semvec/pypi-packages huggingface dataset:
ClustAssessPy 1.0.2
Python package for systematic assessment of clustering results stability on single-cell data.
License: MIT
Requires Python: >=3.7
Keywords: clustering, stability, assessment, machine learning, graph, network, community, detection

# ClustAssessPy

[![Downloads](https://static.pepy.tech/badge/ClustAssessPy)](https://pepy.tech/project/ClustAssessPy)
[![Downloads](https://static.pepy.tech/badge/ClustAssessPy/week)](https://pepy.tech/project/ClustAssessPy)

ClustAssessPy offers a data-driven approach for optimizing parameter values across all stages of graph-based community detection clustering in single-cell datasets. This Python package is a lighter adaptation of ClustAssess (R) [1], incorporating its main functions and can be used by the Scanpy community to guide robust clustering through data-driven selection in all community detection clustering steps:

- **Dimensionality Reduction:** Selection of the base embedding (UMAP vs PCA) and the number and type of features (e.g., highly-variable vs most abundant).
- **Graph Type:** Choice of graph type for the adjacency matrix (nearest neighbors vs shared nearest neighbors) and the number of neighbors.
- **Clustering:** Identification of the most stable algorithm (Leiden or Louvain) and the appropriate resolution value.

## Installation

ClustAssessPy requires Python 3.7 or newer.

### Dependencies

- numpy
- pandas
- scanpy
- umap-learn
- seaborn
- matplotlib
- scipy
- networkx
- plotnine
- pynndescent
- leidenalg
- louvain
- igraph

### User Installation

We recommend that you download ClustAssessPy on a virtual environment (venv or Conda).

```sh
pip install ClustAssessPy
```

## Getting Started

Documentation for the main functions is available [here](https://core-bioinformatics.github.io/ClustAssessPy/ClustAssessPy.html). For a detailed tutorial, click [here](https://github.com/Core-Bioinformatics/ClustAssessPy/blob/main/Examples/tutorial_cuomo.ipynb).

## References

[1] Shahsavari, A., Munteanu, A., & Mohorianu, I. (2022). ClustAssess: Tools for Assessing the Robustness of Single-Cell Clustering. https://doi.org/10.1101/2022.01.31.478592
sgelt 4.9.0
SGELT: Site GEnerator for Labs and Teams
License: MIT
Requires Python: >=3.9

# SGELT: Site GEnerator for Labs and Teams

## Installation

* Create a python virtual environment :

```bash
virtualenv .venv/
```

* Activate it

```bash
source .venv/bin/activate
```

* Install the package and its dependencies :

```bash
pip install sgelt
```

## Build a website

### Files organization

* `config.yml` describes the site configuration
* `content/` dir contains markdown files
* `agenda.json` contains all events data
* `teams.json` contains teams data

### Edit content

* setup configuration in `config.yml`
* edit `content/` dir.
* generate json data files

### Build and serve

To build all site files, run :

```bash
sgelt
```

in the project directory.
Output files will be written in `output/` dir.

### Build and serve

To serve the website and rebuild automatically when the content has changed:

```bash
sgelt -a -s
```

To open the homepage in a web browser, add:

```bash
sgelt -a -s -b
```

### Get help with CLI

```bash
sgelt -h
```

## Tests

### Run tests using tox

Tox setup the appropriate environments and run tests using pytest:

```bash
pip install tox  # install tox
tox  # run
```

### Run tests using pytest

```bash
pip install -e .[tests]  # install dependencies for tests
pytest -sv tests/  # run tests
```