reduceMatrix: Reduction of matrix of CNV segments
In gada: Genome Alteration Detection Algorithm (GADA)

Description Usage Arguments Details Value Author(s) See Also Examples

Reduces the columns of matrix of CNV segments by gathering similar neigboring colums and discarding those whose values do not vary much across rows.

1	reduceMatrix(x, gen.info, chr, varSimil = 0.99, subVariation = 0.99, inc = 0)

`x`	Matrix of CNV segements obtanied by getChromosomeDat
`gen.info`	genetic info: genomic positions of each probe.
`chr`	Chromosome number.
`varSimil`	`varSimil=0.99` produces blocks of neighboring CNVs which do differ in more than 99% across the subject sample.
`subVariation`	`subVariation = 0.99` gets rid of CNV blocks that are constant across the the whole subject sample, except for 1% of the subjects.
`inc`	scalar to change the coding of losses, gains or no-changes in CNV calling

Similarity between variables is taken respect the first variable of the block. Therefore, the extreems of all blocks do not differ in more than varSim % of their entries. The value of the block across subjects is that of the first CNV in the block.

`reduceMatrix`	produces a `data.frame` with colums `as.factors` representing each CNV block
`attr(reduceMatrix,"cnv.blocks"`	lists the genetic information of each CNV block

Alejandro Caceres

getChromosomeDat,getReducedData,matrixPlot

## Not run: 

# 'Hapmap270Cnv.RData' file can be downloaded from ...

load("Hapmap270Cnv.RData")

ch<-6
mat<-get.chromosome.dat(segments,ch,gen.info)
mat.f<-reduce.matrix(mat,gen.info,ch,varSimil=0.99,subVariation=0.99)
cnv.blocks<-attr(mat.f,"cnv.blocks")
matrixPlot(mat.f)

## End(Not run)