iggy
Tools for consistency based analysis of influence graphs and observed systems behavior
Tools for consistency based analysis of influence graphs and observed systems behavior
iggy
+ opt_graph
iggy
and opt_graph
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.
opt_graph
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.
precompiled binaries for 64bit linux and macOS can be found on the release page
sample data demo_data.tar.gz
Clone the git repository:
git clone https://github.com/bioasp/iggy.git
cargo build --release
The executables can be found under ./target/release/
Typical usage is:
> iggy -n network.cif -o observation.obs -l 10 -p
For more options you can ask for help as follows:
> iggy -h
iggy 2.1.0
Sven Thiele <sthiele78@gmail.com>
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 [FLAGS] [OPTIONS] --network <network-file>
FLAGS:
-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 Prints help information
--mics Compute minimal inconsistent cores
--scenfit Compute scenfit of the data, default is mcos
-p, --show-predictions Show predictions
-V, --version Prints version information
OPTIONS:
-l, --show-labelings <max-labelings> Show max-labelings labelings, default is OFF, 0=all
-n, --network <network-file> Influence graph in CIF format
-o, --observations <observations-file> Observations in bioquali format
Typical usage is:
> opt_graph -n network.cif -o observations_dir/ --show-repairs 10
For more options you can ask for help as follows:
> opt_graph -h
opt_graph 2.1.0
Sven Thiele <sthiele78@gmail.com>
Opt-graph 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:
opt_graph [FLAGS] [OPTIONS] --network <network-file> --observations <observations-dir>
FLAGS:
-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 Prints help information
-V, --version Prints version information
OPTIONS:
-r, --show-repairs <max-repairs> Show max-repairs repairs, default is OFF, 0=all
-n, --network <network-file> Influence graph in CIF format
-o, --observations <observations-dir> Directory of observations in bioquali format
-m, --repair-mode <repair-mode> Repair mode: remove = remove edges (default),
optgraph = add + remove edges,
flip = flip direction of edges
Designing optimal experiments to discriminate interaction graph models, IEEE/ACM Trans. Comput. Biol. Bioinform, 16(3), 2019.
Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies, BMC Bioinformatics, 2015.
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.
Directed random walks and constraint programming reveal active pathways in HGF signaling, FEBS Journal, 2015.