GenoCeptR

GenoCeptR is an interactive R Shiny application for exploring overlaps between differential gene expression (DE) result sets and running downstream pathway enrichment analysis — all in one workspace.

Developed by Dinuka Adasooriya, Yonsei University College of Dentistry, Seoul, Korea.

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Key Features

Data Input

• Analyze 2–5 datasets in a single session.
• Two input modes:
  1. Upload DE result files (.csv, .tsv, .txt, .xlsx) with automatic separator/sheet detection and column auto-mapping (gene ID, gene name, adjusted p-value, log2 fold-change).
  2. Paste pre-filtered gene lists (one gene per line, per dataset).
• Adjustable significance (adjusted p-value) and |log2FC| cutoffs, plus an optional up/down gene direction filter.

Set Overlap Visualization

• Venn diagrams (2–5 sets) via ggvenn / ggVennDiagram / VennDiagram.
• Interactive Venn diagram (Plotly-based, with custom hover panels).
• Euler diagrams (eulerr) — area-proportional alternative to Venn.
• UpSet plots (UpSetR) for higher-dimensional overlaps.
• Edwards' Venn diagrams.
• Full customization: labels, fill colors (including colorblind-friendly palettes), font sizes, titles.
• Exports: PNG, SVG, PDF, and interactive HTML.

Overlap Summaries & Gene Lists

• Per-dataset and per-intersection numeric summaries.
• Interactive, searchable gene tables (DT) with aggregated adjusted p-value and log2FC per gene.
• Downloadable gene lists and summaries (CSV / TXT).

Pathway Enrichment Analysis

• Over-representation analysis (ORA) via gprofiler2::gost() against GO, KEGG, Reactome, and other supported databases.
• Run enrichment directly on any dataset or overlap region generated in the Venn/Euler/UpSet workspace, with an optional gene-direction (up/down) filter.
• Results as sortable/searchable tables, bar/dot/lollipop plots, dendrogram trees, and gene–pathway networks.
• Exports: plots (PNG/SVG/PDF), tables (CSV), network nodes/edges (CSV), and interactive network (HTML).

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Getting Started

Requirements

• R (≥ 4.2 recommended)
• RStudio (optional, .Rproj file included)

Install dependencies

pkgs <- c(
  "shiny", "bslib", "shinyjs", "colourpicker", "VennDiagram", "ggvenn",
  "dplyr", "DT", "shinyWidgets", "readxl", "openxlsx", "UpSetR", "eulerr",
  "ggplot2", "showtext", "ggVennDiagram", "plotly", "htmlwidgets"
)
install.packages(pkgs)

# Optional, enables Pathway Analysis tab and network graphs
install.packages(c("gprofiler2", "ggdendro", "visNetwork", "igraph", "scales"))

Run the app

shiny::runApp("path/to/GenoCeptR")

or open GenoCeptR.Rproj in RStudio and run run_app.R, or click Run App.

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Project Structure

GenoCeptR/
├── app.R                 # Shiny entry point (UI + server)
├── global.R              # Package loading, module/util sourcing
├── run_app.R             # Standalone launcher
├── DESCRIPTION           # Package metadata / dependencies
├── modules/              # UI and server modules
│   ├── ui_styles.R
│   ├── ui_sidebar.R
│   ├── ui_tabs.R
│   ├── ui_pathway.R
│   ├── server_data_input.R
│   ├── server_data_input_generate.R
│   ├── server_plotting.R
│   ├── server_downloads.R
│   ├── server_downloads_overlaps.R
│   ├── server_outputs.R
│   └── server_pathway.R
├── utils/                 # Shared utility functions
│   ├── file_utils.R
│   ├── palette_utils.R
│   ├── overlap_utils.R
│   ├── plot_utils.R
│   └── pathway_utils.R
└── www/                   # Static assets (logos, images)

Input Data Format

Supported DE result file formats: .csv, .tsv, .txt, .xlsx. Files should contain at minimum a gene identifier column; adjusted p-value and log2 fold-change columns are required to use significance/direction filtering and are used to annotate the Pathway Analysis and Gene List tabs.

Screenshots

License

Released under the MIT License.

Changelog

See CHANGELOG.md for version history.