Learning of Protein Signaling Logic Models powered by BioASP and CellNOptR
Project description
caspo combines BioASP and CellNOpt to provide an easy to use software for learning Protein Signaling Logic Models 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
caspo will try to install the R package CellNOpt (R must be already installed). Note that you may need root access for this. Otherwise, you can install CellNOpt manually from the R console and use a virtualenv to install caspo.
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 (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)
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
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