getReducedData: Reduced matrix of CNV blocks across the whole genome
In gada: Genome Alteration Detection Algorithm (GADA)

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

Obtains the reduced matrix of CNV segments and subject samples from segmentation output. The initail matrix is obtained and reduced for each cromosome in turn. The resulting matrix is a concatenation of the reduced matrices for each chromosome.

1	getReducedData(x, gg, varSimil = 0.99, subVariation = 0.99)

`x`	concatenated list containing segmentation outcome for each subject, chromosome and population.
`gg`	genetic info: `data.frame` with probe names, chromosome number and genomic positions of each CNV.
`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.

For x, concatenation by subject and chromosome, within a single population, is readily provided by the function summary.parGADA on an object parGADA that has been previously segemnted. Population concatenation is the result of c(summary{parGADA1}), summary{parGADA2}), ... across groups. Concatenation can be obtained with the function sortPopData(segments,nchr,npop), where segments is the output of GADA, nchr the number of chromosomes analyzed and npop the number of populations in the data set.

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

Alejandro Caceres

reduceMatrix, getChromosomeDat

## Not run: 

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

load("Hapmap270Cnv.RData")

mat.f<-getReducedData(segments, gen.info, varSimil=0.99, subVariation=0.90)
cnv.blocks<-attr(mat.f,"cnv.blocks")
matrixPlot(mat.f)

#if segments are available form gada output first sort data by populations then get reduced data.
#segments<-sortPopData(segments,nchr,npop)
#mat.f<-getReducedData(segments, gen.info, varSimil=0.99, subVariation=0.90)


## End(Not run)