Metadata-Version: 1.2
Name: pyscenic
Version: 0.10.1
Summary: Python implementation of the SCENIC pipeline for transcription factor inference from single-cell transcriptomics experiments.
Home-page: https://github.com/aertslab/pySCENIC
Author: Bram Van de Sande
License: GPL-3.0+
Description: pySCENIC
        ========
        
        |buildstatus|_ |pypipackage|_ |docstatus|_
        
        pySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and
        Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from
        single-cell RNA-seq data.
        
        The pioneering work was done in R and results were published in Nature Methods [1]_.
        
        pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
        in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
        
        **Full documentation** is available on `Read the Docs <https://pyscenic.readthedocs.io/en/latest/>`_
        
        News
        ----
        
        2020-02-27
        ^^^^^^^^^^
        
        **0.10.0 release**
        
        * Added a helper script `arboreto_with_multiprocessing.py <https://github.com/aertslab/pySCENIC/blob/master/scripts/arboreto_with_multiprocessing.py>`_ that runs the Arboreto GRN algorithms (GRNBoost2, GENIE3) without Dask for compatibility.
        
        * Ability to set a fixed seed in both the AUCell step and in the calculation of regulon thresholds (CLI parameter :code:`--seed`; aucell function parameter :code:`seed`).
        
        * (since 0.9.18) In the modules_from_adjacencies function, the default value of :code:`rho_mask_dropouts` is changed to False. This now matches the behavior of the R version of SCENIC. The cli version has an additional option to turn dropout masking back on (:code:`--mask_dropouts`).
        
        Overview
        --------
        
        The pipeline has three steps:
        
        1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboreto_ package is used for this step.
        2. These regulons are refined by pruning targets that do not have an enrichment for a corresponding motif of the TF effectively separating direct from indirect targets based on the presence of cis-regulatory footprints.
        3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.
        
        The most impactful speed improvement is introduced by the arboreto_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.
        
        
        All the functionality of the original R implementation is available and in addition:
        
        1. You can leverage multi-core and multi-node clusters using dask_ and its distributed_ scheduler.
        2. We implemented a version of the recovery of input genes that takes into account weights associated with these genes.
        3. Regulons, i.e. the regulatory network that connects a TF with its target genes, with targets that are repressed are now also derived and used for cell enrichment analysis.
        
        
        Website
        -------
        
        For more information, please visit LCB_, SCENIC_ (R version), or SCENICprotocol_ (for a Nextflow implementation).
        
        
        Acknowledgments
        ---------------
        
        We are grateful to all providers of TF-annotated position weight matrices, in particular Martha Bulyk (UNIPROBE), Wyeth Wasserman and Albin Sandelin (JASPAR), BioBase (TRANSFAC), Scot Wolfe and Michael Brodsky (FlyFactorSurvey) and Timothy Hughes (cisBP).
        
        
        References
        ----------
        
        .. [1] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017).
        .. [2] Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org
        .. [3] Huynh-Thu, V. A. et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, (2010).
        .. [4] Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
        
        .. |buildstatus| image:: https://travis-ci.org/aertslab/pySCENIC.svg?branch=master
        .. _buildstatus: https://travis-ci.org/aertslab/pySCENIC
        
        .. |pypipackage| image:: https://badge.fury.io/py/pyscenic.svg
        .. _pypipackage: https://badge.fury.io/py/pyscenic
        
        .. |docstatus| image:: https://readthedocs.org/projects/pyscenic/badge/?version=latest
        .. _docstatus: http://pyscenic.readthedocs.io/en/latest/?badge=latest
        
        .. _SCENIC: http://scenic.aertslab.org
        .. _dask: https://dask.pydata.org/en/latest/
        .. _distributed: https://distributed.readthedocs.io/en/latest/
        .. _arboreto: https://arboreto.readthedocs.io
        .. _LCB: https://aertslab.org
        .. _`SCENICprotocol`: https://github.com/aertslab/SCENICprotocol
        .. _notebooks: https://github.com/aertslab/pySCENIC/tree/master/notebooks
        .. _issue: https://github.com/aertslab/pySCENIC/issues/new
        .. _PyPI: https://pypi.python.org/pypi/pyscenic
        
        
Keywords: single-cell transcriptomics gene-regulatory-network transcription-factors
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6
