Weighted Exclusivity Test (WExT)

The Weighted Exclusivity Test (WExT) was developed by the Raphael research group at Brown University.

Requirements

Latest tested version in parentheses.

1. Python (2.7.9)

   a. NumPy (1.11.0)

   b. SciPy (0.17.0)

2. gcc (4.9.2)

We recommend using virtualenv to install the Python requirements. After installing virtualenv, you can install the Python requirements for the weighted exclusivity test as follows:

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

See the wiki for additional instructions on Setup and installation.

Setup

The C and Fortran extensions must be compiled before running the weighted exclusivity test:

cd wext
python setup.py build
f2py -c src/fortran/bipartite_edge_swap_module.f95 -m bipartite_edge_swap_module

Usage

Data preprocessing

Before computing the weighted test, you need to process the input mutation data and generated permuted matrices.

1. Process mutation data in MAF format with process_mutations.py. See Process mutations on the wiki for details on usage and input.
2. Generate permuted versions of the mutation data -- fixing the number of mutations per gene and per patient/sample -- and compute mutation probabilities with compute_mutation_probabilities.py. See Compute mutation probabilities on the wiki for details on usage and input.

Searching for exclusive sets

Given the mutation data, we compute the exclusivity of mutations in sets M of genes with find_exclusive_sets.py. Users can choose which test (unweighted, weighted, or permutational) and, for the unweighted and weighted tests, which method (exact or saddlepoint) is used to compute the p-values. See Find exclusive sets on the wiki for details on usage and input.

Searching for exclusive, co-occurring, or other sets

Given the mutation data, we compute the exclusivity, co-occurrence, or other patterns of mutations in sets M of genes with find_sets.py. See Find sets on the wiki for details on usage and input.

Visualization

We provide scripts to run an interactive web application to view the output of compute_exclusivity.py, including both the mutations and mutation probabilities for each set. See viz/README.md and the wiki for additional instructions and details.

Testing

See examples for examples, including a simple example that we recommend using for testing.

Support

Please visit the Dendrix Google Group to post questions and view discussions from other users about our methods for identifying mutually exclusive mutations, or contact us through our research group's website.

Reference

Mark D.M. Leiserson, Matthew A. Reyna, and Benjamin J. Raphael. (2016) A Weighted Exact Test for Mutually Exclusive Mutations in Cancer. ECCB/Bioinformatics 32(17):i736-745. arXiv preprint