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align=\"center\"\u003e\n  \u003cimg src=\"www/logo2.png\" alt=\"GenoCeptR Logo\" height=\"120\"\u003e\n\u003c/p\u003e\n\n# GenoCeptR\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/R-Shiny-blue?style=for-the-badge\u0026logo=R\" alt=\"R Shiny\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Version-3.0.0-green?style=for-the-badge\" alt=\"Version\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/License-MIT-yellow?style=for-the-badge\" alt=\"License\"\u003e\n\u003c/p\u003e\n\n**GenoCeptR** is an interactive **R Shiny** application for exploring overlaps\nbetween differential gene expression (DE) result sets and running downstream\npathway enrichment analysis — all in one workspace.\n\nDeveloped by **Dinuka Adasooriya**, Yonsei University College of Dentistry, Seoul, Korea.\n\n---\n\n## Key Features\n\n### Data Input\n- Analyze **2–5 datasets** in a single session.\n- Two input modes:\n  1. **Upload DE result files** (`.csv`, `.tsv`, `.txt`, `.xlsx`) with automatic\n     separator/sheet detection and column auto-mapping (gene ID, gene name,\n     adjusted p-value, log2 fold-change).\n  2. **Paste pre-filtered gene lists** (one gene per line, per dataset).\n- Adjustable significance (adjusted p-value) and |log2FC| cutoffs, plus an\n  optional up/down **gene direction filter**.\n\n### Set Overlap Visualization\n- **Venn diagrams** (2–5 sets) via `ggvenn` / `ggVennDiagram` / `VennDiagram`.\n- **Interactive Venn diagram** (Plotly-based, with custom hover panels).\n- **Euler diagrams** (`eulerr`) — area-proportional alternative to Venn.\n- **UpSet plots** (`UpSetR`) for higher-dimensional overlaps.\n- **Edwards' Venn diagrams**.\n- Full customization: labels, fill colors (including colorblind-friendly\n  palettes), font sizes, titles.\n- Exports: PNG, SVG, PDF, and interactive HTML.\n\n### Overlap Summaries \u0026 Gene Lists\n- Per-dataset and per-intersection numeric summaries.\n- Interactive, searchable gene tables (`DT`) with aggregated adjusted p-value\n  and log2FC per gene.\n- Downloadable gene lists and summaries (CSV / TXT).\n\n### Pathway Enrichment Analysis\n- Over-representation analysis (ORA) via `gprofiler2::gost()` against GO,\n  KEGG, Reactome, and other supported databases.\n- Run enrichment directly on any dataset or overlap region generated in the\n  Venn/Euler/UpSet workspace, with an optional gene-direction (up/down) filter.\n- Results as sortable/searchable tables, bar/dot/lollipop plots, dendrogram\n  trees, and gene–pathway networks.\n- Exports: plots (PNG/SVG/PDF), tables (CSV), network nodes/edges (CSV), and\n  interactive network (HTML).\n\n---\n\n## Getting Started\n\n### Requirements\n- R (≥ 4.2 recommended)\n- RStudio (optional, `.Rproj` file included)\n\n### Install dependencies\n\n```r\npkgs \u003c- c(\n  \"shiny\", \"bslib\", \"shinyjs\", \"colourpicker\", \"VennDiagram\", \"ggvenn\",\n  \"dplyr\", \"DT\", \"shinyWidgets\", \"readxl\", \"openxlsx\", \"UpSetR\", \"eulerr\",\n  \"ggplot2\", \"showtext\", \"ggVennDiagram\", \"plotly\", \"htmlwidgets\"\n)\ninstall.packages(pkgs)\n\n# Optional, enables Pathway Analysis tab and network graphs\ninstall.packages(c(\"gprofiler2\", \"ggdendro\", \"visNetwork\", \"igraph\", \"scales\"))\n```\n\n### Run the app\n\n```r\nshiny::runApp(\"path/to/GenoCeptR\")\n```\n\nor open `GenoCeptR.Rproj` in RStudio and run `run_app.R`, or click **Run App**.\n\n---\n\n## Project Structure\n\n```\nGenoCeptR/\n├── app.R                 # Shiny entry point (UI + server)\n├── global.R              # Package loading, module/util sourcing\n├── run_app.R             # Standalone launcher\n├── DESCRIPTION           # Package metadata / dependencies\n├── modules/              # UI and server modules\n│   ├── ui_styles.R\n│   ├── ui_sidebar.R\n│   ├── ui_tabs.R\n│   ├── ui_pathway.R\n│   ├── server_data_input.R\n│   ├── server_data_input_generate.R\n│   ├── server_plotting.R\n│   ├── server_downloads.R\n│   ├── server_downloads_overlaps.R\n│   ├── server_outputs.R\n│   └── server_pathway.R\n├── utils/                 # Shared utility functions\n│   ├── file_utils.R\n│   ├── palette_utils.R\n│   ├── overlap_utils.R\n│   ├── plot_utils.R\n│   └── pathway_utils.R\n└── www/                   # Static assets (logos, images)\n```\n\n## Input Data Format\n\nSupported DE result file formats: `.csv`, `.tsv`, `.txt`, `.xlsx`. Files\nshould contain at minimum a gene identifier column; adjusted p-value and\nlog2 fold-change columns are required to use significance/direction\nfiltering and are used to annotate the Pathway Analysis and Gene List tabs.\n\n## Screenshots\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"www/image1.png\" alt=\"GenoCeptR screenshot 1\" width=\"800\"\u003e\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src=\"www/image2.png\" alt=\"GenoCeptR screenshot 2\" width=\"800\"\u003e\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src=\"www/image5.png\" alt=\"GenoCeptR screenshot 3\" width=\"800\"\u003e\n\u003c/p\u003e\n\n## License\n\nReleased under the [MIT License](LICENSE).\n\n## Changelog\n\nSee [CHANGELOG.md](CHANGELOG.md) for version history.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdinuka0001%2Fgenoceptr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdinuka0001%2Fgenoceptr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdinuka0001%2Fgenoceptr/lists"}