geneImportanceNetwork.BigBang {galgo}R Documentation

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

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=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

   #bb is a BigBang object
   bb
   gin <- geneImportanceNetwork(bb)
   gin
   gin <- geneImportanceNetwork(bbm, mord=5)
   gin
[Package galgo version 1.0-10 Index]