Metadata-Version: 1.0
Name: caspo
Version: 1.2
Summary: Learning Boolean logic models of protein signaling networks powered by PyASP and CellNOptR
Home-page: http://packages.python.org/caspo/
Author: Sven Thiele, Santiago Videla
Author-email: sthiele@irisa.fr, santiago.videla@irisa.fr
License: GPLv3
Description: ==========================
        caspo :- PyASP, CellNOpt.
        ==========================
        
        caspo combines PyASP_ and CellNOpt_ to provide an easy to use software for learning Boolean logic models of protein signaling networks from a prior knowledge network in `.sif format`_ and a phospho-proteomics dataset in `MIDAS format`_.
        
        .. _PyASP: http://pypi.python.org/pypi/pyasp
        .. _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
        
        Note that you may need root (sudo) access for this. Otherwise, you can use a virtualenv_. Before using caspo make sure that R_ is already installed. The first time you run caspo, CellNOptR will be downloaded and installed in your R environment.
        
        .. _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: 0 <= t <= 0.5
        	                       (Default to 0)
        	  -p P, --discrete=P   discretization over the integer interval: [0,10^P]
        	                       (Default to 2)
        	  -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 for the `prior knowledge network`_ and the `phospho-proteomics dataset`_
        
        .. _`prior knowledge network`: http://www.cs.uni-potsdam.de/~sthiele/bioasp/downloads/samples/liverdata/ExtLiverPCB.sif
        .. _`phospho-proteomics dataset`: 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
        
        1.2 (2013-01-05)
        ----------------
        - Depends on PyASP instead of BioASP
        - Package the full ASP encoding
        - Implements old BioASP's functionality
        - Upgrade dependency on cellnopt.wrapper to 1.0.5
        
        1.1 (2012-12-20)
        ----------------
        - Removes CellNOpt installation relying on cellnopt.wrapper
        
        1.0 (2012-12-03)
        ----------------
        - Initial release
        
Platform: UNKNOWN
