getSigRegionsCompKC: KCsmart Comparative calculate the signficant regions
In KCsmart: Multi sample aCGH analysis package using kernel convolution

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

Extract the significant regions from a compKC object for a given false discovery rate (FDR).

1	getSigRegionsCompKC(compKc, fdr=.01, maxRegionGap=10)

`compKc`	A compKc object as created by the 'compareSpmCollection' function
`fdr`	The false discovery rate to be used to calculate the significantly different regions from the compKc object
`maxRegionGap`	The maximum number of sample points that is allowed to fall under the threshold in a continuous significant region

The false discovery rate that is set is used to determine the significant regions. When the compKc object was created by the siggenes method the corresponding cutoff is looked up in the siggenes results table, otherwise it is calculated from the permuted data. The maxRegionGap determines how many sample points can be under this threshold in a continuous significant region.

Returns a compKcSigRegions object that contains the significant regions for the given FDR in the 'regionTable' slot. The method used to determine the cutoff, the fdr and the cutoff itself are stored in their corresponding slots. Use 'plot' to visualize the results.

Jorma de Ronde

compareSpmCollection, getSigRegionsCompKC

data(hsSampleData)
data(hsMirrorLocs)

spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)

plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)