caspo 1.3

Installation

You can install caspo by running:

$ pip install caspo

Note that you may need root (sudo) access for this. Otherwise, you can use a virtualenv. Before using caspo make sure that R is already installed. The first time you run caspo, CellNOptR will be downloaded and installed in your R environment.

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 [-h] [--version] [--fit F] [--size S] [--discrete D] [--gtts]
                [--cross N K] [--out O]
                pkn midas

positional arguments:
  pkn           Prior knowledge network in SIF format
  midas         Experimental dataset in MIDAS file

optional arguments:
  -h, --help    show this help message and exit
  --version     show program's version number and exit
  --fit F       suboptimal enumeration tolerance (Default to 0)
  --size S      suboptimal size enumeration tolerance (Default to 0). Combined
                with --fit could lead to a huge number of models
  --discrete D  discretization over [0,D] (Default to 100)
  --gtts        compute Global Truth Tables (Default to False). This could
                take some time for many models.
  --cross N K   compute N random K-fold cross validation
  --out O       output directory path (Default to current directory)

Samples

Sample files are available for the prior knowledge network and the phospho-proteomics dataset

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

• exclusive.csv: Mutual exclusives hyperedges with their corresponding frequencies

• inclusive.csv: Mutual inclusives hyperedges with their corresponding frequencies

When using the –gtts option, caspo will also output:

• gtts_stats.csv: Basic cluster analysis.

• gtt-%i.csv: Explicit computation of each Global Truth Table

When using the –cross N K option, caspo will also output:

• cross_validation_%i.csv: K-fold cross validation report for each of the N runs