search

ingmbioinfo / cia

CIA (Cluster Independent Annotation) is a new computational tool that enables a highly accurate automatic classification of cells in scRNA-seq datasets exploiting gene signatures.
MIT License
1 stars 0 forks source link

readme

.. These are examples of badges you might want to add to your README: please update the URLs accordingly

.. image:: https://api.cirrus-ci.com/github/<USER>/CIA.svg?branch=main
    :alt: Built Status
    :target: https://cirrus-ci.com/github/<USER>/CIA
.. image:: https://readthedocs.org/projects/CIA/badge/?version=latest
    :alt: ReadTheDocs
    :target: https://CIA.readthedocs.io/en/stable/
.. image:: https://img.shields.io/coveralls/github/<USER>/CIA/main.svg
    :alt: Coveralls
    :target: https://coveralls.io/r/<USER>/CIA
.. image:: https://img.shields.io/pypi/v/CIA.svg
    :alt: PyPI-Server
    :target: https://pypi.org/project/CIA/
.. image:: https://img.shields.io/conda/vn/conda-forge/CIA.svg
    :alt: Conda-Forge
    :target: https://anaconda.org/conda-forge/CIA
.. image:: https://pepy.tech/badge/CIA/month
    :alt: Monthly Downloads
    :target: https://pepy.tech/project/CIA
.. image:: https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter
    :alt: Twitter
    :target: https://twitter.com/CIA

.. image:: https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold :alt: Project generated with PyScaffold :target: https://pyscaffold.org/

.. image:: docs/logo.png :width: 200 :align: center

|

=== CIA

 CIA (Cluster Independent Annotation) is a new computational tool that enables a highly accurate automatic classification of cells in scRNA-seq datasets exploiting gene signatures.

Given a collection of signatures, CIA synthesizes the information of each signature expression in a single score value for each cell. Comparing the score values, the package assigns labels to each cell accordingly with the top scored signature. This classifier allows the use of different modes, depending on whether the user prefers a faster analysis (useful to get an initial idea about the clustering parameters to choose) or a more statistically accurate analysis; in the second case, CIA exploits the comparison of the obtained signature scores with randomic signature scores, with the possibility to filter the scores by their distribution or their significance, and allowing the comparison of genesets with lengths spanning from tens to thousands genes.

============ Installation

Package is available only on github. To install it locally, run the following commands:

git clone https://github.com/ingmbioinfo/cia.git

cd cia

pip install -e .