# DeeDeeExperiment
`DeeDeeExperiment` is an S4 class extending the `SingleCellExperiment` framework to
facilitate the integration and management of omics analysis results.
It introduces two dedicated slots to store Differential Expression analysis (DEA)
results and Functional Enrichment analysis (FEA) results, providing a structured approach
for downstream analysis.
## Installation
You can install the development version of `DeeDeeExperiment` from GitHub with
``` r
library("remotes")
remotes::install_github("imbeimainz/DeeDeeExperiment",
dependencies = TRUE,
build_vignettes = TRUE)
```
## Structure and Usage
The `DeeDeeExperiment` class extends the core Bioconductor `SingleCellExperiment` object,
retaining its structure, methods, and compatibility with existing tools.
In addition, it introduces new components designed to simplify and enhance downstream analysis.
Specifically, `DeeDeeExperiment` has two new slots:
- `dea` : A slot that stores results from differential expression analysis (DEA),
along with relevant metadata (currently supports results from `DESeq2`, `edgeR`, `limma`)
* `fea` : A slot that stores results from functional enrichment analysis (FEA),
along with relevant metadata (currently supports results from `topGO`, `clusterProfiler`,
`enrichR`, `gProfiler`, `fgsea`, `gsea`, `DAVID`, and output of `GeneTonic` shakers)
## Example
``` r
library("DeeDeeExperiment")
library("macrophage")
# load data
data(gse, "macrophage")
data("de_named_list", package = "DeeDeeExperiment")
data("topGO_results_list", package = "DeeDeeExperiment")
dds_macrophage <- DESeq2::DESeqDataSet(gse, design = ~ line + condition)
# create DeeDeeExperiment object
dde <- DeeDeeExperiment(sce = dds_macrophage,
de_results = de_named_list,
enrich_results = topGO_results_list)
```
## Development
If you encounter a bug, have usage questions, or want to share ideas and functionality
to make this package better, feel free to file an
[issue](https://github.com/imbeimainz/DeeDeeExperiment/issues).
## License
MIT
