulation and modeling the time to live since the diagnostic in primary breast cancer patients.

AbstractAs the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there remains a need for tools that assist faculty when making data-based examples and assignments. In this article, we describe a free application, available both online and through an R package, which we developed for instructors of courses that incorporate introductory statistical methods. The app treats the process of coming up with data for examples and assignments synergistically, beginning with either data creation (through random number generation) or adjustment (when the user has data) and ending with an assessment of the data’s suitability for the desired analysis. The app enables the quick creation of many versions of variables for multiple versions of assignments and is compatible with file types associated with popular interdisciplinary software such as Excel, R, and SPSS. We highlight the app’s features and discuss survey feedback provided by faculty that demonstrates the desire and need for and effectiveness of such an app. Supplementary materials for this article are available online.

Abstract Data mining from RNA-seq or microarray data has become an essential part of cancer biomarker exploration. Certain existing web servers are valuable and broadly utilized, but the meta-analysis of multiple datasets is absent. Most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Indeed, consistent performance across multiple independent cohorts is the foundation for an excellent biomarker. Moreover, the deeper exploration of specific biomarkers on underlying mechanisms, tumor microenvironment, and drug indications are missing in existing tools. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. To ensure the comparability of genes between different sequencing technologies and the legibility of clinical traits, we re-annotated transcriptome data and unified the nomenclature of clinical traits. BEST delivers fast and customizable functions, including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, our web server provides multiple cleaned-up independent datasets and diverse analysis functionalities, helping unleash the value of current data resources. It is freely available at https://rookieutopia.com/ .

Abstract Background Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. Results Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. Conclusion signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

As the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there remains a need for tools that assist faculty when making data-based examples and assignments. In this article, we describe a free application, available both online and through an R package, which we developed for instructors of courses that incorporate introductory statistical methods. The app treats the process of coming up with data for examples and assignments synergistically, beginning with either data creation (through random number generation) or adjustment (when the user has data) and ending with an assessment of the data's suitability for the desired analysis. The app enables the quick creation of many versions of variables for multiple versions of assignments and is compatible with file types associated with popular interdisciplinary software such as Excel, R, and SPSS. We highlight the app's features and discuss survey feedback provided by faculty that demonstrates the desire and need for and effectiveness of such an app. for this article are available online. [ABSTRACT FROM AUTHOR]

Many topics in organizational research involve examining the interpersonal perceptions and behaviors of group members. The resulting data can be analyzed using the social relations model (SRM). This model enables researchers to address several important questions regarding relational phenomena. In the model, variance can be partitioned into group, actor, partner, and relationship; reciprocity can be assessed in terms of individuals and dyads; and predictors at each of these levels can be analyzed. However, analyzing data using the currently available SRM software can be challenging and can deter organizational researchers from using the model. In this article, we provide a "go-to" introduction to SRM analyses and propose SRM_R (https://davidakenny.shinyapps.io/SRM%5fR/), an accessible and user-friendly, web-based application for SRM analyses. The basic steps of conducting SRM analyses in the app are illustrated with a sample dataset of 47 teams, 228 members, and 884 dyadic observations, using the participants' ratings of the advice-seeking behavior of their fellow employees. [ABSTRACT FROM AUTHOR]

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan–Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles. [ABSTRACT FROM AUTHOR]

Background: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. Results: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. Conclusion: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at (https://doi.org/10.18129/B9.bioc.signeR). [ABSTRACT FROM AUTHOR]

Data mining from RNA-seq or microarray data has become an essential part of cancer biomarker exploration. Certain existing web servers are valuable and broadly utilized, but the meta-analysis of multiple datasets is absent. Most web servers only contain tumor samples from the TCGA database with only one cohort for each cancer type, which also means that the analysis results mainly derived from a single cohort are thin and unstable. Indeed, consistent performance across multiple independent cohorts is the foundation for an excellent biomarker. Moreover, the deeper exploration of specific biomarkers on underlying mechanisms, tumor microenvironment, and drug indications are missing in existing tools. Thus, we introduce BEST (Biomarker Exploration for Solid Tumors), a web application for comprehensive biomarker exploration on large-scale data in solid tumors. To ensure the comparability of genes between different sequencing technologies and the legibility of clinical traits, we re-annotated transcriptome data and unified the nomenclature of clinical traits. BEST delivers fast and customizable functions, including clinical association, survival analysis, enrichment analysis, cell infiltration, immunomodulator, immunotherapy, candidate agents, and genomic alteration. Together, our web server provides multiple cleaned-up independent datasets and diverse analysis functionalities, helping unleash the value of current data resources. It is freely available at https://rookieutopia.com/. [ABSTRACT FROM AUTHOR]

Decision making in dam safety is fundamentally based on the comparison between the predictions of a behavior model and the records of the monitoring system. Traditionally, simple linear regression models have been used. Recently, models based on machine learning are being explored, which generally offer greater precision –therefore, greater capacity for detecting anomalies –, higher flexibility and versatility. We have developed an interactive application based on R-Shiny to generate models based on boosted regression trees, evaluate their accuracy and analyze the effect of predictor variables on the system response. This allows for identifying changes in dam behavior, detecting potential anomalies and better understanding the effect of the loads on the structure. The availability of the software will contribute to the penetration of machine learning techniques in the dam engineering sector and will open the door to its use in structural health monitoring for other civil infrastructures.

Abstract Background High-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments. Results Herein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch). Conclusions ΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region.

Abstract Background Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. Results To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. Conclusions MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .

In many areas of healthcare, digitization has progressed only slowly so far. The SARS-CoV-2 pandemic in particular has shown how valuable software solutions that are deployable at short notice, can be. In this review we present some selected possibilities of the easy-to-learn programming language R and demonstrate potential applications of the package Shiny in the fields of statistical analysis and laboratory medicine. In addition to a brief tabular overview of published applications, we present two examples of their use in routine laboratory workflows. The first example demonstrates how a Shiny app can be used to estimate the minimal difference (MD) of laboratory analytes, while the second example illustrates how pre- and post-analytical processing steps can be integrated into a fully automated workflow using R and the Shiny package.

Many inferential and predictive statistical procedures possess underlying theoretical assumptions that should be met in order for the results of those procedures to be considered reliable. One assumption associated with methods for population means, including linear regression coefficients, is that of normality of a population(s). When assessing normality, two graphical tools that are often utilized are normal quantile-quantile (QQ) plots and histograms. However, while these tools are popular, they still present challenges for many who use them due to the subjectivity oftentimes involved when examining them. In this article, we describe a free, interactive Shiny application, downloadable as an R package, which implements two procedures recently developed for graphical inference with a specific emphasis on assessing normality. The application was created and designed with a focus on statistics education.

HyperGUI is an intuitive R-based web application crafted to streamline the loading of raw hyperspectral images while offering essential processing functions for exploring hyperspectral data. Users can easily load, process, and visualize benchtop or aerial hyperspectral images, with the flexibility to export them to R, Python, MATLAB, or raster data frames for further analysis. HyperGUI’s source code is available on GitHub, and it is accessible online via shinyapps.io.

Scientific time-series measurements involve tracking multiple variables over extended periods, leading to large datasets. Analyzing such data requires experts to observe overall trends and small fluctuations. However, this can be time-consuming in R due to existing limitations. To address this, a new package called ''shinyHugePlot'' is proposed. It uses the shiny platform and plotly to interactively visualize data, focusing on smaller samples generated by a specific algorithm. These samples update based on user-defined data ranges, significantly speeding up visualization for large datasets. This tool aids in identifying key data features and supports new scientific discoveries.

Background: High-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments. Results: Herein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch). Conclusions: ΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region. [ABSTRACT FROM AUTHOR]

Background: Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. Results: To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. Conclusions: MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN. [ABSTRACT FROM AUTHOR]

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research. [ABSTRACT FROM AUTHOR]

Abstract Introduction A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills. Objectives The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys. Methods The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children’s food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables. Results The dashboard layout is structured by the YAML (YAML Ain’t Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny’s render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/ . Examples of how to interact with the dashboard for selected oral health variables are illustrated. Conclusion Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

Abstract Background Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. Results The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. Conclusions All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

IntroductionThe lack of a holistic view of agriculture’s social and economic aspects emerges as a limiting factor in policy implementations of sustainable intensification. The aim of this study was to (i) synthesize data from the five domains of sustainable agriculture intensification framework focusing on Senegal as a case study, (ii) harmonize and scale data used to minimize spatial–temporal differences, and (iii) build a digital tool aiming researchers and policymakers to facilitate the data visualization and connection.MethodsWe propose and prototype an interactive digital decision-support tool as a potential solution to integrate environmental, social, economic, human and production domains in agriculture projects. To demonstrate the use of the tool, a case study using data from Senegal was developed to show the benefits of open science and connectivity among domains.Results and discussionThe digital tool presented here allows users to explore an open repository from Senegal serving as a foundational approach to integrate diverse agricultural domains when developing sustainable intensification projects.

Background: Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training. Results: We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools. Conclusions: OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists. [ABSTRACT FROM AUTHOR]

Statistical tables associated with named probability distributions and their families, such as the standard normal, Student's t, and chi-square tables, among others, have been utilized for years and are still widely used today, especially for mathematics and statistics education. While such tables can be found in many statistics textbooks and even on the internet, various shortcomings exist regarding the process of obtaining and distributing tables, as well as the content and appearance of some. In this article, a description is provided of a free, web-based Shiny application created by the author to address these shortcomings and which enables users to generate common statistical tables that are simple and quick to make, customizable, and that users may download and distribute without fear of committing copyright infringement. [ABSTRACT FROM AUTHOR]

Abstract Background Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. Results PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Conclusions We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G′ and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the ‘plotly’ and ‘DT’ packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as ‘rle’ and ‘read.csv’ with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.

Introduction: A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills. Objectives: The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys. Methods: The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children's food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables. Results: The dashboard layout is structured by the YAML (YAML Ain't Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny's render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/. Examples of how to interact with the dashboard for selected oral health variables are illustrated. Conclusion: Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software. [ABSTRACT FROM AUTHOR]

Background: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. Results: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. Conclusions: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE. [ABSTRACT FROM AUTHOR]

Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open‐source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate‐simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud‐computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation. [ABSTRACT FROM AUTHOR]

Assay for transposase-accessible chromatin sequencing (ATAC-seq) has become one of the most widely used sequencing methods in studies of gene regulation, aiming to identify open chromatin sites and decipher how chromatin accessibility regulates gene expression. However, due to a lack of programming experience or minimal bioinformatics training, it is difficult for biologists to fully explore and interpret ATAC-seq results. Despite several available programs or websites that allow researchers to analyze and visualize ATAC-seq data, several limitations exist. ATAC-seq data differential expression analysis (ATAC-DEA), a web application that facilitates the exploration and visualization of differential peak analysis and annotation from ATAC-seq data, was developed (www.atac-dea.xyz:3838/ATAC-DEA). ATAC-DEA uses DiffBind and ChIPpeakAnno to process differential peak and annotation analysis results. ATAC-DEA has five features: (1) runs on a web server; (2) processes three files into one small file, which is used as the input for ATAC-DEA; (3) availability of various downloadable plots; (4) multifactor analysis and customized contrast model; and (5) annotates individual, overlapped, and differential peaks. It provides an easy-to-use user interface (UI) design for users to explore the data and modify the parameters interactively based on experimental purposes. ATAC-DEA allows biologists to generate user-friendly visual results from ATAC-seq downstream analysis. [ABSTRACT FROM AUTHOR]

Common methods for Bayesian prior elicitation call for expert belief in the form of numerical summaries. However, certain challenges remain with such strategies. Drawing on recent advances made in graphical inference, we propose an interactive method and tool for prior elicitation in which experts express their belief through a sequence of selections between pairs of graphics, reminiscent of the common procedure used during eye examinations. The graphics are based on synthetic datasets generated from underlying prior models with carefully chosen parameters, instead of the parameters themselves. At each step of the process, the expert is presented with two familiar graphics based on these datasets, billed as hypothetical future datasets, and makes a selection regarding their relative likelihood. Underneath, the parameters that are used to generate the datasets are generated in a way that mimics the Metropolis algorithm, with the experts' responses forming transition probabilities. Using the general method, we develop procedures for data models used regularly in practice: Bernoulli, Poisson, and Normal, though it extends to additional univariate data models as well. A free, open-source Shiny application designed for these procedures is also available online, helping promote best practice recommendations in myriad ways. The method is supported by simulation. [ABSTRACT FROM AUTHOR]

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the ‘plotly’ and ‘DT’ packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as ‘rle’ and ‘read.csv’ with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.

Rhythmic feeding activity has become an important research area for circadian biologists as it is now clear that metabolic input is critical for regulating circadian rhythms, and chrononutrition has been shown to promote health span. In contrast to locomotor activity rhythm, studies conducting high throughput analysis of Drosophila rhythmic food intake have been limited and few monitoring system options are available. One monitoring system, the Fly Liquid-Food Interaction Counter (FLIC) has become popular, but there is a lack of efficient analysis toolkits to facilitate scalability and ensure reproducibility by using unified parameters for data analysis. Here, we developed Circadian Rhythm Using Mealtime Behavior (CRUMB), a user-friendly Shiny app to analyze data collected using the FLIC system. CRUMB leverages the ‘plotly’ and ‘DT’ packages to enable interactive raw data review as well as the generation of easily manipulable graphs and data tables. We used the main features of the FLIC master code provided with the system to retrieve feeding events and provide a simplified pipeline to conduct circadian analysis. We also replaced the use of base functions in time-consuming processes such as ‘rle’ and ‘read.csv’ with faster versions available from other packages to optimize computing time. We expect CRUMB to facilitate analysis of feeding-fasting rhythm as a robust output of the circadian clock.