Metadata-Version: 2.1
Name: iggy
Version: 1.4.2
Summary: A tool for consistency based analysis of influence graphs and observed systems behavior.
Home-page: http://bioasp.github.io/iggy/
Author: Sven Thiele
Author-email: sthiele78@gmail.com
License: GPLv3+
Description: Installation
        ------------
        
        
        You can install iggy by running::
        
        	$ pip install --user iggy
        
        On Linux the executable scripts can then be found in ``~/.local/bin``
        
        and on MacOS the scripts are under ``/Users/YOURUSERNAME/Library/Python/3.5/bin``.
        
        
        Usage
        -----
        
        Typical usage is::
        
        	$ iggy.py network.sif observation.obs --show_labelings 10 --show_predictions
        
        For more options you can ask for help as follows::
        
        	$ iggy.py -h 		
        	usage: iggy.py [-h] [--no_fwd_propagation] [--no_founded_constraints]
        		       [--elempath] [--depmat] [--mics] [--autoinputs] [--scenfit]
        		       [--show_labelings SHOW_LABELINGS] [--show_predictions]
        		       networkfile observationfile
        
        	Iggy confronts biological networks given as interaction graphs with
        	experimental observations given as signs that represent the concentration
        	changes between two measured states. Iggy supports the incorporation of
        	uncertain measurements, discovers inconsistencies in data or network, applies
        	minimal repairs, and predicts the behavior of unmeasured species. In
        	particular, it distinguishes strong predictions (e.g. increase of a node
        	level) and weak predictions (e.g., node level increases or remains unchanged).
        
        	positional arguments:
        	  networkfile           influence graph in SIF format
        	  observationfile       observations in bioquali format
        
        	optional arguments:
        	  -h, --help            show this help message and exit
        	  --no_fwd_propagation  turn forward propagation OFF, default is ON
        	  --no_founded_constraints
        				turn constraints OFF that every variation must be
        				founded in an input, default is ON
        	  --elempath            a change must be explained by an elementary path from
        				an input.
        	  --depmat              combines multiple states, a change must be explained
        				by an elementary path from an input.
        	  --mics                compute minimal inconsistent cores
        	  --autoinputs          compute possible inputs of the network (nodes with
        				indegree 0)
        	  --scenfit             compute scenfit of the data, default is mcos
        	  --show_labelings SHOW_LABELINGS
        				number of labelings to print, default is OFF, 0=all
        	  --show_predictions    show predictions
        
        
        The second script contained is opt_graph.py
        Typical usage is::
        
        	$ opt_graph.py network.sif observations_dir/ --show_repairs 10
        
        For more options you can ask for help as follows::
        
        	$ opt_graph.py -h 	
        	usage: opt_graph.py [-h] [--no_fwd_propagation] [--no_founded_constraints]
        			    [--elempath] [--depmat] [--autoinputs]
        			    [--show_repairs SHOW_REPAIRS] [--repair_mode REPAIR_MODE]
        			    networkfile observationfiles
        
        	Opt-graph confronts a biological network given as interaction graphs with sets
        	of experimental observations given as signs that represent the concentration
        	changes between two measured states. Opt-graph computes the networks fitting
        	the observation data by removing (or adding) a minimal number of edges in the
        	given network
        
        	positional arguments:
        	  networkfile           influence graph in SIF format
        	  observationfiles      directory of observations in bioquali format
        
        	optional arguments:
        	  -h, --help            show this help message and exit
        	  --no_fwd_propagation  turn forward propagation OFF, default is ON
        	  --no_founded_constraints
        				turn constraints OFF that every variation must be
        				founded in an input, default is ON
        	  --elempath            a change must be explained by an elementary path from
        				an input.
        	  --depmat              combines multiple states, a change must be explained
        				by an elementary path from an input.
        	  --autoinputs          compute possible inputs of the network (nodes with
        				indegree 0)
        	  --show_repairs SHOW_REPAIRS
        				number of repairs to show, default is OFF, 0=all
        	  --repair_mode REPAIR_MODE
        				choose repair mode: 1 = remove edges (default), 2 = add +
        				remove edges (opt-graph), 3 = flip edges
        
        
        Samples
        -------
        
        Sample files available here: demo_data.tar.gz_ 
        
        .. _demo_data.tar.gz: https://bioasp.github.io/iggy/downloads/demo_data.tar.gz
        
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Description-Content-Type: text/markdown
