ExDesi
Designing optimal experiments to discriminate interaction graph models
Designing optimal experiments to discriminate interaction graph models
Modern methods for the inference of cellular networks from experimental data often express nondeterminism by proposing an ensemble of candidate models with similar properties. To further discriminate among these model candidates, and to find the biological truth, new experiments need to be carried out. The goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. Building upon a notion of consistency between biochemical/genetic regulations and high-throughput profiles of cell activity, ExDesi implements an approach for experiment planning with interaction graph models and sign consistency methods. ExDesi can be used in combination with methods for network inference and consistency checking to compute experiments which are most suitable to deliver results that allow a refinement of the model.
You can install exdesi by running:
$ pip install --user exdesi
On Linux the executable scripts can then be found in ~/.local/bin
and on Mac OS the scripts are under /Users/YOURUSERNAME/Library/Python/3.5/bin.
Typical usage is:
$ exdesi.py candidate_directory experimental_variables_file
For more options you can ask for help as follows:
$ exdesi.py -h
usage: exdesi.py [-h] [--best_set BEST_SET] [-x EXCLUDE]
networkfiles experivarfile
positional arguments:
networkfiles directory of influence graphs in SIF format
experivarfile experimental variables
optional arguments:
-h, --help show this help message and exit
--best_set BEST_SET compute best set of experiments maximal number of
experiments, default is OFF, 0=unlimited
-x EXCLUDE, --exclude EXCLUDE
exclude experiments described in file EXCLUDE
Sample files available in the git repository.
A sample call would be:
$ exdesi.py in_silico_HEK293/candidates_round1/ in_silico_HEK293/expvars.txt -x in_silico_HEK293/exclude1.txt
Designing optimal experiments to discriminate interaction graph models, IEEE/ACM Trans. Comput. Biol. Bioinform, preprint.
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.
Detecting Inconsistencies in Large Biological Networks with Answer Set Programming, Theory and Practice of Logic Programming, 2011.
Q: I don’t have pip. How can I install pip without admin rights?
A: To 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: How can I write the output of exdesi into a file?
A: You can redirect the output of exdesi into a file using >. For example to write the results into the file myfile.txt type:
$ exdesi.py network.sif observation.obs --show_labelings 10 --show_predictions > myfile.txt