iggy and optgraph are tools for consistency based analysis of influence graphs and observed systems behavior (signed changes between two measured states). For many (biological) systems are knowledge bases available that describe the interaction of its components in terms of causal networks, boolean networks and influence graphs where edges indicate either positive or negative effect of one node upon another.
iggy implements methods to check the consistency of large-scale data sets and provides explanations for inconsistencies. In practice, this is used to identify unreliable data or to indicate missing reactions. Further, iggy addresses the problem of repairing networks and corresponding yet often discrepant measurements in order to re-establish their mutual consistency and predict unobserved variations even under inconsistency.
optgraph confronts interaction graph models with observed systems behavior from multiple experiments. opt_graph computes networks fitting the observation data by removing (or adding) a minimal number of edges in the given network.
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Precompiled binaries for 64bit linux and macOS can be found on the release page
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Sample data demo_data.tar.gz
Clone the git repository:
git clone https://github.com/bioasp/iggy.git
cargo build --releaseThe executables can be found under ./target/release/
Typical usage is:
iggy -n network.cif -o observation.obs -l 10 -pFor more options you can ask for help as follows:
> iggy -h
iggy 2.2.1-dev
Sven Thiele <[email protected]>
Iggy confronts interaction graph models with observations of (signed) changes between two measured
states (including uncertain observations). Iggy discovers inconsistencies in networks or data,
applies minimal repairs, and predicts the behavior for the unmeasured species. It distinguishes
strong predictions (e.g. increase in a node) and weak predictions (e.g., the value of a node
increases or remains unchanged)
USAGE:
iggy [OPTIONS] --network <FILE>
OPTIONS:
-a, --auto-inputs Declare nodes with indegree 0 as inputs
--depmat Combine multiple states, a change must be explained by an
elementary path from an input
--elempath Every change must be explained by an elementary path from an
input
--founded-constraints-off Disable foundedness constraints
--fwd-propagation-off Disable forward propagation constraints
-h, --help Print help information
--json Print JSON output
-l, --show-labelings <N> Show N labelings, default is OFF, 0=all
--mics Compute minimal inconsistent cores
-n, --network <FILE> Influence graph in CIF format
-o, --observations <FILE> Observations in bioquali format
-p, --show-predictions Show predictions
--scenfit Compute scenfit of the data, default is mcos
-V, --version Print version information
Typical usage is:
optgraph -n network.cif -o observations_dir/ --show-repairs 10For more options you can ask for help as follows:
> optgraph -h
optgraph 2.2.1-dev
Sven Thiele <[email protected]>
Optgraph confronts interaction graph models with observations of (signed) changes between two
measured states. Opt-graph computes networks fitting the observation data by removing (or adding) a
minimal number of edges in the given network
USAGE:
optgraph [OPTIONS] --network <FILE> --observations <DIR>
OPTIONS:
-a, --auto-inputs Declare nodes with indegree 0 as inputs
--depmat Combine multiple states, a change must be explained by an
elementary path from an input
--elempath Every change must be explained by an elementary path from an
input
--founded-constraints-off Disable foundedness constraints
--fwd-propagation-off Disable forward propagation constraints
-h, --help Print help information
--json Print JSON output
-m, --repair-mode <REPAIR_MODE> REPAIR_MODE: remove = remove edges (default), optgraph = add
+ remove edges, flip = flip direction of edges
-n, --network <FILE> Influence graph in CIF format
-o, --observations <DIR> Directory of observations in bioquali format
-r, --show-repairs <N> Show N repairs, default is OFF, 0=all
-V, --version Print version information-
Designing optimal experiments to discriminate interaction graph models, IEEE/ACM Trans. Comput. Biol. Bioinform, 16(3), 2019.
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Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies, BMC Bioinformatics, 2015.
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Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming, 12th International Conference on the Principles of Knowledge Representation and Reasoning, 2010.
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Directed random walks and constraint programming reveal active pathways in HGF signaling, FEBS Journal, 2015.
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