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).
1 2 3 4 5 | 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 |
mfreq |
Mutation frequency: probability of flipping each bit in each GA solution is |
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 |
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 |
experiod |
Number of generations after which to exchange solutions between the distinct GA subpopulations. If |
diffThreshold |
Numeric value specifying minimum proportion of values in each row of |
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 |
fitnessArgs |
List containing arguments to be used in |
fitnessFun |
Function taking argument |
featureSelFun |
Function taking argument |
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 |
samples |
Which columns of the input array |
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
1 2 3 4 5 6 7 |
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