Shiny Application Research Articles

On the ability to extract MLVA profiles of Vibrio cholerae isolates from WGS data generated with Oxford Nanopore Technologies

ObjectiveMultiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) is widely used to subtype pathogens causing foodborne and waterborne disease outbreaks. The MLVAType shiny application was previously designed to extract MLVA profiles of Vibrio cholerae isolates from whole-genome sequencing (WGS) data, and provide backward compatibility with traditional MLVA typing methods. The previous development and validation work was conducted using short (pair-end 300 and 150 nt long) reads from Illumina MiSeq and Hiseq sequencing. In this study, the MLVAType application was validated using long reads generated by Oxford Nanopore Technologies (ONT) sequencing platforms. In silico MLVA profiles of V. cholerae isolates (n = 9) from the Democratic Republic of the Congo were generated using the MLVAType application on Nanopore WGS data. The WGS-derived in silico MLVA profiles were extracted from Canu (v.2.2) assemblies obtained through MinION and GridION sequencing by ONT. The results were compared to those obtained from SPAdes assemblies (v3.13.0; k-mer 175) generated from short-read (pair-end 300-bp) reference data obtained by MiSeq sequencing, Illumina.ResultsFor each isolate, the in silico MLVA profiles were concordant across all three sequencing methods, demonstrating that the MLVAType application can accurately predict the MLVA profiles from assembled genomes generated by long-reads ONT sequencers.

Sherlock-Genome: an R Shiny application for genomic analysis and visualization

MotivationNext-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioinformaticians, hindering the usability of WGS data for biological discovery.ResultsWe developed Sherlock-Genome, an R Shiny app for data harmonization, visualization, and integrative analysis of WGS-based cancer genomics studies. Following FAIR principles, Sherlock-Genome provides a platform and guidelines for managing and sharing finalized sample-level WGS analysis results, enabling users to upload results, inspect analyses locally, and perform integrative analyses. It includes modules for major cancer genomic analyses, allowing interactive data visualizations and integrative analyses with other data types. Sherlock-Genome supports both local and cloud deployment, facilitating the sharing of results for related publications. This tool has the potential to be widely adopted in cancer genomics, significantly enhancing the accessibility and usability of sample-level WGS analysis results for comprehensive biological discovery and research advancements.Availability and implementationThe source code and installation instructions for Sherlock-Genome can be accessed via Github https://github.com/xtmgah/Sherlock-Genome. Documentation and data requirements for user project data can also be found on the same GitHub page.

How to Put Survival After Cardiothoracic Interventions in the General Population Context: A Case-Based Practical Guideline to Calculate Cumulative Matched-General-Population Survival.

Observed patient survival after cardiothoracic interventions should ideally be placed in the context of matched-general-population survival. This study outlines several methodologies of matching general population mortality to the study sample, subsequently calculating cumulative matched-general-population survival, highlighting their respective advantages, disadvantages, and limitations. A multicenter data set containing survival data after the Ross procedure was used for methodological illustration. General population mortality was extracted from country-specific life tables in the Human Mortality Database. The matched-general-population mortality, also known as background mortality, was obtained by matching general population mortality to the study sample using different matching strategies, iteratively considering median/individual age and median/individual calendar year, besides country and sex. The corresponding cumulative matched-general-population survival was calculated subsequently. Sensitivity analyses were performed to assess the impact of varying patient ages on survival estimates by adding and subtracting 15 years from individual patients. A web-based Shiny Application (App) was developed to easily calculate cumulative matched-general-population survival. In total, 1431 hospital survivors from the Ross procedure from 5 countries (25.7% female; median age, 48.5 [interquartile range, 42.7-54.0] years) were included. Fifteen-year survival was 88.3% (95% CI, 85.3-90.6). Cumulative matched-general-population 15-year survival varied from 87.7% to 89.8% using the 3 methods of different complexities. For 15-year-older patients, the cumulative matched-general-population 15-year survival was 67.4%, 59.8%, and 63.2%, respectively, using the simplest to the most comprehensive matching methods; for 15-year-younger patients, it was 96.9%, 96.1%, and 96.7%, respectively. Different methodologies to match general population mortality to observed patient mortality yield variable estimates of cumulative matched-general-population survival, especially in older patients. The cumulative matched-general-population survival should ideally be calculated by considering country, sex, individual patient age, and calendar year (both updated annually). This method can be easily implemented using the web-based Shiny App enclosed in this article.

Application of machine learning in depression risk prediction for connective tissue diseases

This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2019 and December 2023 to develop and validate a multi-classification machine learning (ML) model for assessing depression risk. Addressing the limitations of traditional assessment tools, six ML models were constructed using univariate analysis and the LASSO algorithm, with the categorical boosting (Catboost) model emerging as the best performer, demonstrating strong predictive ability across different depression severity levels (none_F1 = 0.879, mild_F1 = 0.627, moderate and severe_F1 = 0.588). Additionally, the study provided an interpretation of the best-performing model using SHAP and developed a user-friendly R Shiny application (https://macnomogram.shinyapps.io/Catboost/) to facilitate clinical use. The findings suggest that the Catboost model represents a significant advancement in assessing depression risk among CTD patients, highlighting the potential of ML in enhancing mental health management for this patient population.

Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths.

The Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) was launched in 2019 by the World Health Organization and African nations to combat Neglected Tropical Diseases (NTDs), including Soil-transmitted helminths (STH), which still affect over 1.5 billion people globally. In this study, we present a comprehensive geostatistical analysis of publicly available STH survey data from ESPEN to delineate inter-country disparities in STH prevalence and its environmental drivers while highlighting the strengths and limitations that arise from the use of the ESPEN data. To achieve this, we also propose the use of calibration validation methods to assess the suitability of geostatistical models for disease mapping at the national scale. We analysed the most recent survey data with at least 50 geo-referenced observations, and modelled each STH species data (hookworm, roundworm, whipworm) separately. Binomial geostatistical models were developed for each country, exploring associations between STH and environmental covariates, and were validated using the non-randomized probability integral transform. We produced pixel-, subnational-, and country-level prevalence maps for successfully calibrated countries. All the results were made publicly available through an R Shiny application. Among 35 countries with STH data that met our inclusion criteria, the reported data years ranged from 2004 to 2018. Models from 25 countries were found to be well-calibrated. Spatial patterns exhibited significant variation in STH species distribution and heterogeneity in spatial correlation scale (1.14 km to 3,027.44 km) and residual spatial variation variance across countries. This study highlights the utility of ESPEN data in assessing spatial variations in STH prevalence across countries using model-based geostatistics. Despite the challenges posed by data sparsity which limit the application of geostatistical models, the insights gained remain crucial for directing focused interventions and shaping future STH assessment strategies within national control programs.

AGvHDtrackR and cGvHDtrackR: shiny applications for graft versus host disease management and clinical data collection.

Graft-versus-host disease (GvHD) is one of the most common and troublesome complications after allogeneic hematopoietic stem cell transplantation (HSCT). Despite adequate GvHD prophylaxis, 30-50% of the patients still develop acute or chronic GvHD, often requiring multiple lines of therapy. Therefore, it is crucial to closely monitor the onset and the response of GvHD to therapies to identify the best available treatment for each patient. Currently, some applications (desktop or mobile) that allow to score GvHD severity at the bedside are available. However, none of the published systems is designed to record ongoing therapies and to upload data in a database, which can support both the clinical decision-making process as well as data collection. To this aim, we developed two Shiny apps: aGvHDtrackR for acute GvHD and cGvHDtrackR for chronic GvHD. These applications record GvHD grading alongside the therapies used for each patient and allow to export of the data in a longitudinal patient-specific database. This is of help for the clinical management of patients and for future multicentric studies on GvHD.

ShinyGS-a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations.

Genomic prediction is a powerful approach for improving genetic gain and shortening the breeding cycles in animal and crop breeding programs. A series of statistical and machine learning models has been developed to increase the prediction performance continuously. However, the application of these models requires advanced R programming skills and command-line tools to perform quality control, format input files, and install packages and dependencies, posing challenges for breeders. Here, we present ShinyGS, a stand-alone R Shiny application with a user-friendly interface that allows breeders to perform genomic selection through simple point-and-click actions. This toolkit incorporates 16 methods, including linear models from maximum likelihood and Bayesian framework (BA, BB, BC, BL, and BRR), machine learning models, and a data visualization function. In addition, we benchmarked the performance of all 16 models using multiple populations and traits with varying populations and genetic architecture. Recommendations were given for specific breeding applications. Overall, ShinyGS is a platform-independent software that can be run on all operating systems with a Docker container for quick installation. It is freely available to non-commercial users at Docker Hub (https://hub.docker.com/r/yfd2/ags).

A comprehensive guide to study the agreement and reliability of multi-observer ordinal data

BackgroundA recent systematic review revealed issues in regard to performing and reporting agreement and reliability studies for ordinal scales, especially in the presence of more than two observers. This paper therefore aims to provide all necessary information in regard to the choice among the most meaningful and most used measures and the planning of agreement and reliability studies for ordinal outcomes.MethodsThis paper considers the generalisation of the proportion of (dis)agreement, the mean absolute deviation, the mean squared deviation and weighted kappa coefficients to more than two observers in the presence of an ordinal outcome.ResultsAfter highlighting the difference between the concepts of agreement and reliability, a clear and simple interpretation of the agreement and reliability coefficients is provided. The large sample variance of the various coefficients with the delta method is presented or derived if not available in the literature to construct Wald confidence intervals. Finally, a procedure to determine the minimum number of raters and patients needed to limit the uncertainty associated with the sampling process is provided. All the methods are available in an R package and a Shiny application to circumvent the limitations of current software.ConclusionsThe present paper completes existing guidelines, such as the Guidelines for Reporting Reliability and Agreement Studies (GRRAS), to improve the quality of reliability and agreement studies of clinical tests. Furthermore, we provide open source software to researchers with minimum programming skills.

Evaluation of the safety and efficacy of stereotactic body radiotherapy for radio-recurrent prostate cancer: a systematic review and meta-analysis.

Stereotactic body radiotherapy (SBRT) is pivotal in managing radio-recurrent prostate cancer (PCa). This study aims to comprehensively review its efficacy and associated severe toxicities. A thorough review of PubMed and EMBASE databases up to July 2024 was conducted to assess recurrence-free survival (RFS) with salvage SBRT across various subgroups. Survival curves were reconstructed using WebPlotDigitizer and a newly developed shiny application. Thirty-six studies were analyzed, with 15 papers (682 patients) contributing to survival curve reconstruction. Median RFS was 36.2 months, with 2-, 3-, and 5-year rates of 64.8%, 50.7%, and 40.6%, respectively. Factors associated with improved RFS included whole-gland irradiation [focal vs. whole, hazard ratio (HR) 1.83 (95% CI: 1.16-2.87), p = 0.008], and higher biologically effective dose (BED) [120-138.1 Gy vs. 144-167.7 Gy, HR 1.40 (95% CI: 1.07-1.83), p = 0.015]. Severe (grade ≥ 3) acute and late genitourinary (GU) toxicities occurred in 1.4% (95% CI: 0.8-2.3) and 3.7% (95% CI: 2.6-4.9) of patients, respectively. Severe acute and late gastrointestinal (GI) toxicities were reported in 0.5% (95% CI: 0.2-1.1) and 0.4% (95% CI: 0.1-1.0) of patients, respectively. Combined severe GU and GI toxicities were observed in 5.8% (95% CI: 4.5-7.4) and 1.3% (95% CI: 0.7-2.2) of patients, respectively. This study provides a comprehensive assessment of toxicities and conducts a pooled analysis of RFS for salvage SBRT in radio-recurrent PCa. Factors such as whole-gland irradiation, and higher BED show promise as prognostic indicators for RFS. However, confirmation through randomized controlled trials is essential due to the low levels of evidence and study heterogeneity.

FAfA: Factor Analysis for All An R Package to Conduct Factor Analysis with RShiny Application

This paper presents a presentation of FAfA (the R Shiny application), which was specifically developed for performing complete factor analysis processes. These procedures include data wrangling, assumption checks for exploratory and confirmatory factor analysis, reliability analysis, exploratory graphic analysis, and item weighting. The objective of the paper is to provide users with clear instructions on how to effectively use the FAfA package, therefore guaranteeing precise and consistent outcomes in their research. The FAfA application's primary goal is to integrate EFA and CFA into a single software. In addition, the FAfA application has the capability to do reliability analysis and item weighing.

A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication.

Short tandem repeat (STR) profiling is commonly performed for authentication of biomedical models of human origin, yet no tools exist to easily compare sets of STR profiles to each other or an existing database in a high-throughput manner. Here, we present STRprofiler, a Python package, command line tool, and Shiny application providing methods for STR profile comparison and cross-contamination detection. STRprofiler can be run with custom databases or used to query against the Cellosaurus cell line database. STRprofiler is freely available as a Python package with a rich CLI from PyPI https://pypi.org/project/strprofiler/ with source code available under the MIT license on GitHub https://github.com/j-andrews7/strprofiler and at https://zenodo.org/records/10989034. A web server hosting an example STRprofiler Shiny application backed by a database with data from the National Cancer Institute-funded PDXNet consortium and The Jackson Laboratory PDX program is available at https://sj-bakerlab.shinyapps.io/strprofiler/. Full documentation is available at https://strprofiler.readthedocs.io/en/latest/.

OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data.

Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires in vivo experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.

NeuroVar: an open-source tool for the visualization of gene expression and variation data for biomarkers of neurological diseases.

The expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. Here, we developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. NeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. Data collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. NeuroVar provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the project's GitHub repository (https://github.com/omicscodeathon/neurovar).

Mapping of Health System Performance Indicators to the WHO HSPA Framework.

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland's high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.

NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy

SummaryNear-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/.NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user’s choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development.NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.

M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.

Fast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency, and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general. The software tool, code, and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541.

IModMix: Integrative Module Analysis for Multi-omics Data.

Shiny application: https://imodmix.moffitt.org. The R package and source code: https://github.com/biodatalab/iModMix.

A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects.

We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.

Margaret: Streamlining Research Productivity Analysis in Colombia with an R Package for GrupLAC Integration

Margaret is an advanced R package designed to systematically extract and consolidate data pertaining to research outputs (such as publications, books, book chapters, and conference presentations) of scientific groups from the GrupLAC platform, an online application managed by Minciencias in Colombia for the registration and updating of researcher and research group information in the fields of science, technology, and innovation. The challenge of monitoring and evaluating scientific production across various web platforms presents a substantial barrier to universities in tracking their contributions effectively. To address this challenge, Margaret accepts a designated list of links corresponding to university-affiliated research groups within GrupLAC. Utilizing web-scraping techniques, the package retrieves and compiles this data into a comprehensive XLSX file. This file encompasses information across 51 distinct categories of research products, enabling research directors to meticulously assess, monitor, and enhance various strategies that aim to augment the production, quality, and impact of scientific outputs. The Shiny application is publicly accessible and can be accessed via the following link: https://ucatolicaluisamigo-investigaciones.shinyapps.io/margaret/