Skip to main content

A tool for consistency based analysis of influence graphs and observed systems behavior.

Project 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](https://bioasp.github.io/iggy/downloads/demo_data.tar.gz)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for iggy, version 1.4.3
Filename, size File type Python version Upload date Hashes
Filename, size iggy-1.4.3.tar.gz (30.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page