Metadata-Version: 2.1
Name: GmGM
Version: 0.0.2
Summary: An implementation of the Gaussian multi-Graphical Model
Project-URL: Homepage, https://github.com/BaileyAndrew/GmGM-python
Project-URL: Bug Tracker, https://github.com/BaileyAndrew/GmGM-python/issues
Author-email: Bailey Andrew <sceba@leeds.ac.uk>
License: MIT License
        
        Copyright (c) 2023 BaileyAndrew
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Requires-Dist: dask>=2022.03
Requires-Dist: numba>=0.57
Requires-Dist: numpy>=1.23
Requires-Dist: scipy>=1.9
Description-Content-Type: text/markdown

# GmGM-python
A python package for the GmGM algorithm.  [Read the pre-print here.](https://arxiv.org/abs/2211.02920)

This is a very early version so the API is subject to change.

## Installation

```{bash}
# Pip
python -m pip install GmGM
```

Conda install coming soon.

## About

This package learns a graphical representation of every "axis" of your data.  For example, if you had a paired scRNA+scATAC multi-omics dataset, then your axes would be "genes" (columns of scRNA matrix), "axes" (columns of scATAC matrix), and "cells" (rows of both matrices).

This package works on any dataset that can be expressed as multiple tensors of arbitrary length (so multi-omics, videos, etc...).  The only restriction is that no tensor can have the same axis twice (no "genes x genes" matrix); the same axis can appear multiple times, as long as it only appears once per matrix.

## Usage

The first step is to express your dataset as a "Dataset" object.

## Roadmap

- [ ] Add direct support for AnnData and MuData objects (so that converson to "Dataset" is not needed)
- [ ] Stabilize API
- [ ] Add comprehensive docs