A set of scripts that use patterns of recurrence and mutual exclusivity 
to identify functional modules in tumors. The tool will build a weighted 
graph using the Winnow algorithm, then search it for modules that are
highly recurrent (across samples) and have high levels of mutual 
exclusivity. The significance of these patterns is determined using 
the algorithmic significance test

For a complete description, see this publication:
Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors. Christopher A. Miller, Stephen H. Settle, Erik P. Sulman, Kenneth D Aldape, and Aleksandar Milosavljevic. BMC Medical Genomics. 2011, 4:34 doi:10.1186/1755-8794-4-34 (http://www.biomedcentral.com/1755-8794/4/34)

Requirements: Bash interpreter, Ruby >1.8 
Author: Chris Miller (chrisamiller@gmail.com)
Version: 1.0
License: MIT (see LICENSE)

Input:
  - A binary matrix detailing which genes are aberrant in each sample. 
    The binary matrix should be constructed such that the first row and 
    column contain labels, and every other value is either a one or a zero.

Output:
  - A list of the most highly significant modules (that exceed the specified
    significance threshold)


Usage:
  - execute run.sh with no arguments for basic usage info


Parameters:

--maxModSize   The largest module size that the algorithm will attempt to 
               find. Warning! The algorithm's complexity grows very quickly 
               as a result of using combinatorial search. Values over 5,
               applied to very large matrices, may take a long time (days).

--infile       A tab-delimited file containing a binary matrix, structured 
               as follows: 
                - a header row containing sample ids
                - a header column containing gene names
                - each position in the matrix should contain either 1 or 0,
                  with a 1 specifying that a particular gene is aberrant 
                  in a specific sample.

--genes        An integer representing the total number of genes assated. 
               This allows for multiple testing correction. Include all 
               genes assayed, even if they are not represented the matrix 
               (there is little reason to include genes in the matrix that 
               have no mutations in any sample).

--outFile1     Output file 1 - a complete list of all potential modules

--outFile2     Output file 2 - the largest and most significant 
               non-overlappingmodules 

--threshold    Winnow threshold - the algorithm speeds up the search 
               process by excluding poor edges. This parameter controls
               the threhold score for an edge to be kept.  Due to Winnow's 
               design, these values should be powers of 2. (4, 8. 16, 32 ...)
               Default value: 4

               The optimal value is dependent on size of the input data. 
               Filtering too agressively may lead to missing modules. Some 
               suggested values:
                 ~1000  attributes, ~200 samples:  4
                 ~5000  attributes, ~500 samples:  32
                 ~18000 attributes, ~500 samples:  128


--minFreq      Genes must be altered in this proportion of the samples to 
               be considered for inclusion in a module. Recommended default
               is 0.10, as the false positive rate increases below that 
               point.The minimum frequency of alteration required for a gene 
               to be included in the search for modules. Default value: 0.10
               
--bgRate       Background Rate - the expected odds of a particular attribute
               in a particular sample being altered, assuming no selective 
               pressure. The default value assumes data composed of copy 
               number and somatic mutation assays, and is derived from HapMap
               data and estimation of passenger mutation rates in glioblastoma 
               multiforme.  Default value: 0.01037848

--sigThresh    Significance Threshold - the minimum algorithmic significance 
               value that a module must exceed. Optimal values will depend 
               on input data size. Suggested values:
                 ~1000  genes, ~200 samples: 100
                 ~5000  genes, ~500 samples: 200
                 ~18000 genes, ~500 samples: 300

Example:

  The exampleFiles folder contains a contains a binary matrix of simulated data. 
  The first 1290 rows are simulated genes with a mutation distribution based on 
  those found in a large glioblastoma tumor set. The last 3 are generated such 
  that mutations follow an RME pattern.

  To run the example:

    cd exampleFiles/
    bash ../run.sh -s 3 -i input.dat -g 1290 -o potentialModules -p topModules -t 200


---------

RMEmod was developed at Baylor College of Medicine, in the Bionformatics Research Laboratory (http://www.genboree.org/site/bioinformatics_research_laboratory)