Shiny Web Application Research Articles

Association Between the LZTFL1 rs11385942 Polymorphism and COVID-19 Severity in Colombian Population.

Genetic and non-genetic factors are responsible for the high interindividual variability in the response to SARS-CoV-2. Although numerous genetic polymorphisms have been identified as risk factors for severe COVID-19, these remain understudied in Latin-American populations. This study evaluated the association of non-genetic factors and three polymorphisms: ACE rs4646994, ACE2 rs2285666, and LZTFL1 rs11385942, with COVID severity and long-term symptoms by using a case-control design. The control group was composed of asymptomatic/mild cases (n = 61) recruited from a private laboratory, while the case group was composed of severe/critical patients (n = 63) hospitalized in the Hospital Universitario Mayor-Méderi, both institutions located in Bogotá, Colombia. Clinical follow up and exhaustive revision of medical records allowed us to assess non-genetic factors. Genotypification of the polymorphism of interest was performed by amplicon size analysis and Sanger sequencing. In agreement with previous reports, we found a statistically significant association between age, male sex, and comorbidities, such as hypertension and type 2 diabetes mellitus (T2DM), and worst outcomes. We identified the polymorphism LZTFL1 rs11385942 as an important risk factor for hospitalization (p < 0.01; OR = 5.73; 95% CI = 1.2–26.5, under the allelic test). Furthermore, long-term symptoms were common among the studied population and associated with disease severity. No association between the polymorphisms examined and long-term symptoms was found. Comparison of allelic frequencies with other populations revealed significant differences for the three polymorphisms investigated. Finally, we used the statistically significant genetic and non-genetic variables to develop a predictive logistic regression model, which was implemented in a Shiny web application. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC = 0.86; 95% confidence interval 0.79–0.93). These results suggest that LZTFL1 rs11385942 may be a potential biomarker for COVID-19 severity in addition to conventional non-genetic risk factors. A better understanding of the impact of these genetic risk factors may be useful to prioritize high-risk individuals and decrease the morbimortality caused by SARS-CoV2 and future pandemics.

Abstract 1909: Comparison of mutational burden hotspots in cutaneous squamous cell carcinoma and UV-exposed healthy skin for development of optimal targeted sequencing panels

Abstract Cutaneous squamous cell carcinoma (cSCC) is the second most common human malignancy in the United States. Chronic long-term ultraviolet (UV) exposure drives cSCC development. Early clonal mutation (CM) accumulation is the first step in photocarcinogenesis and is the first known manifestation of field cancerization. Understanding CM accumulation and development is expected to improve the prevention of field cancerization and cSCC. Nevertheless, CMs are poorly understood, and mutations in cSCC have yet to be systematically compared to CM in sun-exposed skin. Key steps for studies to detect and compare CMs are to design optimal targeted sequencing panels and compare mutational hotspot areas. The ideal panel should cover genomic regions containing maximum numbers of mutations within a given number of amplicons. Currently there is no computational tool to help optimize target area selection. Therefore, we aimed to create an algorithm to optimize sequencing target area design for capturing high mutation frequency hotspot areas. An R Shiny web application was created to identify an optimal sequencing target panel from an input mutation dataset and compare the distribution of mutational hotspots. The tool optimizes sequencing target areas based on preset amplicon length by identifying the best fitting panel of amplicons to capture mutations efficiently. Besides identifying optimal sequencing target areas, the developed software also efficiently identifies the most highly mutated genomic areas and compares target area overlaps. The developed algorithm was used to compare the mutational hotspots of cSCC and CM in clinically normal-appearing skin or cSCC from previous publications (cBioPortal, Martincorena et al, Hernando et al, Wei et al, Fowler et al). The current method was more efficient than tested previously available alternative methods by increasing target areas capture efficacy by 1.05 - 8.1-fold. Using the developed tool, we found that frequently mutated areas of CMs in normal skin significantly overlap (p &amp;lt; 0.005) with those of cSCC. Mutational hotspots of normal skin with a history of frequent UV exposure had 1.2-fold greater overlap with cSCC than skin with minimal UV exposure, suggesting the frequently UV-exposed skin carries a greater number of cSCC-related mutations. Although we found that normal skin CM hotspots better predict capture efficiency in sun-exposed normal skin than cSCC mutation hotspots, cSCC mutation hotspots can still design efficient sequencing targets in genomic areas where we do not have deep sequencing data on CM in normal skin. Our work provides a framework for helping design efficient, customized sequencing panels covering the genomic regions with the highest number of mutations. We expect the current algorithm to be a valuable tool for similar studies of other cancers. Citation Format: Sydney R. Grant, Megan E. Fitzgerald, Barbara A. Foster, Wendy J. Huss, Lei Wei, Gyorgy Paragh. Comparison of mutational burden hotspots in cutaneous squamous cell carcinoma and UV-exposed healthy skin for development of optimal targeted sequencing panels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1909.

A living tool for the continued exploration of microplastic toxicity

Throughout the past decade, many studies have reported adverse effects in biota following microplastic exposure. Yet, the field is still emerging as the current understanding of microplastic toxicity is limited. At the same time, recent legislative mandates have required environmental regulators to devise strategies to mitigate microplastic pollution and develop health-based thresholds for the protection of human and ecosystem health. The current publication rate also presents a unique challenge as scientists, environmental managers, and other communities may find it difficult to keep up with microplastic research as it rapidly evolves. At present, there is no tool that compiles and synthesizes the data from these studies to allow for visualization, interpretation, or analysis. Here, we present the Toxicity of Microplastics Explorer (ToMEx), an open access database and open source accompanying R Shiny web application that enables users to upload, search, visualize, and analyze microplastic toxicity data. Though ToMEx was originally created to facilitate the development of health-based thresholds to support California legislations, maintaining the database by the greater scientific community will be invaluable to furthering research and informing policies globally. The database and web applications may be accessed at https://microplastics.sccwrp.org/.Graphical

Population Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface.

BackgroundPopulation size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis.ObjectiveThis study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE.Methodsshinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user’s machine.ResultsThe application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided.ConclusionsThrough a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.

Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling.

Randomized response models (RRMs) aim at increasing the validity of measuring sensitive attributes by eliciting more honest responses through anonymity protection of respondents. This anonymity protection is achieved by implementing randomization in the questioning procedure. On the other hand, this randomization increases the sampling variance and, therefore, increases sample size requirements. The present work aims at countering this drawback by combining RRMs with curtailed sampling, a sequential sampling design in which sampling is terminated as soon as sufficient information to decide on a hypothesis is collected. In contrast to nontruncated sequential designs, the curtailed sampling plan includes the definition of a maximum sample size and subsequent prevalence estimation is easy to conduct. Using this approach, resources can be saved such that the application of RRMs becomes more feasible. An R Shiny web application is provided for simplified application of the proposed procedures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE.

The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer.

Optimizing Measurement Reliability in Within-Person Research: Guidelines for Research Design and R Shiny Web Application Tools

Optimizing Measurement Reliability in Within-Person Research: Guidelines for Research Design and R Shiny Web Application Tools

Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices

Abstract The process of standardizing taxon names, taxonomic name harmonization, is necessary to properly merge data indexed by taxon names. The large variety of taxonomic databases and related tools are often not well described. It is often unclear which databases are actively maintained or what is the original source of taxonomic information. In addition, software to access these databases is developed following non‐compatible standards, which creates additional challenges for users. As a result, taxonomic harmonization has become a major obstacle in ecological studies that seek to combine multiple datasets. Here, we review and categorize a set of major taxonomic databases publicly available as well as a large collection of R packages to access them and to harmonize lists of taxon names. We categorized available taxonomic databases according to their taxonomic breadth (e.g. taxon specific vs. multi‐taxa) and spatial scope (e.g. regional vs. global), highlighting strengths and caveats of each type of database. We divided R packages according to their function, (e.g. syntax standardization tools, access to online databases, etc.) and highlighted overlaps among them. We present our findings (e.g. network of linkages, data and tool characteristics) in a ready‐to‐use Shiny web application (available at: https://mgrenie.shinyapps.io/taxharmonizexplorer/). We also provide general guidelines and best practice principles for taxonomic name harmonization. As an illustrative example, we harmonized taxon names of one of the largest databases of community time series currently available. We showed how different workflows can be used for different goals, highlighting their strengths and weaknesses and providing practical solutions to avoid common pitfalls. To our knowledge, our opinionated review represents the most exhaustive evaluation of links among and of taxonomic databases and related R tools. Finally, based on our new insights in the field, we make recommendations for users, database managers and package developers alike.

AutoPlate: Rapid Dose-Response Curve Analysis for Biological Assays.

The emergence of COVID-19 has emphasised that biological assay data must be analysed quickly to develop safe, effective and timely vaccines/therapeutics. For viruses such as SARS-CoV-2, the primary way of measuring immune correlates of protection is through assays such as the pseudotype microneutralisation (pMN) assay, thanks to its safety and versatility. However, despite the presence of existing tools for data analysis such as PRISM and R the analysis of these assays remains cumbersome and time-consuming. We introduce an open-source R Shiny web application and R library (AutoPlate) to accelerate data analysis of dose-response curve immunoassays. Using example data from influenza studies, we show that AutoPlate improves on available analysis software in terms of ease of use, flexibility and speed. AutoPlate (https://philpalmer.shinyapps.io/AutoPlate/) is a tool for the use of laboratories and wider scientific community to accelerate the analysis of biological assays in the development of viral vaccines and therapeutics.

Pi-VAT: A web-based visualization tool for decision support using spatially complex water quality model outputs

Effective watershed management and protection of water resources from non-point source pollution require identification, prioritization, and targeting of pollutant source areas. Process-based hydrology and water quality models are powerful heuristic tools for land and water resources managers. However, because of their complexity, such models are often under-utilized as management prioritization and planning tools. In this paper, we present a prioritization, interactive visualization, and analysis tool (Pi-VAT) that is programmed to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models. We demonstrate the utility of Pi-VAT to examine simulated hydrologic, sediment, and water quality response at the hillslope/hydrologic response unit (HRU) scale. We apply Pi-VAT to output from multiple watersheds and for multiple management scenarios and treatments from two geospatial models for watershed management: Water Erosion Prediction Project (WEPP) and Soil & Water Assessment Tool (SWAT). Pi-VAT was developed using the Shiny web application framework for the R programming language. In a matter of minutes, Pi-VAT can synthesize overwhelming amounts of output from process-based models into information useful for land and water resources managers. We illustrate the use of Pi-VAT to interactively identify, quantify, and visualize areas that are most susceptible to disturbance under different scenarios and provide a synthesis approach based on land use, soil type, and slope steepness. This approach guides land and water resources managers in prioritizing the areas of the watershed that provide the maximum reduction in pollutant loads while treating the least amount of area. Pi-VAT provides a flexible reactive platform for the development of decision support tools based on process-based models intended for watershed management and research applications.

Perk: A Shiny Web Application to Predict and Visualize Concentrations of Pharmaceuticals in the Aqueous Environment

Perk: A Shiny Web Application to Predict and Visualize Concentrations of Pharmaceuticals in the Aqueous Environment

MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists.

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

KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data.

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.

Make Interactive Complex Heatmaps in R.

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

ROB-MEN: a tool to assess risk of bias due to missing evidence in network meta-analysis

BackgroundSelective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analyses and can affect clinical decision-making. A rigorous method to evaluate the impact of this bias on the results of network meta-analyses of interventions is lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN).MethodsROB-MEN first evaluates the risk of bias due to missing evidence for each of the possible pairwise comparison that can be made between the interventions in the network. This step considers possible bias due to the presence of studies with unavailable results (within-study assessment of bias) and the potential for unpublished studies (across-study assessment of bias). The second step combines the judgements about the risk of bias due to missing evidence in pairwise comparisons with (i) the contribution of direct comparisons to the network meta-analysis estimates, (ii) possible small-study effects evaluated by network meta-regression, and (iii) any bias from unobserved comparisons. Then, a level of “low risk”, “some concerns”, or “high risk” for the bias due to missing evidence is assigned to each estimate, which is our tool’s final output.ResultsWe describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder.ConclusionsROB-MEN is the first tool for evaluating the risk of bias due to missing evidence in network meta-analysis and applies to networks of all sizes and geometry. The use of ROB-MEN is facilitated by an R Shiny web application that produces the Pairwise Comparisons and ROB-MEN Table and is incorporated in the reporting bias domain of the CINeMA framework and software.

Optimal diagnostic test allocation strategy during the COVID-19 pandemic and beyond.

Timely diagnostic testing for active SARS‐CoV‐2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high‐risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long‐time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID‐19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT‐PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision‐making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID‐19 pandemic.

ShinyCurves, a shiny web application to analyse multisource qPCR amplification data: a COVID-19 case study

BackgroundQuantitative, reverse transcription PCR (qRT-PCR) is currently the gold-standard for SARS-CoV-2 detection and it is also used for detection of other virus. Manual data analysis of a small number of qRT-PCR plates per day is a relatively simple task, but automated, integrative strategies are needed if a laboratory is dealing with hundreds of plates per day, as is being the case in the COVID-19 pandemic.ResultsHere we present shinyCurves, an online shiny-based, free software to analyze qRT-PCR amplification data from multi-plate and multi-platform formats. Our shiny application does not require any programming experience and is able to call samples Positive, Negative or Undetermined for viral infection according to a number of user-defined settings, apart from providing a complete set of melting and amplification curve plots for the visual inspection of results.ConclusionsshinyCurves is a flexible, integrative and user-friendly software that speeds-up the analysis of massive qRT-PCR data from different sources, with the possibility of automatically producing and evaluating melting and amplification curve plots.

MultiMarker: software for modelling and prediction of continuous food intake using multiple biomarkers measurements

BackgroundMetabolomic biomarkers offer potential for objective and reliable food intake assessment, and there is growing interest in using biomarkers in place of or with traditional self-reported approaches. Ongoing research suggests that multiple biomarkers are associated with single foods, offering great sensitivity and specificity. However, currently there is a dearth of methods to model the relationship between multiple biomarkers and single food intake measurements.ResultsHere, we introduce multiMarker, a web-based application based on the homonymous R package, that enables one to infer the relationship between food intake and two or more metabolomic biomarkers. Furthermore, multiMarker allows prediction of food intake from biomarker data alone. multiMarker differs from previous approaches by providing distributions of predicted intakes, directly accounting for uncertainty in food intake quantification. Usage of both the R package and the web application is demonstrated using real data concerning three biomarkers for orange intake. Further, example data is pre-loaded in the web application to enable users to examine multiMarker’s functionality.ConclusionThe proposed software advance the field of Food Intake Biomarkers providing researchers with a novel tool to perform continuous food intake quantification, and to assess its associated uncertainty, from multiple biomarkers. To facilitate widespread use of the framework, multiMarker has been implemented as an R package and a Shiny web application.

Matching Species Names Across Biodiversity Databases: Sources, tools, pitfalls and best practices for taxonomic harmonization

The quantity and quality of ecological data have rapidly increased in the last decades, bringing ecology into the realm of big data. Frequently, multiple databases with different origins and data characteristics are combined to address new research questions. Taxonomic name harmonization, i.e., the process of standardizing taxon names according to common sources such as taxonomic databases (TD), is necessary to properly combine multiple databases using species names. In order to be able to develop proper data matching workflows, TDs and tools using them need to be clearly and comprehensively described. But this is rarely the case. Common problems users have to deal with are: poorly described taxonomic concepts behind biological databases, lack of information when TDs are actively updated, and details regarding where the primary source of taxonomic information comes from (e.g., secondary TDs taking information from primary TDs). In addition, software to access these TDs is not always advertised, partly redundant, or developed with incompatible standards, creating additional challenges for users. As a result, taxonomic name harmonization has become a major difficulty in ecological studies. Researchers face a jungle of primary and secondary TDs with a diversity of tools to access them and no clear workflow on how to practically proceed. As a consequence, it is hard for users to know which TD, tool and workflow will fit the task at hand and lead to the most robust results when combining different biological datasets. Here, we present an overview of major TDs as well as an extensive review of R packages to access TDs, and to harmonize taxa names. We developed an R Shiny web application summarizing meta-data and linkages among TDs and R packages (Figs 1, 2), which users can explore to learn about general features of TDs and tools and how they are linked among one another. This is particularly helpful to assist users when deciding on the TDs and tools that best fit the tasks and data at hand and to develop more informed workflows for taxonomic name harmonization. Finally, from our review and using the Shiny app, we were able to provide general best practice principles to harmonize taxonomic names and avoid common pitfalls. To our knowledge, this study represents the most exhaustive review of TDs and R tools for taxonomic name harmonization. Our intuitive Shiny app can help make practical decisions when harmonizing taxonomic names across multiple datasets. Finally, our proposed workflows, based on conservative guideline principles (e.g., making sure incompatible taxonomic hypotheses are not combined together), provide a hands-on approach for taxonomic harmonization, which focuses on the quality of the end results while maximizing the number of species correctly matched.

Plant PhysioSpace: a robust tool to compare stress response across plant species.

Generalization of transcriptomics results can be achieved by comparison across experiments. This generalization is based on integration of interrelated transcriptomics studies into a compendium. Such a focus on the bigger picture enables both characterizations of the fate of an organism and distinction between generic and specific responses. Numerous methods for analyzing transcriptomics datasets exist. Yet, most of these methods focus on gene-wise dimension reduction to obtain marker genes and gene sets for, for example, pathway analysis. Relying only on isolated biological modules might result in missing important confounders and relevant contexts. We developed a method called Plant PhysioSpace, which enables researchers to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene sets. Plant PhysioSpace extracts physiologically relevant signatures from a reference dataset (i.e. a collection of public datasets) by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores between the acquired data and the extracted compendium. Because of its robustness against platform bias and noise, Plant PhysioSpace can function as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms, including single-cell technologies. We have also implemented two R packages, one software and one data package, and a Shiny web application to facilitate access to our method and precomputed models.