# MuVI
A multi-view latent variable model with domain-informed structured sparsity, that integrates noisy domain expertise in terms of feature sets.
## Quick links
[Examples](examples/1_basic_tutorial.ipynb) | [Paper](https://proceedings.mlr.press/v206/qoku23a/qoku23a.pdf) | [BibTeX](citation.bib)
## Setup
We suggest using [conda](https://docs.conda.io/en/latest/miniconda.html) to manage your environments, and either [pip](https://pypi.org/project/pip/) or [poetry](https://python-poetry.org/) to install `muvi` as a python package. Follow these steps to get `muvi` up and running!
### Remotely
1. Create a python environment in `conda`:
```bash
conda create -n muvi python=3.9
```
2. Activate freshly created environment:
```bash
source activate muvi
```
3. Install `muvi` with `pip`:
```bash
python3 -m pip install git+https://github.com/MLO-lab/MuVI.git
```
### Locally
1. Clone repository:
```bash
git clone https://github.com/MLO-lab/MuVI.git
```
2. Create a python environment in `conda`:
```bash
conda create -n muvi python=3.9
```
3. Activate freshly created environment:
```bash
source activate muvi
```
4. Install `muvi` with `poetry`:
```bash
cd MuVI
poetry install
```
## Getting started
Check out [basic tutorial](examples/1_basic_tutorial.ipynb) to get familiar with MuVI!
## Citation
If you use `MuVI` in your work, please use this [BibTeX](citation.bib) entry:
> **Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity**
>
> Arber Qoku and Florian Buettner
>
> _International Conference on Artificial Intelligence and Statistics (AISTATS)_ 2023
>
> <https://proceedings.mlr.press/v206/qoku23a.html>
Raw data
{
"_id": null,
"home_page": "https://github.com/MLO-lab/MuVI",
"name": "muvi",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.9,<3.11",
"maintainer_email": "",
"keywords": "multi-view,multi-omics,feature sets,latent variable model,structured sparsity,variational inference,single-cell",
"author": "Arber Qoku",
"author_email": "arber.qoku@dkfz-heidelberg.com",
"download_url": "https://files.pythonhosted.org/packages/09/84/a7787b3588fadf0ddeecfb996c9a139b161d153b58bbbc73b6d524abea96/muvi-0.1.1.tar.gz",
"platform": null,
"description": "# MuVI\n\nA multi-view latent variable model with domain-informed structured sparsity, that integrates noisy domain expertise in terms of feature sets.\n\n## Quick links\n\n[Examples](examples/1_basic_tutorial.ipynb) | [Paper](https://proceedings.mlr.press/v206/qoku23a/qoku23a.pdf) | [BibTeX](citation.bib)\n\n## Setup\n\nWe suggest using [conda](https://docs.conda.io/en/latest/miniconda.html) to manage your environments, and either [pip](https://pypi.org/project/pip/) or [poetry](https://python-poetry.org/) to install `muvi` as a python package. Follow these steps to get `muvi` up and running!\n\n### Remotely\n\n1. Create a python environment in `conda`:\n\n```bash\nconda create -n muvi python=3.9\n```\n\n2. Activate freshly created environment:\n\n```bash\nsource activate muvi\n```\n\n3. Install `muvi` with `pip`:\n\n```bash\npython3 -m pip install git+https://github.com/MLO-lab/MuVI.git\n```\n\n### Locally\n\n1. Clone repository:\n\n```bash\ngit clone https://github.com/MLO-lab/MuVI.git\n```\n\n2. Create a python environment in `conda`:\n\n```bash\nconda create -n muvi python=3.9\n```\n\n3. Activate freshly created environment:\n\n```bash\nsource activate muvi\n```\n\n4. Install `muvi` with `poetry`:\n\n```bash\ncd MuVI\npoetry install\n```\n\n## Getting started\n\nCheck out [basic tutorial](examples/1_basic_tutorial.ipynb) to get familiar with MuVI!\n\n## Citation\n\nIf you use `MuVI` in your work, please use this [BibTeX](citation.bib) entry:\n\n> **Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity**\n>\n> Arber Qoku and Florian Buettner\n>\n> _International Conference on Artificial Intelligence and Statistics (AISTATS)_ 2023\n>\n> <https://proceedings.mlr.press/v206/qoku23a.html>\n",
"bugtrack_url": null,
"license": "",
"summary": "MuVI: A multi-view latent variable model with domain-informed structured sparsity for integrating noisy feature sets.",
"version": "0.1.1",
"project_urls": {
"Homepage": "https://github.com/MLO-lab/MuVI",
"Repository": "https://github.com/MLO-lab/MuVI"
},
"split_keywords": [
"multi-view",
"multi-omics",
"feature sets",
"latent variable model",
"structured sparsity",
"variational inference",
"single-cell"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "eda1fb3f57357d0e00cca98ca13d604ef63a67d47499f5bbf67b30b0088ad7b8",
"md5": "74da4a3464fac3c682713ff3746a3b5b",
"sha256": "22de749679e35fe717e2a8c1440ebe72538b733d8aaac97d4ff1b9788d8e5e12"
},
"downloads": -1,
"filename": "muvi-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "74da4a3464fac3c682713ff3746a3b5b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9,<3.11",
"size": 46559,
"upload_time": "2023-10-23T20:13:28",
"upload_time_iso_8601": "2023-10-23T20:13:28.259452Z",
"url": "https://files.pythonhosted.org/packages/ed/a1/fb3f57357d0e00cca98ca13d604ef63a67d47499f5bbf67b30b0088ad7b8/muvi-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0984a7787b3588fadf0ddeecfb996c9a139b161d153b58bbbc73b6d524abea96",
"md5": "a900e5cc64834a892619cf7ca7bbdf01",
"sha256": "44a2f6633981007b34aba8d1016038920e86d077dde83e39cd22d6c0509c8ac5"
},
"downloads": -1,
"filename": "muvi-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "a900e5cc64834a892619cf7ca7bbdf01",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9,<3.11",
"size": 43560,
"upload_time": "2023-10-23T20:13:29",
"upload_time_iso_8601": "2023-10-23T20:13:29.762036Z",
"url": "https://files.pythonhosted.org/packages/09/84/a7787b3588fadf0ddeecfb996c9a139b161d153b58bbbc73b6d524abea96/muvi-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-23 20:13:29",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "MLO-lab",
"github_project": "MuVI",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "muvi"
}
JSON
