proteomics-downstream-analysis 0.1.3
A package for downstream data analysis of proteomics data
Requires Python: >=3.6.1
Keywords: proteomics,downstream analysis,data analysis,data visualization,mass spectrometry

<details>
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#package-description">Package description</a>
      <ul>
      </ul>
    </li>
    <li>
      <a href="#getting-started">Getting Started</a>
      <ul>
      </ul>
    </li>
    <li><a href="#usage">Usage</a></li>
    <li><a href="#contributing">Contributing</a></li>
    <li><a href="#license">License</a></li>
</details>

<p align="right">(<a href="#readme-top">back to top</a>)</p>

## Package description
Introducing Proteomics Downstream Analysis v0.1.0, a comprehensive Python package designed to simplify and streamline the process of downstream data analysis for proteomics research. This package offers a user-friendly and efficient way to handle, manipulate, process, and visualize large proteomics datasets, helping researchers gain valuable insights from their data more quickly and effectively.

Key features of proteomics_downstream_analysis v0.1.0 include:

Data import and preprocessing: Easily import and preprocess raw proteomics data from DIA-NN. Automatically handle missing values, normalization, and data transformation as needed.

Statistical analysis: Perform essential statistical tests such as t-tests, ANOVA, and multiple testing correction methods to assess the significance of differentially expressed proteins.

Enrichment analysis: Conduct functional enrichment analysis to identify over-represented functional categories, biological processes, or pathways in your protein sets, supporting popular databases like Gene Ontology and KEGG.

Clustering and dimensionality reduction: Apply advanced unsupervised learning techniques to group similar proteins and uncover underlying biological patterns. Techniques include hierarchical clustering, k-means clustering, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE).

Customizable data visualization: Create stunning and informative visualizations to better understand and communicate your results. Generate heatmaps, volcano plots, Venn diagrams, and more with full customization options.

Integration with existing tools: Compatibility with popular Python libraries including NumPy, pandas, and matplotlib, allowing you to seamlessly integrate this package into your existing data analysis workflow.

Proteomics Downstream Analysis v0.1.0 provides a solid foundation for your proteomics research needs.


<p align="right">(<a href="#readme-top">back to top</a>)</p>


## Getting Started

ProteomicsDownstreamAnalysis can be installed using:
```
pip install proteomics-downstream-analysis
```

<p align="right">(<a href="#readme-top">back to top</a>)</p>


## Usage
```
import proteomics_downstream_analysis as pda
```
<p align="right">(<a href="#readme-top">back to top</a>)</p>

## Contributing

Contribution is much appreciated. Happy to get feedback and suggestions! 

Should you have a suggestion that could enhance this project, kindly fork the repository and create a pull request. You may also open an issue labeled as “improvement”. 

<p align="right">(<a href="#readme-top">back to top</a>)</p>


## License

Distributed under the MIT License. See `MIT.txt` for more information.

<p align="right">(<a href="#readme-top">back to top</a>)</p>