Research output from inter- and trans-disciplinary research projects is challenging to store, link, analyze, and communicate among stakeholders and researchers from other fields. Datasets, models, ...and methods are often non-interactive, hard to access and understand, and hidden in research articles with specific terminologies. Here, we present GRETA Analytics, an R Shiny application developed to communicate the output of the GRETA (GReen Energy Transition Actions) EU Horizon 2020 research project. The tool presents and visualizes data collected from qualitative case studies, an extensive multinational survey in 16 EU countries, their re-use in scenario predictions, and their framing for developing interactive transition pathways for individuals and communities. With a strong geospatial focus and a narrative design, the tool promotes collaborative sense-making processes of the real world instead of plain visualization. This paper addresses the development process, design choices, data integration strategies, architecture details, and deployment methods of the tool. We demonstrate how using collaborative and interactive dashboards can enhance the communication of research projects and support a geographical understanding of energy citizenship and energy transition actions, which were the overall goals of the GRETA project.
Abstract
Required sample sizes for a study need to be carefully assessed to account for logistics, cost, ethics and statistical rigour. For example, many studies have shown that methodological ...variations can impact the critical thermal limits (CTLs) recorded for a species, although studies on the impact of sample size on these measures are lacking. Here, we present
ThermalSampleR
; an R CRAN package and Shiny application that can assist researchers in determining when adequate sample sizes have been reached for their data. The method is particularly useful because it is not taxon specific. The Shiny application offers a user‐friendly interface equivalent to the package for users not familiar with R programming.
ThermalSampleR
is accompanied by an in‐built example dataset, which we use to guide the user through the workflow with a fully worked tutorial.
Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of ...the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
•An ICA-SERS method was developed for the simultaneous duplex detection of two pyrethroid pesticides.•ICA based on competitive immunosorbence shows efficiency for detecting small pesticide ...molecules.•This method is a multiplexed, rapid, and economic way for on spot pesticide detection in agriculture.•R Shiny APP shows its potential in streamlining all the data treatment and analyses.
Here we describe a surface-enhanced Raman scattering (SERS)-based immunochromatographic assay (ICA) method for the dual detection of two pyrethroid pesticides cypermethrin and esfenvalerate. In the proposed ICA-SERS methods, antibody conjugated gold nanoparticles (AuNPs) were used as the SERS substrate while fluorescence tags labeled on AuNPs were used as the Raman reporters for SERS. Through the immuno-specific combination of antigen and antibody, Raman reporter labeled AuNPs were immobilized on the test line of the ICA strip and facilitated the SERS measurement. And by immobilizing two test lines designed for the detection of two pesticides, a simultaneous dual detection was accomplished. The LODs of the ICA-SERS system was 2.3 × 10−4 and 2.6 × 10-5 ng/mL for cypermethrin and esfenvalerate, respectively. The sensitivity was 3 to 4 times higher than ELISA and fluorescent-based ICA methods using the same indirect competitive immunoassay methods. For the three types of samples (tap water, river water, and milk), the recoveries ranged from 94.9% to 112%, with RSD values ranged from 1.3% to 13.5%. The research reveals that this ICA-SERS method possesses high specificity, sensitivity, and reproducibility, and can be reliably used to detect dual pesticides in real samples. An R Shiny-based application 2plex-speclysis was built and used for all the data analyses in this paper. The application was designed to incorporate all the analyses including spectral preprocessing, performance evaluation, and PCA analysis, and make the spectral analysis accessible for people without programming experience.
Abstract
Motivation
DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information ...(non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD.
Implementation
ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure.
General features
The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration).
Availability
ShinyDataSHIELD is publicly hosted online https://datashield-demo.obiba.org/, the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available https://hub.docker.com/r/brgelab/shiny-data-shield.
Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression ...databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills.
We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses.
Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .
In plants, C-to-U RNA editing mainly occurs in plastid and mitochondrial transcripts, which contributes to a complex transcriptional regulatory network. More evidence reveals that RNA editing plays ...critical roles in plant growth and development. However, accurate detection of RNA editing sites using transcriptome sequencing data alone is still challenging. In the present study, we develop PlantC2U, which is a convolutional neural network, to predict plastid C-to-U RNA editing based on the genomic sequence. PlantC2U achieves >95% sensitivity and 99% specificity, which outperforms the PREPACT tool, random forests, and support vector machines. PlantC2U not only further checks RNA editing sites from transcriptome data to reduce possible false positives, but also assesses the effect of different mutations on C-to-U RNA editing based on the flanking sequences. Moreover, we found the patterns of tissue-specific RNA editing in the mangrove plant Kandelia obovata, and observed reduced C-to-U RNA editing rates in the cold stress response of K. obovata, suggesting their potential regulatory roles in plant stress adaptation. In addition, we present RNAeditDB, available online at https://jasonxu.shinyapps.io/RNAeditDB/. Together, PlantC2U and RNAeditDB will help researchers explore the RNA editing events in plants and thus will be of broad utility for the plant research community.
Whole-farm mathematical programming (MP) models help to understand the complex implications of farm planning decisions taking the full agricultural system into account. Applying optimization models ...in direct interaction with farmers goes beyond leading to improved recommendations for mixed crop-livestock farm systems. The tacit farmer knowledge revealed in such interaction sessions can also provide valuable input for descriptive and predictive models of farmer behavior in scientific analyses of agricultural policy or technology adoption. To date, a conceptual and technical knowledge divide has prevented the widespread use of MP in interactive modeling with farmers in the field.
This article revisits the rationale for interactive MP modeling and explores whether it can be upheld in practice. We evaluate whether a lightweight graphical interface that can be quickly adapted to practical applications of whole-farm planning can help bridge the gap between models and users.
We present a user interface for whole-farm MP models implemented in the open-source statistical programming language R. We apply the tool in a participatory research process in Paraguay, analyzing the opportunities and barriers to adoption of new agroforestry options for smallholder farmers. Using in-depth interviews and participant feedback, we evaluate the tool's accessibility, credibility and relevance as well as its contribution to bridging knowledge gaps in the overall research process.
In our explorative case study, interactive MP modeling provided essential insights into the local farming system and was highly accepted by smallholder farmers and extension workers. We observed a high interest in structured farm analysis, planning and education for smallholder agriculture. The lightweight user interface contributed to efficient and successful interaction between models and users.
Interactive modeling helps to improve the application of whole-farm MP modeling in agricultural systems analysis. Case study examples and a quickly customizable interface can lower the barriers for using interactive MP modeling with farmers and other stakeholders. With the recent wave of digitalization in agriculture, the insights obtained here are also relevant for the development of digital farm management tools for systematic whole-farm planning.
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•Whole-farm modeling is hampered by the gap between models and users regarding accessibility, credibility and relevance•We test if Interactive whole-farm optimization models can be effective for knowledge-bridging in a participatory context•We use a lightweight model-user interface implemented with an open-source adaptable software solution•Our study shows that interactive modeling tools are well-accepted by smallholder farmers and extension service officers•Interactive modeling sessions can elicit tacit farmer knowledge and provide valuable insights for participatory research
Chronic obstructive pulmonary disease (COPD) is a common disease that accounts for a significant individual and societal burden. Pulmonary rehabilitation (PR) is a key management strategy but it is ...highly inaccessible, making prioritisation highly needed. This study aimed to determine and optimize predictive models of PR outcomes and build a tool to help healthcare professionals in their clinical decision-making about PR prioritisation. Data from patients who performed a 12-week community-based PR programme were analysed. Exercise capacity with the six-minutes walk test distance (6MWD), isometric quadriceps muscle strength with the handheld dynamometry (QMS) and dyspnoea with the modified Medical Research Council dyspnoea scale (mMRC) were assessed before and after PR. Multiple linear regression models were determined based on the Akaike information criteria and a cross-validation method. The resultant multiobjective problem was solved using the Nondominated Sorting Genetic Algorithm-II. R Shiny package was used to create a web-based user interface. Data from 95 patients with COPD (median age of 69 years, 19 female and generally overweight), resulted in linear predictive models for the post-pre difference of the 6MWD, QMS and mMRC with cross-validation R2 of 0.49, 0.53 and 0.51, respectively. 6MWD and mMRC were common statistically significant predictors. Pareto front patients were obese ex-smoker women that do not do long-term oxygen therapy and that performed PR. The distance to the Pareto front along with the estimates given by our models are easily obtained using the designed R Shiny interface and may help healthcare professionals decide on the prioritisation to PR programmes.
When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. ...However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a simulation-based method and an easy-to-use web application (
https://xuqin.shinyapps.io/CausalMediationPowerAnalysis/
) for power and sample size calculations for regression-based causal mediation analysis. By repeatedly drawing samples of a specific size from a population predefined with hypothesized models and parameter values, the method calculates the power to detect a causal mediation effect based on the proportion of the replications with a significant test result. The Monte Carlo confidence interval method is used for testing so that the sampling distributions of causal effect estimates are allowed to be asymmetric, and the power analysis runs faster than if the bootstrapping method is adopted. This also guarantees that the proposed power analysis tool is compatible with the widely used R package for causal mediation analysis, mediation, which is built upon the same estimation and inference method. In addition, users can determine the sample size required for achieving sufficient power based on power values calculated from a range of sample sizes. The method is applicable to a randomized or nonrandomized treatment, a mediator, and an outcome that can be either binary or continuous. I also provided sample size suggestions under various scenarios and a detailed guideline of app implementation to facilitate study designs.
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