GABi: Genetic Algorithm for Generalized Biclustering
In edcurry/GABi: Framework for Generalized Subspace Pattern Mining

Description Usage Arguments Details Value Author(s) Examples

A flexible framework for finding submatrices that are good manifestations of a user-specified pattern from within a numeric (often binary) matrix. The user-defined pattern is specified via feature selection and bicluster desirability evaluation functions (see details).

GABi(x,nSols=0,convergenceGens=40,popsize=256,mfreq=1,xfreq=0.5,
maxNgens=200,keepBest=FALSE,identityThreshold=0.75,
nsubpops=4,experiod=10,diffThreshold=0.9,verbose=FALSE,maxLoop=1,
fitnessArgs=list(consistency=0.8,featureWeights = rowMeans(x, na.rm = TRUE)),
fitnessFun=getFitnesses.entropy,featureSelFun=featureSelection.basic)

`x`	Numeric data input array used to generate binary output array. Each row of the array represents a different variable.
`nSols`	Number of solutions at which to terminate loop.
`convergenceGens`	Number of generations after which to terminate the GA process within each loop if no improvement to the best solution's fitness is seen.
`popsize`	Total number of solutions to be evolved in GA (divided across `nsubpops` subpopulations.)
`mfreq`	Mutation frequency: probability of flipping each bit in each GA solution is `mfreq/ncol(x)`.
`xfreq`	Crossover frequency: probability of each pair of solutions having the crossover operator being applied.
`maxNgens`	Maximum number of generations in GA process within each loop.
`keepBest`	Boolean specifying whether or not to pass the best solution from each generation unchanged into the next.
`identityThreshold`	Numeric value specifying the proportion of shared columns from `x` above which two biclusters are considered redundant and only the best is output as a solution.
`nsubpops`	Numeric value specifying the number of distinct subpopulations across which to distribute the GAs population of solutions. For more details on the Island Model of GAs, see Whitley 1995. If `nsubpops=1`, a traditional (non-Island) GA will be implemented.
`experiod`	Number of generations after which to exchange solutions between the distinct GA subpopulations. If `experiod` is greater than `maxNgens`, the subpopulations will be kept completely distinct.
`diffThreshold`	Numeric value specifying minimum proportion of values in each row of `x` to be greater than the minimum or less than the maximum value. Included primarily as a filter to remove invariant rows from binary datasets `x`.
`verbose`	Boolean indicating whether or not to print diagnostic messages to R console.
`maxLoop`	Numeric value specifying maximum number of runs of the GA, after which GABi will terminate and return all recovered solutions, even if `nSols` isn't reached.
`fitnessArgs`	List containing arguments to be used in `fitnessFun` and `featureSelFun`.
`fitnessFun`	Function taking argument `chr`, a numeric vector specifying the solution to be evaluated. All other arguments to be used in the function should be specified in `fitnessArgs`. Must return a single numeric value indicating the relative fitness (i.e. 'goodness') of the solution.
`featureSelFun`	Function taking argument `chr`, a numeric vector specifying the solution to be evaluated. All other arguments to be used in the function should be specified in `fitnessArgs`. Must return a numeric vector indicating which features (i.e. rows of `x`) to be included in the bicluster.

GABi uses flexible user-defined (or preset) functions to perform generalized biclustering of a numeric or binary data matrix x. It implements a number of features, including an Island Model of population evolution (in which a number of distinct subpopulations are kept isolated for the purposes of selection and crossover) and an iterative loop of solution generation (in which the GA process is rerun with a 'tabu' list, ensuring that previously returned solutions are not selected for in subsequent runs of the GA). Given an appropriate fitness function fitnessFun and feature selection function featureSelFun, which take a binary chromosome (in which a 1 denotes that the corresponding column of x is included in the bicluster) and return a desirability score and a list of the features fitting the bicluster pattern across the specified columns, respectively.

List of biclusters. Each bicluster represents a submatrix satisfying the conditions of the specified pattern, and contains the elements:

`features`	Which rows of the input array `x` are in this bicluster
`samples`	Which columns of the input array `x` are in this bicluster
`score`	Fitness evaluation of this bicluster (can be used to compare the different biclusters output by the algorithm)

Ed Curry e.curry@imperial.ac.uk

## create a binary array
x <- array(round(runif(1200)),dim=c(100,12))
## Not run: x

## use GABi to find biclusters
x.bc <- GABi(x,maxNgens=20)
## Not run: x.bc