# Carnation
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**Less Code, More Biology: Carnation for Collaborative Multi-Omics Exploration**
Carnation is a modular, interactive Shiny app for working with bulk RNA-seq and
related omics data across differential expression (DE), functional enrichment,
and gene pattern analysis. It’s designed for experiments with many contrasts
and iterative tuning, where results are otherwise scattered across scripts and
output files, and can be used to compare signals across modalities (e.g.
transcriptomics and proteomics).
A range of interactive visualizations are available, including PCA, MA
plots, UpSet plots, heatmaps, customizable gene-level views, and multiple
enrichment summaries. Also, a global gene scratchpad lets you track genes of interest
across all modules.
Carnation supports multiple projects and user access control, and can be run
locally or deployed on a central server for sharing analyses with
collaborators. Its modular design makes it easy to extend with new tools,
visualizations, or data types. In summary, Carnation democratizes multi-omic
genomics analyses, transforming static analysis into a collaborative,
interactive experience, enabling research teams to spend less time navigating
data and more time generating biological insight.
**Carnation is now on Bioconductor devel (Official release: April 2026)**
Check out the official bioconductor page [here](https://bioconductor.org/packages/devel/bioc/html/carnation.html)
for more details.
## Key Features
- **DE Analysis**: Analyze differential expression through multiple visualizations
- **Summary**: Get a quick overview of your differential expression results
- **Metadata**: Explore sample metadata and experimental design
- **PCA Plot**: Visualize sample relationships with gene loadings overlay
- **Scatter Plot**: Compare fold-changes for genes between different comparisons
- **MA Plot**: Identify differentially expressed genes with statistical significance
- **Gene Plot**: Create customizable expression visualizations for genes of interest
- **UpSet Plot**: Discover overlapping gene sets across multiple comparisons
- **Heatmap**: Examine expression patterns across samples and conditions
- **Functional Enrichment**: Understand the biological significance of your results
- **Table**: Interactive tables with powerful search capabilities
- **Plots**: Seven different visualizations including network plots and dendrograms
- **Compare Results**: Directly compare enrichment results between conditions
- **Pattern Analysis**: Identify co-regulated gene clusters across conditions
- **Plot**: Visualize expression patterns of gene clusters
- **Cluster Membership**: Explore which genes belong to which clusters
- **Gene Scratchpad**: Track genes of interest across all visualizations
- **Flexible Deployment**: Run locally for personal analysis or on a server to share with collaborators
- **User Management**: Optional authentication system for controlled access in multi-user environments
## Installation
Carnation can be installed using `BiocManager::install`. First, start R (version: 4.6)
and then run:
```r
# first check to see if BiocManager is available
if(!requireNamespace('BiocManager', quietly=TRUE)){
install.packages('BiocManager')
}
BiocManager::install('carnation')
```
To install the 'devel' version
```r
BiocManager::install('carnation', version='devel')
```
### remotes
You can install the developmental version of carnation from github using the `remotes`
package:
```r
install.packages('remotes')
remotes::install_github('NICHD-BSPC/carnation',
dependencies=TRUE, build_vignettes=TRUE)
```
### conda
An alternative way to get started with Carnation is through conda, which handles all dependencies automatically:
```bash
# Create environment outside the carnation directory
cd .. && conda env create -p env --file carnation/requirements-pinned.yaml
conda activate ./env
R
```
Then install the package with the `remotes` package. Here we set
`upgrade='never'` to make sure the conda-installed package versions remain
unchanged.
```r
remotes::install_github('NICHD-BSPC/carnation@r4.3', upgrade='never')
```
Note:
- Conda packages for R >= 4.6.0 may not be available yet causing installation using the default
github branch to fail. To avoid this, use branch `r4.3` which pins R to a lower version.
## Getting Started
### Data Organization
Organize your data in a directory structure that Carnation can easily navigate:
```
/carnation/data/
├─ project1
│ ├─ main.rds
│ ├─ subset.rds
│ └─ main-nooutlier.rds
│
└─ project2
└─ default.rds
```
### First Run
Load the library and install required Python dependencies:
```r
library(carnation)
install_carnation() # Installs plotly and kaleido for PDF export
run_carnation()
```
For remote servers with SSH port forwarding:
```r
run_carnation(options=list(port=12345, launch.browser=FALSE))
```
Then access Carnation at `http://127.0.0.1:12345`
## Documentation
Each module includes comprehensive help documentation accessible through the
help buttons throughout the interface. The documentation provides detailed
explanations of plot options, statistical methods, and interpretation
guidelines.
## Contributing
We welcome contributions to Carnation! Please feel free to submit issues or
pull requests to the GitHub repository.
## License
Carnation is available under the MIT license.
