ingranalyze
Influence graph analysis, consistency check, diagnosis, repair and prediction
Influence graph analysis, consistency check, diagnosis, repair and prediction
For many biological systems knowledge bases are available that describe the interaction of its components usually in terms of causal networks and influence graphs. In particular signed influence graphs where edges indicate either positive or negative effect of one node upon another. Building upon a notion of consistency between biochemical/genetic regulations and high-throughput profiles of cell activity. We present an approach to check the consistency of large-scale data sets, provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions. Further, we address 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.
You can install ingranalyze by running:
$ pip install --user ingranalyze
On Linux the executable script can then be found in ~/.local/bin
and on Mac OS the script is under /Users/YOURUSERNAME/Library/Python/3.6/bin.
Typical usage is:
$ ingranalyze.py --mics --repair 5 networkfile observationfile
For more options you can ask for help as follows:
$ ingranalyze.py -h
usage: ingranalyze.py [-h] [--mics] [--repair {1,2,3,4,5}] [--list_repairs] networkfile observationfile
positional arguments:
networkfile influence graph in bioquali format
observationfile observations in bioquali format
optional arguments:
-h, --help show this help message and exit
--mics compute minimal inconsistent cores
--repair {1,2,3,4,5} choose repair method: 1 flip observed variations, 2
flip influences, 3 define network nodes as inputs, 4
define network nodes as input in an experiment (use
only in case of multiple experiments), 5 add
influences. default is 3
--list_repairs compute all minimal repair sets
Sample files for yeast are available here:: yeast_guelzim.net yeast_snf2.obs
Detecting Inconsistencies in Large Biological Networks with Answer Set Programming. (2011). Theory and Practice of Logic Programming. DOI
Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming. (2010). 12th International Conference on the Principles of Knowledge Representation and Reasoning.DOI
Q: I don’t have pip. How can I install pip without admin rights?
A: You can install pip without admin rights.
Download getpip.py.
$ wget https://raw.github.com/pypa/pip/master/contrib/get-pip.py
Install pip locally.
$ python get-pip.py --user
You can install using your local pip.
Q: I don’t have pip. How can I install ingranalyze without pip?
A: You can install ingranalyze without pip if you take care of the dependencies yourself.
Download pyasp-1.3.3.
$ wget https://pypi.python.org/packages/source/p/pyasp/pyasp-1.3.3.tar.gz
Extract and install pyasp.
$ gzip -d pyasp-1.3.3.tar.gz
$ tar -xvf pyasp-1.3.3.tar
$ cd pyasp-1.3.3
$ python setup.py install --user
Download ingranalyze-1.5.3.
$ wget https://pypi.python.org/packages/source/i/ingranalyze/ingranalyze-1.5.3.tar.gz
Extract and install ingranalyze.
$ gzip -d ingranalyze-1.5.3.tar.gz
$ tar -xvf ingranalyze-1.5.3.tar
$ cd ingranalyze-1.5.3
$ python setup.py install --user
The executable script can then be found in ~/.local/bin on Linux and in /Users/YOURUSERNAME/Library/Python/2.7/binon Mac OS.