Learning of Protein Signaling Logic Models powered by BioASP and CellNOptR
Project description
==========================
caspo :- BioASP, CellNOpt.
==========================
caspo combines BioASP_ and CellNOpt_ to provide an easy to use software for learning Protein Signaling Logic Models from a Prior Knowledge Network in `.sif format`_ and a phospho-proteomics dataset in `MIDAS format`_.
.. _BioASP: http://www.cs.uni-potsdam.de/~sthiele/bioasp/
.. _CellNOpt: http://www.ebi.ac.uk/saezrodriguez/cno/
.. _`.sif format`: http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats
.. _`MIDAS format`: http://www.ebi.ac.uk/saezrodriguez/cno/doc/cnodocs/midas.html
Installation
============
You can install caspo by running::
$ pip install caspo
caspo will try to install the R_ package CellNOpt_ (R_ must be already installed). Note that you may need root access for this. Otherwise, you can install CellNOpt_ manually from the R_ console and use a virtualenv_ to install caspo.
.. _R: http://www.r-project.org/
.. _virtualenv: http://pypi.python.org/pypi/virtualenv
Usage
=====
Typical usage is::
$ caspo.py pkn.sif midas.csv
For more options you can ask for help as follows::
$ caspo.py --help
Usage: caspo.py [options] pkn.sif midas.csv
Options:
-h, --help show this help message and exit
-t T, --tolerance=T Suboptimal enumeration tolerance (Default to 0)
-p P, --discrete=P Discretization range exponent: 10^P (Default to 2)
-q Q, --alpha=Q Size penalty exponent 1/10^Q (Default to 5)
-g, --gtts Compute Global Truth Tables (Default to False). This
could take some time for many models.
-o O, --outdir=O Output directory path (Default to current directory)
Samples
=======
Sample files are available here:
ExtLiverPCB.sif_ ExtLiverPCB.csv_
.. _ExtLiverPCB.sif: http://www.cs.uni-potsdam.de/~sthiele/bioasp/downloads/samples/liverdata/ExtLiverPCB.sif
.. _ExtLiverPCB.csv: http://www.cs.uni-potsdam.de/~sthiele/bioasp/downloads/samples/liverdata/ExtLiverPCB.csv
Output
======
By default, the output of caspo will be 4 comma-separated-values files::
- models.csv: Matrix representation of logic models
- frequencies.csv: Frequencies of hyperedges occurrence
- exclusives.csv: Mutual exclusives hyperedges with their corresponding frequencies
- inclusives.csv: Mutual inclusives hyperedges with their corresponding frequencies
When using the -g option, caspo will also output::
- gtt_stats.csv: Basic cluster analysis.
- gtt-%i.csv: Explicit computation of each Global Truth Table
caspo :- BioASP, CellNOpt.
==========================
caspo combines BioASP_ and CellNOpt_ to provide an easy to use software for learning Protein Signaling Logic Models from a Prior Knowledge Network in `.sif format`_ and a phospho-proteomics dataset in `MIDAS format`_.
.. _BioASP: http://www.cs.uni-potsdam.de/~sthiele/bioasp/
.. _CellNOpt: http://www.ebi.ac.uk/saezrodriguez/cno/
.. _`.sif format`: http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats
.. _`MIDAS format`: http://www.ebi.ac.uk/saezrodriguez/cno/doc/cnodocs/midas.html
Installation
============
You can install caspo by running::
$ pip install caspo
caspo will try to install the R_ package CellNOpt_ (R_ must be already installed). Note that you may need root access for this. Otherwise, you can install CellNOpt_ manually from the R_ console and use a virtualenv_ to install caspo.
.. _R: http://www.r-project.org/
.. _virtualenv: http://pypi.python.org/pypi/virtualenv
Usage
=====
Typical usage is::
$ caspo.py pkn.sif midas.csv
For more options you can ask for help as follows::
$ caspo.py --help
Usage: caspo.py [options] pkn.sif midas.csv
Options:
-h, --help show this help message and exit
-t T, --tolerance=T Suboptimal enumeration tolerance (Default to 0)
-p P, --discrete=P Discretization range exponent: 10^P (Default to 2)
-q Q, --alpha=Q Size penalty exponent 1/10^Q (Default to 5)
-g, --gtts Compute Global Truth Tables (Default to False). This
could take some time for many models.
-o O, --outdir=O Output directory path (Default to current directory)
Samples
=======
Sample files are available here:
ExtLiverPCB.sif_ ExtLiverPCB.csv_
.. _ExtLiverPCB.sif: http://www.cs.uni-potsdam.de/~sthiele/bioasp/downloads/samples/liverdata/ExtLiverPCB.sif
.. _ExtLiverPCB.csv: http://www.cs.uni-potsdam.de/~sthiele/bioasp/downloads/samples/liverdata/ExtLiverPCB.csv
Output
======
By default, the output of caspo will be 4 comma-separated-values files::
- models.csv: Matrix representation of logic models
- frequencies.csv: Frequencies of hyperedges occurrence
- exclusives.csv: Mutual exclusives hyperedges with their corresponding frequencies
- inclusives.csv: Mutual inclusives hyperedges with their corresponding frequencies
When using the -g option, caspo will also output::
- gtt_stats.csv: Basic cluster analysis.
- gtt-%i.csv: Explicit computation of each Global Truth Table
