https://github.com/dinuka0001/genoceptr
GenoCeptR — An interactive R Shiny application for visualizing and comparing differential gene expression overlaps using 2–5 datasets. Supports CSV/TSV/Excel inputs, automatic column detection, customizable Venn diagrams, and downloadable results.
https://github.com/dinuka0001/genoceptr
bioinformatics differential-expression differential-expression-analysis rna-seq-analysis sankey-diagram sankey-diagram-generator shiny shiny-apps upset-plot venn venn-diagram venn-diagrams venn-plot venndiagram visualization
Last synced: 16 days ago
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GenoCeptR — An interactive R Shiny application for visualizing and comparing differential gene expression overlaps using 2–5 datasets. Supports CSV/TSV/Excel inputs, automatic column detection, customizable Venn diagrams, and downloadable results.
- Host: GitHub
- URL: https://github.com/dinuka0001/genoceptr
- Owner: Dinuka0001
- License: mit
- Created: 2025-11-19T04:31:48.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2026-07-01T15:03:45.000Z (23 days ago)
- Last Synced: 2026-07-01T15:28:46.755Z (23 days ago)
- Topics: bioinformatics, differential-expression, differential-expression-analysis, rna-seq-analysis, sankey-diagram, sankey-diagram-generator, shiny, shiny-apps, upset-plot, venn, venn-diagram, venn-diagrams, venn-plot, venndiagram, visualization
- Language: R
- Homepage: https://dinuka-genoceptr.share.connect.posit.cloud
- Size: 1.08 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# 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.
---
## 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).
---
## Getting Started
### Requirements
- R (≥ 4.2 recommended)
- RStudio (optional, `.Rproj` file included)
### Install dependencies
```r
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
```r
shiny::runApp("path/to/GenoCeptR")
```
or open `GenoCeptR.Rproj` in RStudio and run `run_app.R`, or click **Run App**.
---
## 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](LICENSE).
## Changelog
See [CHANGELOG.md](CHANGELOG.md) for version history.