Learning Boolean Logic Models of Protein Signaling Networks powered by BioASP and CellNOptR
Project description
caspo combines BioASP and CellNOpt to provide an easy to use software for learning Boolean Logic Models of Protein Signaling Networks from a Prior Knowledge Network in .sif format and a phospho-proteomics dataset in MIDAS format.
Installation
You can install caspo by running:
$ pip install caspo
Note that you may need root access for this. Otherwise, you can use a virtualenv. Then, before using caspo make sure that R is already installed.
Usage
Typical usage is:
$ caspo.py pkn.sif midas.csv
For more options you can ask for help as follows:
$ caspo.py --help Usage: caspo.py [options] pkn.sif midas.csv Options: -h, --help show this help message and exit -t T, --tolerance=T Suboptimal enumeration tolerance: 0 <= t <= 0.5 (Default to 0) -p P, --discrete=P Discretization range exponent: 10^P (Default to 2) -q Q, --alpha=Q Size penalty exponent 1/10^Q (Default to 5) -g, --gtts Compute Global Truth Tables (Default to False). This could take some time for many models. -o O, --outdir=O Output directory path (Default to current directory)
Samples
- Sample files are available here:
Output
- By default, the output of caspo will be 4 comma-separated-values files:
models.csv: Matrix representation of logic models
frequencies.csv: Frequencies of hyperedges occurrence
exclusives.csv: Mutual exclusives hyperedges with their corresponding frequencies
inclusives.csv: Mutual inclusives hyperedges with their corresponding frequencies
- When using the -g option, caspo will also output:
gtt_stats.csv: Basic cluster analysis.
gtt-%i.csv: Explicit computation of each Global Truth Table
1.1 (2012-12-20)
Removes CellNOpt installation relying on cellnopt.wrapper
1.0 (2012-12-03)
Initial release
Project details
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