# ClustAssessPy

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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

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Avatar for rafaelkoll from gravatar.com rafaelkoll

Credits

Author: Rafael Kollyfas

License

MIT

Requires

Python >=3.7

Tags

clustering stability assessment machine learning graph network community detection

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Metadata

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for rafaelkoll from gravatar.com rafaelkoll

Credits

Author: Rafael Kollyfas

License

MIT

Requires

Python >=3.7

Tags

clustering stability assessment machine learning graph network community detection

Report project as malware

Release files for ClustAssessPy 1.0.2

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Source distribution (sdist)

Source distribution for ClustAssessPy 1.0.2
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Built distributions (wheels)

Table of built distributions (wheels) for ClustAssessPy 1.0.2
File
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ClustAssessPy-1.0.2-cp313-cp313-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details
ClustAssessPy-1.0.2-cp313-cp313-musllinux_1_2_i686.whl 1.2 MB CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-32 Details
ClustAssessPy-1.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 135.0 kB CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64 Details
ClustAssessPy-1.0.2-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl 140.7 kB CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-32 Details
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ClustAssessPy-1.0.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl 139.6 kB CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-32 Details
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ClustAssessPy-1.0.2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl 139.2 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ x86-32 Details
ClustAssessPy-1.0.2-cp38-cp38-macosx_11_0_arm64.whl 96.1 kB CPython 3.8 CPython 3.8 macOS 11.0+ ARM64 Details
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ClustAssessPy-1.0.2-cp37-cp37m-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ x86-64 Details
ClustAssessPy-1.0.2-cp37-cp37m-musllinux_1_2_i686.whl 1.2 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ x86-32 Details
ClustAssessPy-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 137.3 kB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64 Details
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