Set of tools and pipelines to analyse GDSC data (cancerrxgene.org)
Project description
GDSCtools
============
.. image:: https://badge.fury.io/py/gdsctools.svg
:target: https://pypi.python.org/pypi/gdsctools
.. image:: https://secure.travis-ci.org/CancerRxGene/gdsctools.png
:target: http://travis-ci.org/CancerRxGene/gdsctools
.. image:: https://coveralls.io/repos/CancerRxGene/gdsctools/badge.svg?branch=master&service=github
:target: https://coveralls.io/github/CancerRxGene/gdsctools?branch=master
.. image:: https://badge.waffle.io/CancerRxGene/gdsctools.png?label=issues&title=issues
:target: https://waffle.io/gdsctools/gdsctools
:Note: tested for PYthon 2.7, 3.3, 3.4
:Contributions: Please join https://github.com/CancerRxGene/gdsctools project
:Online documentation: `On pypi website <http://pythonhosted.org/gdsctools/>`_,
:Issues and bug reports: `On github <https://github.com/CancerRxGene/gdsctools/issues>
`_,
Overview
-----------
Genomics of Drug Sensitivity in Cancer (GDSC) tools including pipelines related to http://www.cancerrxgene.org/
Installation
---------------
::
pip install gdsctools
QuickStart
-------------
2 inputs matrices are required: (1) an IC50 matrix with COSMIC identifiers as rows and drugs as columns, and (2) a genomic feature matrix with COSMIC identifiers as rows and features as columns. Then, you can analyse the data with the standalone application::
gdsctools_anova --ic50 ic50.txt --feature features.txt --analyse-all
or as a script::
from gdsctools import anova, reader
ic50 = reader.IC50('ic50.txt')
features = reader.Features('features.txt')
an = ANOVA(ic50, features)
an.anova_all()
More examples are provided in the documentation. You can for example select a specific drug, or a set of drugs instead of the entire screening, or perform a sub selection on features.
.. note:: Version 1 (linear regression and ANOVA) was created based on https://github.com/francescojm/FI.GDSC.ANOVA
============
.. image:: https://badge.fury.io/py/gdsctools.svg
:target: https://pypi.python.org/pypi/gdsctools
.. image:: https://secure.travis-ci.org/CancerRxGene/gdsctools.png
:target: http://travis-ci.org/CancerRxGene/gdsctools
.. image:: https://coveralls.io/repos/CancerRxGene/gdsctools/badge.svg?branch=master&service=github
:target: https://coveralls.io/github/CancerRxGene/gdsctools?branch=master
.. image:: https://badge.waffle.io/CancerRxGene/gdsctools.png?label=issues&title=issues
:target: https://waffle.io/gdsctools/gdsctools
:Note: tested for PYthon 2.7, 3.3, 3.4
:Contributions: Please join https://github.com/CancerRxGene/gdsctools project
:Online documentation: `On pypi website <http://pythonhosted.org/gdsctools/>`_,
:Issues and bug reports: `On github <https://github.com/CancerRxGene/gdsctools/issues>
`_,
Overview
-----------
Genomics of Drug Sensitivity in Cancer (GDSC) tools including pipelines related to http://www.cancerrxgene.org/
Installation
---------------
::
pip install gdsctools
QuickStart
-------------
2 inputs matrices are required: (1) an IC50 matrix with COSMIC identifiers as rows and drugs as columns, and (2) a genomic feature matrix with COSMIC identifiers as rows and features as columns. Then, you can analyse the data with the standalone application::
gdsctools_anova --ic50 ic50.txt --feature features.txt --analyse-all
or as a script::
from gdsctools import anova, reader
ic50 = reader.IC50('ic50.txt')
features = reader.Features('features.txt')
an = ANOVA(ic50, features)
an.anova_all()
More examples are provided in the documentation. You can for example select a specific drug, or a set of drugs instead of the entire screening, or perform a sub selection on features.
.. note:: Version 1 (linear regression and ANOVA) was created based on https://github.com/francescojm/FI.GDSC.ANOVA
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