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