.. image:: img/arboreto.png
:alt: arboreto
:scale: 100%
:align: left
.. image:: https://travis-ci.com/aertslab/arboreto.svg?branch=master
:alt: Build Status
:target: https://travis-ci.com/aertslab/arboreto
.. image:: https://readthedocs.org/projects/arboreto/badge/?version=latest
:alt: Documentation Status
:target: http://arboreto.readthedocs.io/en/latest/?badge=latest
.. image:: https://anaconda.org/bioconda/arboreto/badges/version.svg
:alt: Bioconda package
:target: https://anaconda.org/bioconda/arboreto
.. image:: https://img.shields.io/pypi/v/arboreto
:alt: PyPI package
:target: https://pypi.org/project/arboreto/
----
.. epigraph::
*The most satisfactory definition of man from the scientific point of view is probably Man the Tool-maker.*
.. _arboreto: https://arboreto.readthedocs.io
.. _`arboreto documentation`: https://arboreto.readthedocs.io
.. _notebooks: https://github.com/tmoerman/arboreto/tree/master/notebooks
.. _issue: https://github.com/tmoerman/arboreto/issues/new
.. _dask: https://dask.pydata.org/en/latest/
.. _`dask distributed`: https://distributed.readthedocs.io/en/latest/
.. _GENIE3: http://www.montefiore.ulg.ac.be/~huynh-thu/GENIE3.html
.. _`Random Forest`: https://en.wikipedia.org/wiki/Random_forest
.. _ExtraTrees: https://en.wikipedia.org/wiki/Random_forest#ExtraTrees
.. _`Stochastic Gradient Boosting Machine`: https://en.wikipedia.org/wiki/Gradient_boosting#Stochastic_gradient_boosting
.. _`early-stopping`: https://en.wikipedia.org/wiki/Early_stopping
Inferring a gene regulatory network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances
in high-throughput gene profiling technology.
The arboreto_ software library addresses this issue by providing a computational strategy that allows executing the class of GRN inference algorithms
exemplified by GENIE3_ [1] on hardware ranging from a single computer to a multi-node compute cluster. This class of GRN inference algorithms is defined by
a series of steps, one for each target gene in the dataset, where the most important candidates from a set of regulators are determined from a regression
model to predict a target gene's expression profile.
Members of the above class of GRN inference algorithms are attractive from a computational point of view because they are parallelizable by nature. In arboreto,
we specify the parallelizable computation as a dask_ graph [2], a data structure that represents the task schedule of a computation. A dask scheduler assigns the
tasks in a dask graph to the available computational resources. Arboreto uses the `dask distributed`_ scheduler to
spread out the computational tasks over multiple processes running on one or multiple machines.
Arboreto currently supports 2 GRN inference algorithms:
1. **GRNBoost2**: a novel and fast GRN inference algorithm using `Stochastic Gradient Boosting Machine`_ (SGBM) [3] regression with `early-stopping`_ regularization.
2. **GENIE3**: the classic GRN inference algorithm using `Random Forest`_ (RF) or ExtraTrees_ (ET) regression.
Get Started
***********
Arboreto was conceived with the working bioinformatician or data scientist in mind. We provide extensive documentation and examples to help you get up to speed with the library.
* Read the `arboreto documentation`_.
* Browse example notebooks_.
* Report an issue_.
License
*******
BSD 3-Clause License
pySCENIC
========
.. _pySCENIC: https://github.com/aertslab/pySCENIC
.. _SCENIC: https://aertslab.org/#scenic
Arboreto is a component in pySCENIC_: a lightning-fast python implementation of
the SCENIC_ pipeline [5] (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.
References
**********
1. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE
2. Rocklin, M. (2015). Dask: parallel computation with blocked algorithms and task scheduling. In Proceedings of the 14th Python in Science Conference (pp. 130-136).
3. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.
4. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., ... & Dream5 Consortium. (2012). Wisdom of crowds for robust gene network inference. Nature methods, 9(8), 796-804.
5. Aibar S, Bravo Gonzalez-Blas C, Moerman T, Wouters J, Huynh-Thu VA, Imrichova H, Kalender Atak Z, Hulselmans G, Dewaele M, Rambow F, Geurts P, Aerts J, Marine C, van den Oord J, Aerts S. SCENIC: Single-cell regulatory network inference and clustering. Nature Methods 14, 1083–1086 (2017). doi: 10.1038/nmeth.4463
Raw data
{
"_id": null,
"home_page": "https://github.com/aertslab/arboreto",
"name": "arboreto",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "gene,regulatory,network,inference,regression,ensemble,scalable,dask",
"author": "Thomas Moerman",
"author_email": "thomas.moerman@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/d8/b2/1942195d3848abf64b8115e219c4a530b05798f7332938dfd0e80b93c464/arboreto-0.1.6.tar.gz",
"platform": "any",
"description": ".. image:: img/arboreto.png\n :alt: arboreto\n :scale: 100%\n :align: left\n\n.. image:: https://travis-ci.com/aertslab/arboreto.svg?branch=master\n :alt: Build Status\n :target: https://travis-ci.com/aertslab/arboreto\n\n.. image:: https://readthedocs.org/projects/arboreto/badge/?version=latest\n :alt: Documentation Status\n :target: http://arboreto.readthedocs.io/en/latest/?badge=latest\n\n.. image:: https://anaconda.org/bioconda/arboreto/badges/version.svg\n :alt: Bioconda package\n :target: https://anaconda.org/bioconda/arboreto\n\n.. image:: https://img.shields.io/pypi/v/arboreto\n :alt: PyPI package\n :target: https://pypi.org/project/arboreto/\n\n----\n\n.. epigraph::\n\n *The most satisfactory definition of man from the scientific point of view is probably Man the Tool-maker.*\n\n.. _arboreto: https://arboreto.readthedocs.io\n.. _`arboreto documentation`: https://arboreto.readthedocs.io\n.. _notebooks: https://github.com/tmoerman/arboreto/tree/master/notebooks\n.. _issue: https://github.com/tmoerman/arboreto/issues/new\n\n.. _dask: https://dask.pydata.org/en/latest/\n.. _`dask distributed`: https://distributed.readthedocs.io/en/latest/\n\n.. _GENIE3: http://www.montefiore.ulg.ac.be/~huynh-thu/GENIE3.html\n.. _`Random Forest`: https://en.wikipedia.org/wiki/Random_forest\n.. _ExtraTrees: https://en.wikipedia.org/wiki/Random_forest#ExtraTrees\n.. _`Stochastic Gradient Boosting Machine`: https://en.wikipedia.org/wiki/Gradient_boosting#Stochastic_gradient_boosting\n.. _`early-stopping`: https://en.wikipedia.org/wiki/Early_stopping\n\nInferring a gene regulatory network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances\nin high-throughput gene profiling technology.\n\nThe arboreto_ software library addresses this issue by providing a computational strategy that allows executing the class of GRN inference algorithms\nexemplified by GENIE3_ [1] on hardware ranging from a single computer to a multi-node compute cluster. This class of GRN inference algorithms is defined by\na series of steps, one for each target gene in the dataset, where the most important candidates from a set of regulators are determined from a regression\nmodel to predict a target gene's expression profile.\n\nMembers of the above class of GRN inference algorithms are attractive from a computational point of view because they are parallelizable by nature. In arboreto,\nwe specify the parallelizable computation as a dask_ graph [2], a data structure that represents the task schedule of a computation. A dask scheduler assigns the\ntasks in a dask graph to the available computational resources. Arboreto uses the `dask distributed`_ scheduler to\nspread out the computational tasks over multiple processes running on one or multiple machines.\n\nArboreto currently supports 2 GRN inference algorithms:\n\n1. **GRNBoost2**: a novel and fast GRN inference algorithm using `Stochastic Gradient Boosting Machine`_ (SGBM) [3] regression with `early-stopping`_ regularization.\n2. **GENIE3**: the classic GRN inference algorithm using `Random Forest`_ (RF) or ExtraTrees_ (ET) regression.\n\nGet Started\n***********\n\nArboreto was conceived with the working bioinformatician or data scientist in mind. We provide extensive documentation and examples to help you get up to speed with the library.\n\n* Read the `arboreto documentation`_.\n* Browse example notebooks_.\n* Report an issue_.\n\nLicense\n*******\n\nBSD 3-Clause License\n\npySCENIC\n========\n\n.. _pySCENIC: https://github.com/aertslab/pySCENIC\n.. _SCENIC: https://aertslab.org/#scenic\n\nArboreto is a component in pySCENIC_: a lightning-fast python implementation of\nthe SCENIC_ pipeline [5] (Single-Cell rEgulatory Network Inference and Clustering)\nwhich enables biologists to infer transcription factors, gene regulatory networks\nand cell types from single-cell RNA-seq data.\n\nReferences\n**********\n\n1. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE\n2. Rocklin, M. (2015). Dask: parallel computation with blocked algorithms and task scheduling. In Proceedings of the 14th Python in Science Conference (pp. 130-136).\n3. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.\n4. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., ... & Dream5 Consortium. (2012). Wisdom of crowds for robust gene network inference. Nature methods, 9(8), 796-804.\n5. Aibar S, Bravo Gonzalez-Blas C, Moerman T, Wouters J, Huynh-Thu VA, Imrichova H, Kalender Atak Z, Hulselmans G, Dewaele M, Rambow F, Geurts P, Aerts J, Marine C, van den Oord J, Aerts S. SCENIC: Single-cell regulatory network inference and clustering. Nature Methods 14, 1083\u20131086 (2017). doi: 10.1038/nmeth.4463\n\n\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License",
"summary": "Scalable gene regulatory network inference using tree-based ensemble regressors",
"version": "0.1.6",
"split_keywords": [
"gene",
"regulatory",
"network",
"inference",
"regression",
"ensemble",
"scalable",
"dask"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "eb58a93dc468b7145743a477ff5bc9a6",
"sha256": "6c70074b9d7273efaed0f89dd508c886b83c22ef81ae07ca923b7d21e7bbd057"
},
"downloads": -1,
"filename": "arboreto-0.1.6-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "eb58a93dc468b7145743a477ff5bc9a6",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": null,
"size": 15504,
"upload_time": "2021-02-09T11:20:19",
"upload_time_iso_8601": "2021-02-09T11:20:19.838572Z",
"url": "https://files.pythonhosted.org/packages/91/26/8c4a9191c2d31c4f30aecd4382bcc209b67629b827955fb164ce03c09e08/arboreto-0.1.6-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "2dc1577ddbb8cf6fc5416b9fa6d4eca6",
"sha256": "32fdac5e8a3e0ef2e196b5827f067d815ac4e689d2fca0dc437f42abdeeb89ab"
},
"downloads": -1,
"filename": "arboreto-0.1.6.tar.gz",
"has_sig": false,
"md5_digest": "2dc1577ddbb8cf6fc5416b9fa6d4eca6",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 14625,
"upload_time": "2021-02-09T11:20:21",
"upload_time_iso_8601": "2021-02-09T11:20:21.423935Z",
"url": "https://files.pythonhosted.org/packages/d8/b2/1942195d3848abf64b8115e219c4a530b05798f7332938dfd0e80b93c464/arboreto-0.1.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2021-02-09 11:20:21",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "aertslab",
"github_project": "arboreto",
"travis_ci": true,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "dask",
"specs": []
},
{
"name": "distributed",
"specs": []
},
{
"name": "numpy",
"specs": [
[
">=",
"1.16.5"
]
]
},
{
"name": "pandas",
"specs": []
},
{
"name": "scikit-learn",
"specs": []
},
{
"name": "scipy",
"specs": []
}
],
"lcname": "arboreto"
}
JSON
