package help

R/arcpga.app-package.R

+#' ARCPGA: Explore and Visualize Metabolic Data in PDAC Transcriptomic Datasets
+#'
+#' The `arcpga.app` package provides a Shiny application that allows users to explore transcriptomic datasets 
+#' with a focus on metabolic filtering and visualization. It is tailored for PDAC (Pancreatic Ductal Adenocarcinoma) 
+#' research, enabling detailed differential expression analysis and interactive data exploration.
+#'
+#' @section Data Exploration and Visualization:
+#'   Users can select a dataset of interest and apply pre-integrated metabolic filters or create custom filters 
+#'   to refine the dataset. The application includes:
+#'   
+#'   **Volcano Plot**
+#'   
+#'   Powered by the VolcanoseR Shiny app, this module visualizes differentially expressed genes based on statistical significance 
+#'   (p-value) and fold-change. The plot offers:
+#'   
+#'   - Interactive zooming and selection: Users can zoom into specific regions of the volcano plot to focus on significant genes.
+#'   
+#'    - Highlighting key genes: Selected genes can be color-coded for quick identification of genes of interest, such as those 
+#'    significantly upregulated or downregulated.
+#'    
+#'   - Threshold adjustment: Users can dynamically adjust p-value and fold-change thresholds to refine the display of significant genes.
+#'   
+#'   - Custom labels: Easily annotate points of interest (genes) directly on the plot for presentations or reports.
+#'   
+#'   
+#'   **Modular Heatmaps**
+#'   
+#'   Leverage modularized components from VolcanoseR to generate heatmaps for selected genes, providing in-depth insights into 
+#'   gene expression patterns across samples.
+#'   
+#'   **Interactive and Reactive Gene Selection**
+#'   
+#'   The interactive selection of genes in the volcano plot is fully reactive and seamlessly integrated with other visualizations 
+#'   such as heatmaps and enrichment analysis. Selecting genes in the volcano plot automatically updates these linked modules, 
+#'   ensuring a dynamic and cohesive data exploration experience.
+#'   
+#'   **EnrichR Analysis**
+#'   
+#'   Perform enrichment analysis on selected gene lists, leveraging databases of interest to identify pathways or biological functions.
+#'   
+#' @section Gene Database Browsing:
+#'   Access curated gene lists for human and mouse models from the Vasseur Team's research. This includes:
+#'   
+#'   **Side Panel**
+#'   
+#'    Displays gene information such as Entrez IDs and gene symbols, with additional details in a third column.
+#'   
+#'   **Main Panel**
+#'   
+#'   Provides a comprehensive overview of all possible gene entries and their correspondences with various ID databases, 
+#'   facilitating cross-referencing across multiple resources.
+#'   
+#' @section File Download:
+#'   The app includes a file download section where users can access project-related data in various formats, including:
+#'   
+#'   - Differential Expression Results: Download analysis results in Excel, TXT, PDF, or HTML formats.
+#'   
+#'   - RNA-Seq Data: Access raw or processed RNA-Seq data related to the project.
+#'   
+#'   - Metabolic References: Obtain references and resources linked to metabolic processes and annotations.
+#'   
+#'   - Presentations and Reports: Download presentations, HTML reports, and archived files related to the project.
+#'   
+#' @section Project Information:
+#'   Contextualize the research project by providing a detailed overview of the experimental design and key objectives. 
+#'   This helps users understand the underlying scientific goals and how the app can support their research.
+#'
+#' @seealso \code{\link{run_app}()} for getting started.
+#' @name arcpga.app-package
+#' @keywords internal
+"_PACKAGE"
+
+
+## usethis namespace: start
+## usethis namespace: end
+NULL

man/arcpga.app-package.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/arcpga.app-package.R
+\docType{package}
+\name{arcpga.app-package}
+\alias{arcpga.app}
+\alias{arcpga.app-package}
+\title{ARCPGA: Explore and Visualize Metabolic Data in PDAC Transcriptomic Datasets}
+\description{
+The `arcpga.app` package provides a Shiny application that allows users to explore transcriptomic datasets 
+with a focus on metabolic filtering and visualization. It is tailored for PDAC (Pancreatic Ductal Adenocarcinoma) 
+research, enabling detailed differential expression analysis and interactive data exploration.
+}
+\section{Data Exploration and Visualization}{
+
+  Users can select a dataset of interest and apply pre-integrated metabolic filters or create custom filters 
+  to refine the dataset. The application includes:
+  
+  **Volcano Plot**
+  
+  Powered by the VolcanoseR Shiny app, this module visualizes differentially expressed genes based on statistical significance 
+  (p-value) and fold-change. The plot offers:
+  
+  - Interactive zooming and selection: Users can zoom into specific regions of the volcano plot to focus on significant genes.
+  
+   - Highlighting key genes: Selected genes can be color-coded for quick identification of genes of interest, such as those 
+   significantly upregulated or downregulated.
+   
+  - Threshold adjustment: Users can dynamically adjust p-value and fold-change thresholds to refine the display of significant genes.
+  
+  - Custom labels: Easily annotate points of interest (genes) directly on the plot for presentations or reports.
+  
+  
+  **Modular Heatmaps**
+  
+  Leverage modularized components from VolcanoseR to generate heatmaps for selected genes, providing in-depth insights into 
+  gene expression patterns across samples.
+  
+  **Interactive and Reactive Gene Selection**
+  
+  The interactive selection of genes in the volcano plot is fully reactive and seamlessly integrated with other visualizations 
+  such as heatmaps and enrichment analysis. Selecting genes in the volcano plot automatically updates these linked modules, 
+  ensuring a dynamic and cohesive data exploration experience.
+  
+  **EnrichR Analysis**
+  
+  Perform enrichment analysis on selected gene lists, leveraging databases of interest to identify pathways or biological functions.
+}
+
+\section{Gene Database Browsing}{
+
+  Access curated gene lists for human and mouse models from the Vasseur Team's research. This includes:
+  
+  **Side Panel**
+  
+   Displays gene information such as Entrez IDs and gene symbols, with additional details in a third column.
+  
+  **Main Panel**
+  
+  Provides a comprehensive overview of all possible gene entries and their correspondences with various ID databases, 
+  facilitating cross-referencing across multiple resources.
+}
+
+\section{File Download}{
+
+  The app includes a file download section where users can access project-related data in various formats, including:
+  
+  - Differential Expression Results: Download analysis results in Excel, TXT, PDF, or HTML formats.
+  
+  - RNA-Seq Data: Access raw or processed RNA-Seq data related to the project.
+  
+  - Metabolic References: Obtain references and resources linked to metabolic processes and annotations.
+  
+  - Presentations and Reports: Download presentations, HTML reports, and archived files related to the project.
+}
+
+\section{Project Information}{
+
+  Contextualize the research project by providing a detailed overview of the experimental design and key objectives. 
+  This helps users understand the underlying scientific goals and how the app can support their research.
+}
+
+\seealso{
+\code{\link{run_app}()} for getting started.
+}
+\author{
+\strong{Maintainer}: Eugénie Lohmann \email{eugenie.lohmann@inserm.fr} (\href{https://orcid.org/0000-0002-3230-8363}{ORCID})
+
+Authors:
+\itemize{
+  \item Ghislain Bidaut
+}
+
+}
+\keyword{internal}