geneImportanceNetwork.BigBang: Computes the number of times a couple of top-ranked-genes are...

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

Description

Computes the number of times top-ranked-genes are present in models.

Usage

## S3 method for class 'BigBang'
geneImportanceNetwork(o, 
 filter="none", 
 subset=TRUE, 
 mord=min(ncol(o$data$data),50), 
 inc.rank=FALSE, 
 inc.index=FALSE, ...)

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.
mord	The number of “top-ranked-genes” to highlight.
inc.rank	Incluye the gene rank in rownames and colnames.
inc.index	Incluye the gene index in rownames and colnames.

Value

Returns a matrix with number of overlaps for every top-ranked-gene pairs. The order correspond to rank.

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. *distanceImportanceNetwork().

Examples

## Not run: 
   #bb is a BigBang object
   bb
   gin <- geneImportanceNetwork(bb)
   gin
   gin <- geneImportanceNetwork(bbm, mord=5)
   gin
   
## End(Not run)
 

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