geneRankStability.BigBang: Computes the rank history for top-ranked genes

In galgo: Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description

Computes the rank history for top-ranked genes.

Usage

## S3 method for class 'BigBang'
geneRankStability(o,
	filter="none",
	subset=TRUE,
	gene.names=TRUE,
	lastSolutionFirst=TRUE,
	...)

Arguments

filter	The BigBang object can save information about solutions that did not reach the goalFitness. filter=="solutions" ensures that only chromosomes that reach the goalFitness are considered. fitlter=="none" take all chromosomes. filter=="nosolutions" consider only no-solutions (for comparative purposes).
subset	Second level of filter. subset can be a vector specifying which filtered chromosomes are used. It can be a logical vector or a numeric vector (indexes in order given by $bestChromosomes in BigBang object variable). If it is a numeric vector length one, a positive value means take those top chromosomes sorted by fitness, a negative value take those at bottom.
gene.names	TRUE for naming the result with the stored $geneNames in oject BigBang. Other character to name-specific.
lastSolutionFirst	Order of the results. TRUE the las solutions is given in the first column.

Value

A matrix which genes are fit in rows and solutions in columns.

Author(s)

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

References

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

See Also

For more information see BigBang.

Examples

   #bb is a BigBang object
   ## Not run: 
   geneRankStability(bb)
   
## End(Not run)
 

galgo documentation built on May 2, 2019, 4:20 a.m.