Metadata-Version: 2.4
Name: bja_utils
Version: 0.1.1
Summary: Convenience functions for mass spectrometry proteomics & lipidomics analysis, parsing, statisticss, biological interpretation, and plotting.
Author: Benton Anderson
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: pandas>=1.0.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: seaborn>=0.10.0
Requires-Dist: scipy
Requires-Dist: statsmodels>=0.11.1
Requires-Dist: plotly>6.0.0
Requires-Dist: pygoslin
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# bja_utils

[![PyPI version](https://img.shields.io/pypi/v/bja_utils.svg)](https://pypi.org/project/bja_utils/)
[![Python Version](https://img.shields.io/pypi/pyversions/bja_utils.svg)](https://pypi.org/project/bja_utils/)

`bja_utils` is a Python package providing convenience functions for **mass spectrometry proteomics and lipidomics analysis**, data parsing, statistics, biological interpretation, and plotting. It is designed to streamline common workflows and reduce repetitive coding for omics researchers.

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## Features

- Data processing and transformation
- Statistical analysis functions
- Visualization and plotting utilities
- Parsing tools for common data formats
- Biological interpretation helpers

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## Package Structure

The package is organized into the following modules:

### `processing`
Functions for data preprocessing, multi-processing of computationally intensive tasks, normalization, imputation, and cleaning. 
Includes tools for handling missing values, scaling, and aggregating omics data.

### `analysis`
Statistical functions and models for downstream analysis. Supports descriptive statistics, hypothesis testing, regression models, and more.

### `plotting`
Helper functions for common plotting tasks `matplotlib` and `plotly`. 
Simplifies custom styling, multi-panel figures, and specialized omics visualizations.

### `parsing`
Functions for reading, parsing, and converting glycoproteomics and lipidomics identifiers.

### `utils`
General helper functions for file handling, logging, and other repetitive tasks that donâ€™t fit into other modules.



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## Installation

You can install the latest release via PyPI:

```bash
pip install bja_utils
