Package: iggy
# InstallationYou can install iggy by running:> pip install --user iggyOn 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`.# UsageTypical usage is:> iggy.py network.sif observation.obs --show_labelings 10 --show_predictionsFor more options you can ask for help as follows:> iggy.py -husage: iggy.py [-h] [--no_fwd_propagation] [--no_founded_constraints][--elempath] [--depmat] [--mics] [--autoinputs] [--scenfit][--show_labelings SHOW_LABELINGS] [--show_predictions]networkfile observationfileIggy confronts biological networks given as interaction graphs withexperimental observations given as signs that represent the concentrationchanges between two measured states. Iggy supports the incorporation ofuncertain measurements, discovers inconsistencies in data or network, appliesminimal repairs, and predicts the behavior of unmeasured species. Inparticular, it distinguishes strong predictions (e.g. increase of a nodelevel) and weak predictions (e.g., node level increases or remains unchanged).positional arguments:networkfile           influence graph in SIF formatobservationfile       observations in bioquali formatoptional arguments:-h, --help            show this help message and exit--no_fwd_propagation  turn forward propagation OFF, default is ON--no_founded_constraintsturn constraints OFF that every variation must befounded in an input, default is ON--elempath            a change must be explained by an elementary path froman input.--depmat              combines multiple states, a change must be explainedby an elementary path from an input.--mics                compute minimal inconsistent cores--autoinputs          compute possible inputs of the network (nodes withindegree 0)--scenfit             compute scenfit of the data, default is mcos--show_labelings SHOW_LABELINGSnumber of labelings to print, default is OFF, 0=all--show_predictions    show predictionsThe second script contained is opt_graph.pyTypical usage is:> opt_graph.py network.sif observations_dir/ --show_repairs 10For more options you can ask for help as follows:> opt_graph.py -husage: opt_graph.py [-h] [--no_fwd_propagation] [--no_founded_constraints][--elempath] [--depmat] [--autoinputs][--show_repairs SHOW_REPAIRS] [--repair_mode REPAIR_MODE]networkfile observationfilesOpt-graph confronts a biological network given as interaction graphs with setsof experimental observations given as signs that represent the concentrationchanges between two measured states. Opt-graph computes the networks fittingthe observation data by removing (or adding) a minimal number of edges in thegiven networkpositional arguments:networkfile           influence graph in SIF formatobservationfiles      directory of observations in bioquali formatoptional arguments:-h, --help            show this help message and exit--no_fwd_propagation  turn forward propagation OFF, default is ON--no_founded_constraintsturn constraints OFF that every variation must befounded in an input, default is ON--elempath            a change must be explained by an elementary path froman input.--depmat              combines multiple states, a change must be explainedby an elementary path from an input.--autoinputs          compute possible inputs of the network (nodes withindegree 0)--show_repairs SHOW_REPAIRSnumber of repairs to show, default is OFF, 0=all--repair_mode REPAIR_MODEchoose repair mode: 1 = remove edges (default), 2 = add +remove edges (opt-graph), 3 = flip edges# SamplesSample files available here: [demo_data.tar.gz](https://bioasp.github.io/iggy/downloads/demo_data.tar.gz)