Dataset Information

SpatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data.

ABSTRACT:

Summary

Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g., Ripley's K, Besag's L, Macron's M, and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time.

Availability and implementation

spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Creed JH

PROVIDER: S-EPMC8652029 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. RESULTS:We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. CONCLUSION:The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Project description:Inference of toxicological and mechanistic properties of untested chemicals through structural or biological similarity is a commonly employed approach for initial chemical characterization and hypothesis generation. We previously developed a web-based application, Tox21Enricher-Grails, on the Grails framework that identifies enriched biological/toxicological properties of chemical sets for the purpose of inferring properties of untested chemicals within the set. It was able to detect significantly overrepresented biological (e.g., receptor binding), toxicological (e.g., carcinogenicity), and chemical (e.g., toxicologically relevant chemical substructures) annotations within sets of chemicals screened in the Tox21 platform. Here, we present an R Shiny application version of Tox21Enricher-Grails, Tox21Enricher-Shiny, with more robust features and updated annotations. Tox21Enricher-Shiny allows users to interact with the web application component (available at http://hurlab.med.und.edu/Tox21Enricher/) through a user-friendly graphical user interface or to directly access the application's functions through an application programming interface. This version now supports InChI strings as input in addition to CASRN and SMILES identifiers. Input chemicals that contain certain reactive functional groups (nitrile, aldehyde, epoxide, and isocyanate groups) may react with proteins in cell-based Tox21 assays: this could cause Tox21Enricher-Shiny to produce spurious enrichment analysis results. Therefore, this version of the application can now automatically detect and ignore such problematic chemicals in a user's input. The application also offers new data visualizations, and the architecture has been greatly simplified to allow for simple deployment, version control, and porting. The application may be deployed onto a Posit Connect or Shiny server, and it uses Postgres for database management. As other Tox21-related tools are being migrated to the R Shiny platform, the development of Tox21Enricher-Shiny is a logical transition to use R's strong data analysis and visualization capacities and to provide aesthetic and developmental consistency with other Tox21 applications developed by the Division of Translational Toxicology (DTT) at the National Institute of Environmental Health Sciences (NIEHS).