PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Explainable machine learning framework to predict the risk of work-related neck and shoulder musculoskeletal disorders among healthcare professionals.

Objective: This study aims to develop risk prediction models for neck and shoulder musculoskeletal disorders among healthcare professionals. Methods: A stratified sampling method was employed to select employees from medical institutions in Nanning City, yielding 617 samples. The Boruta algorithm was used for feature selection, and various models, including Tree-Based Models, Single Hidden-Layer Neural Network Models (MLP), Elastic Net Models (ENet), and Support Vector Machines (SVM), were applied to predict the selected variables, utilizing SHAP algorithms for individual-level local explanations. Results: The SVM model excels in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and exhibits more stable performance when generalizing to unseen data. The Random Forest model exhibited relatively high overall performance on the training set. The MLP model emerges as the most consistent and accurate in predicting shoulder musculoskeletal disorders, while the SVM model shows strong fitting capabilities during the training phase, with occupational factors identified as the main contributors to WMSDs. Conclusion: This study successfully constructs work-related musculoskeletal disorder risk prediction models for healthcare professionals, enabling a quantitative analysis of the impact of occupational factors. This advancement is beneficial for future economical and convenient work-related musculoskeletal disorder screening in healthcare professions.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis.

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Steroid-tacrolimus drug-drug interaction and the effect of CYP3A genotypes.

AIMS: Tacrolimus, metabolized by CYP3A4 and CYP3A5 enzymes, is susceptible to drug-drug interactions (DDI). Steroids induce CYP3A genes to increase tacrolimus clearance, but the effect is variable. We hypothesized that the extent of the steroid-tacrolimus DDI differs by CYP3A4/5 genotypes. METHODS: Kidney transplant recipients (n = 2462) were classified by the number of loss of function alleles (LOF) (CYP3A5*3, *6 and *7 and CYP3A4*22) and steroid use at each tacrolimus trough in the first 6 months post-transplant. A population pharmacokinetic analysis was performed by nonlinear mixed-effect modelling (NONMEM) and stepwise covariate modelling to define significant covariates affecting tacrolimus clearance. A stochastic simulation was performed and translated into a Shiny application with the mrgsolve and Shiny packages in R. RESULTS: Steroids were associated with modestly higher (3%-11.8%) tacrolimus clearance. Patients with 0-LOF alleles receiving steroids showed the greatest increase (11.8%) in clearance compared to no steroids, whereas those with 2-LOFs had a negligible increase (2.6%) in the presence of steroids. Steroid use increased tacrolimus clearance by 5% and 10.3% in patients with 1-LOF and 3/4-LOFs, respectively. CONCLUSIONS: Steroids increase the clearance of tacrolimus but vary slightly by CYP3A genotype. This is important in individuals of African ancestry who are more likely to carry no LOF alleles, may more commonly receive steroid treatment, and will need higher tacrolimus doses.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

PROpwr: a Shiny R application to analyze patient-reported outcomes data and estimate power.

Patient Reported Outcomes (PROs) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. We propose this R shiny web application, PROpwr, that estimates power for two-arm clinical trials with PRO measures as endpoints using Item Response Theory (GRM: Graded Response Model) and simulations. PROpwr also supports the analysis of PRO data for convenience of estimating the effect size. There are seven function tabs in PROpwr: Frequentist Analysis, Bayesian Analysis, GRM power, T-test Power Given Sample Size, T-test Sample Size Given Power, Download, and References. PROpwr is user-friendly with point-and-click functions. PROpwr can assist researchers to analyze and calculate power and sample size for PRO endpoints in clinical trials without prior programming knowledge.

Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app.

BACKGROUND: Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity. METHODS: By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots. RESULTS: In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting. CONCLUSION: While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data.

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from the WARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

Sample size and power calculations for causal mediation analysis: A Tutorial and Shiny App.

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.

Age and Sex Distributions of 31 Common Racial Groups in Hawai'i: A Shiny Web Application.

Hawai'i is the most ethnically diverse state with the highest proportion of multiracial individuals in the United States. The Stepwise Proportional Weighting Algorithm (SPWA) was developed to bridge the categorization of multiracial Census data into single-race population estimates for common races in Hawai'i. However, these estimates have not been publicly available. A Shiny web application, the Hawai'i Single-Race Categorization Tool, was developed as a user friendly research tool to obtain the age and sex distributions of single-race estimates for common racial groups in Hawai'i. The Categorization Tool implements the SPWA and presents the results in tabular and graphic formats, stratified by sex and age. It also allows the categorization of partial Native Hawaiians as Native Hawaiians in the population estimation. Using this tool, the current paper reports population estimates and distributions for 31 common racial groups using Hawai'i Census 2010 data. Among the major Census races, Asian had the largest population (631 881; 46.5%) in Hawai'i, followed by White (431 635; 31.7%) and Native Hawaiian and Other Pacific Islander (227 588; 16.7%). Among Census detailed races within Asian, Filipino had the largest population estimate (244 730; 18.0%), followed by Japanese (227 165; 16.7%) and Chinese (103 600; 7.6%). Native Hawaiian accounted for 12.3% of the Hawai'i population (166 944). After recategorizing part-Native Hawaiians as Native Hawaiians, Native Hawaiian increased by 150.0%, with the greatest increase among the young. This publicly available tool would be valuable for race-related resource allocation, policy development, and health disparities research in Hawai'i.

NEAL: an open-source tool for audio annotation.

Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.

Efficacy and safety of anti-amyloid-ß monoclonal antibodies in current Alzheimer's disease phase III clinical trials: A systematic review and interactive web app-based meta-analysis.

The risk-benefit profile of anti-Aß monoclonal antibodies (mAbs) in Alzheimer's disease (AD) remains unclear, especially concerning their safety and overall effects on AD progression and cognitive function. Here, we investigated cognitive, biomarker and side effects of anti-Aß mAbs in large phase III randomized placebo-controlled clinical trials (RCTs) in sporadic AD. The search was performed on Google Scholar, PubMed and ClinicalTrials.gov by applying Jadad score to evaluate the methodological quality of the reports. Studies were excluded if they scored < 3 on Jadad scale or if they analyzed less than 200 sporadic AD patients. We followed PRISMA guidelines and DerSimonian-Laird random-effects model in R. Primary outcomes were cognitive: AD Assessment Scale-Cognitive Subscale (ADAS-Cog), Mini Mental State Examination (MMSE) and Clinical Dementia Rating Scale-sum of Boxes (CDR-SB). Secondary and tertiary outcomes included biomarkers of Aß and tau pathology, adverse events, and performance on Alzheimer's Disease Cooperative Study - Activities of Daily Living Scale. The meta-analysis included 14,980 patients in 14 studies and four mAbs: Bapineuzumab, Aducanumab, Solanezumab and Lecanemab. The results of this study suggest that anti-Aß mAbs statistically improved cognitive and biomarker outcomes, particularly Aducanumab and Lecanemab. However, while cognitive effects were of small effect sizes, these drugs considerably increased risk of side effects such as Amyloid Related Imaging Abnormalities (ARIA), especially in APOE-ε4 carriers. Meta-regression revealed that higher (better) baseline MMSE score was associated with improved ADAS Cog and CDR-SB. In order to improve reproducibility and update the analysis in the future, we developed AlzMeta.app, web-based application freely available at https://alzmetaapp.shinyapps.io/alzmeta/.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

The SAlzburg PEritoneal SUrface CAlculator (SAPESUCA): The First Web-Based Application for Peritoneal Surface Area Quantification.

(1) Background: Peritoneal metastasized colorectal cancer is associated with a worse prognosis. The combination of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) showed promising results in selected patients, but standardization is lacking so far. We present the first tool enabling standardized peritoneal surface area (PSA) quantification in patients undergoing CRS and HIPEC: The SAlzburg PEritoneal SUrface CAlculator (SAPESUCA). (2) Methods: SAPESUCA was programmed using the R-Shiny framework. The application was validated in 23 consecutive colon cancer patients who received 27 closed oxaliplatin-based HIPECs between 2016 and 2020. The programming algorithm incorporates the patient's body surface area and its correlated peritoneal surface area (PSA) based on the 13 Peritoneal Cancer Index (PCI) regions. (3) Results: Patients' median age was 56 years. Median PCI was 9. SAPESUCA revealed a mean PSA of 18,613 cm2 ± 1951 of all patients before compared to 13,681 cm2 ± 2866 after CRS. The Central PCI region revealed the highest mean peritonectomy extent (1517 cm2 ± 737). The peritonectomy extent correlated significantly with PCI score and postoperative morbidity. The simulated mean oxaliplatin dose differed significantly before and after CRS (558 mg/m2 ± 58.4 vs. 409 mg/m2 ± 86.1; p < 0.0001). (4) Conclusion: SAPESUCA is the first free web-based app for standardized determination of the resected and remaining PSA after CRS. The tool enables chemotherapeutic dose adjustment to the remaining PSA.

MSMpred: interactive modelling and prediction of individual evolution via multistate models.

BACKGROUND: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making easier to work with those models. RESULTS: MSMpred is a web tool created with the shiny R package that has two main features: 1) to allow to fit a MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be upload in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient' length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject's evolution such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. CONCLUSIONS: MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.

SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.

SEQUIN is a web-based application (app) that allows fast and intuitive analysis of RNA sequencing data derived for model organisms, tissues, and single cells. Integrated app functions enable uploading datasets, quality control, gene set enrichment, data visualization, and differential gene expression analysis. We also developed the iPSC Profiler, a practical gene module scoring tool that helps measure and compare pluripotent and differentiated cell types. Benchmarking to other commercial and non-commercial products underscored several advantages of SEQUIN. Freely available to the public, SEQUIN empowers scientists using interdisciplinary methods to investigate and present transcriptome data firsthand with state-of-the-art statistical methods. Hence, SEQUIN helps democratize and increase the throughput of interrogating biological questions using next-generation sequencing data with single-cell resolution.

Volcanic-associated ecosystems of the Mediterranean Sea: a systematic map and an interactive tool to support their conservation.

Background: Hydrothermal vents, cold seeps, pockmarks and seamounts are widely distributed on the ocean floor. Over the last fifty years, the knowledge about these volcanic-associated marine ecosystems has notably increased, yet available information is still limited, scattered, and unsuitable to support decision-making processes for the conservation and management of the marine environment. Methods: Here we searched the Scopus database and the platform Web of Science to collect the scientific information available for these ecosystems in the Mediterranean Sea. The collected literature and the bio-geographic and population variables extracted are provided into a systematic map as an online tool that includes an updated database searchable through a user-friendly R-shiny app. Results: The 433 literature items with almost one thousand observations provided evidence of more than 100 different volcanic-associated marine ecosystem sites, mostly distributed in the shallow waters of the Mediterranean Sea. Less than 30% of these sites are currently included in protected or regulated areas. The updated database available in the R-shiny app is a tool that could guide the implementation of more effective protection measures for volcanic-associated marine ecosystems in the Mediterranean Sea within existing management instruments under the EU Habitats Directive. Moreover, the information provided in this study could aid policymakers in defining the priorities for the future protection measures needed to achieve the targets of the UN Agenda 2030.

MALDImID: Spatialomics R package and Shiny app for more specific identification of MALDI imaging proteolytic peaks using LC-MS/MS-based proteomic biomarker discovery data.

Matrix-assisted laser desorption/ionization (MALDI) imaging of proteolytic peptides from formalin-fixed paraffin embedded (FFPE) tissue sections could be integrated in the portfolio of molecular pathologists for protein localization and tissue classification. However, protein identification can be very tedious using MALDI-time-of-flight (TOF) and post-source decay (PSD)-based fragmentation. Hereby, we implemented an R package and Shiny app to exploit liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic biomarker discovery data for more specific identification of peaks observed in bottom-up MALDI imaging data. The package is made available under the GPL 3 license. The Shiny app can directly be used at the following address: https://biosciences.shinyapps.io/Maldimid.