Weighted Exclusivity Test (WExT)
The Weighted Exclusivity Test (WExT) was developed by the Raphael research group at Brown University.
Latest tested version in parentheses.
a. NumPy (1.11.0)
b. SciPy (0.17.0)
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.
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
Before computing the weighted test, you need to process the input mutation data and generated permuted matrices.
- Process mutation data in MAF format with
process_mutations.py. See Process mutations on the wiki for details on usage and input.
- 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.
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.
See examples for examples, including a simple example that we recommend using for testing.
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.