compareSpmCollection: KCsmart Comparative calculate null distribution
In KCsmart: Multi sample aCGH analysis package using kernel convolution

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

Compare the samples of one class in the sample point matrix collection to the samples in the other class and calculate the null distribution

1	compareSpmCollection(spmCollection, nperms=20, method=c("siggenes", "perm"), siggenes.args=NULL, altcl=NULL)

`spmCollection`	An spmCollection object as created by the 'calcSpmCollection' function
`nperms`	The number of permutations to be used to calculate the null distribution
`altcl`	Instead of using the class vector from the spmCollection object an alternative vector can be used
`method`	The method to be used to calculate the null distribution
`siggenes.args`	Optional additional arguments to the siggenes function

The method to be used to determine significant regions can either be the SAM methodology from the siggenes package or a signal-to-noise/permutation based method. For more information regarding the siggenes method please check the corresponding package.

Returns a compKc object which returns the original data and, depending on the method used, the permuted data or the fdr-delta value combinations as calculated by the siggenes package.

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)