Metadata-Version: 2.1
Name: sift-sc
Version: 0.1.0
Summary: Biological signal filtering in single-cell data.
Home-page: https://github.com/nitzanlab/sift-sc
License: BSD-3-Clause
Author: Zoe Piran
Author-email: zoe.piran@mail.huji.ac.il
Requires-Python: >=3.8,<4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
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 :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
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Provides-Extra: docs
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Provides-Extra: test
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Project-URL: Documentation, https://sift-sc.readthedocs.io/
Description-Content-Type: text/x-rst

SiFT - Biological signal filtering in single-cell data
======================================================

.. image:: https://raw.githubusercontent.com/nitzanlab/sift-sc/main/docs/_static/img/sift_gc.png
    :width: 200px
    :align: center
    :alt: SiFT logo

Signal FilTering is a tool for uncovering hidden biological processes in single-cell data.
It can be applied to a wide range of tasks, from the removal of unwanted variation as a pre-processing step,
through revealing hidden biological structure by utilizing prior knowledge with respect to existing signal,
to uncovering trajectories of interest using reference data to remove unwanted variation.

.. image:: https://raw.githubusercontent.com/nitzanlab/sift-sc/main/docs/_static/img/sift_abs.png
    :width: 600px
    :align: center
    :alt: SiFT pipeline

Visit our `documentation`_ for installation, tutorials, examples and more.

Manuscript
----------
Please see our manuscript `Zoe Piran and Mor Nitzan (2022)`_.

Installation
------------
Install SiFT via PyPI by running::

    pip install sift-sc

.. _documentation: https://sift-sc.readthedocs.io/
.. _Zoe Piran and Mor Nitzan (2022): https://github.com/nitzanlab/sift-sc

