caspo 1.1

Learning Boolean Logic Models of Protein Signaling Networks powered by BioASP and CellNOptR

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:

ExtLiverPCB.sif ExtLiverPCB.csv

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