Metadata-Version: 2.4
Name: summarizedpy
Version: 0.1.3
Summary: A differential expression analysis package for proteomics data, which leverages transcriptomics tools.
Author-email: Sebastian Dohm-Hansen <heinrich.sebastian.dohmhansen@gmail.com>
Maintainer-email: Sebastian Dohm-Hansen <heinrich.sebastian.dohmhansen@gmail.com>
License-Expression: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: pyarrow
Requires-Dist: adjustText
Requires-Dist: pyjanitor
Requires-Dist: openpyxl
Provides-Extra: dev
Requires-Dist: jupyter; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Dynamic: license-file

# DEPy

![PyPI version](https://img.shields.io/pypi/v/DEPy.svg)

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

## 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
- [Installation guide](docs/installation.md)
- [Usage](docs/usage.md)

## 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.
