https://github.com/sebastiandha/depy
A hybrid Python-R proteomics DEA package
https://github.com/sebastiandha/depy
bioinformatics differential-expression-analysis metabolomics proteomics python r transcriptomics
Last synced: 29 days ago
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A hybrid Python-R proteomics DEA package
- Host: GitHub
- URL: https://github.com/sebastiandha/depy
- Owner: SebastianDHA
- License: mit
- Created: 2025-10-14T14:36:18.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2026-02-03T18:04:45.000Z (4 months ago)
- Last Synced: 2026-02-04T05:36:06.401Z (4 months ago)
- Topics: bioinformatics, differential-expression-analysis, metabolomics, proteomics, python, r, transcriptomics
- Language: Python
- Homepage: https://sebastiandha.github.io/DEPy/
- Size: 5.15 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- License: LICENSE
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README
# DEPy
A differential expression analysis package for bulk proteomics (and metabolomics) data, which leverages transcriptomics tools.
Inspired by R tools like DEP and SummarizedExperiment, it brings the power of Bioconductor to Python.
All you need is a matrix of features and their intensity values.
* PyPI package: https://pypi.org/project/summarizedpy/
* GitHub: [SebastianDHA/DEPy](https://github.com/SebastianDHA/DEPy)
* Free software: MIT License
## Features
* SummarizedPY: A container for your -omics data, much like SummarizedExperiment or DEP in R.
* Filtering and subsetting your samples and features
* Missing value filtering
* Imputation using ImputeLCMD (many methods)
* Transforming (log, centering, standardizing, vsn)
* Leverage surrogate variable analysis (sva) to adjust for latent batch effects
* Use the flexibility and power of limma-trend to improve your DEA results and accommodate mixed effects
* Limma arrayWeights to adjust variable sample quality (often an issue in human and animal datasets)
* Visualize your DEA results with elegant volcano plots
* Highly-variable feature selection
* PCA plots
* Saving & loading SummarizedPy objects to & from disk
## Installation
### conda
This is the best way to install DEPy.
```Sh
conda env create -f environment.yml
```
Note that DEPy (summarizedpy) must be run within the [depy conda environment](environment.yml) or a cloned version of it.
This is because summarizedpy needs an isolated environment to run R in due to the complex loading behavior of Bioconductor packages.
## Using pip
```Sh
pip install summarizedpy
```
## Quick start
```Py
import depy as dp
sp = dp.SummarizedPy()
sp = sp.import_from_delim_file(path="path/to/pgroup.tsv", delim="\t")
```
See the full [tutorial](docs/usage.md) for more.
## Documentation
- [GitHub pages](https://sebastiandha.github.io/DEPy/)
- [ReadTheDocs](https://depy.readthedocs.io/en/latest/)
## Citation
DEPy and its theoretical foundations are described in the following paper:
```
Dohm-Hansen, S., et al. (2026).
Expanding the Proteomics and Metabolomics Toolkit with Methods for Differential Expression Analysis from Transcriptomics.
Journal of Proteome Research Article ASAP.
https://doi.org/10.1021/acs.jproteome.5c00719
```
## Credits
This package leverages amazing packages from the R and Bioconductor community, including [limma](https://bioconductor.org/packages/3.20/bioc/html/limma.html), [vsn](https://bioconductor.org/packages/release/bioc/html/vsn.html), [sva](https://bioconductor.org/packages/release/bioc/html/sva.html), [ImputeLCMD](https://cran.r-project.org/package=imputeLCMD), and [Tidyverse](https://www.tidyverse.org/).
This package was created with [Cookiecutter](https://github.com/audreyfeldroy/cookiecutter) and the [audreyfeldroy/cookiecutter-pypackage](https://github.com/audreyfeldroy/cookiecutter-pypackage) project template.