findSigLevelTrad: Find significance level
In KCsmart: Multi sample aCGH analysis package using kernel convolution

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

Method to find the cutoff at which gains and losses are considered significant using permutations

1	findSigLevelTrad(data, observedSpm, n = 1, p = 0.05, maxmem = 1000)

`data`	aCGH data in the same format as used for 'calcSpm'
`observedSpm`	A sample point matrix as produced by 'calcSpm'
`n`	Number of permutations
`p`	Alpha level for significance
`maxmem`	This parameter controls memory usage, set to lower value to lower memory consumption

The number of permutations needed for reliable results depends on the data and can not be determined beforehand. As a general rule-of-thumb around 100 permutations should be used for 'quick checks' and around 2000 permutations for more rigorous testing.

p is the uncorrected alpha level, the method corrects for multiple testing internally using simple Bonferroni correction. See the referenced publication for more details.

A list with the cutoffs corresponding to the given alpha level

`pos`	The cutoff for the gains
`neg`	The cutoff for the losses'

Jorma de Ronde

plotScaleSpace

data(hsSampleData)
data(hsMirrorLocs)

spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)

sigLevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)

plot(spm1mb, sigLevels=sigLevel1mb)
plotScaleSpace(list(spm1mb), list(sigLevel1mb), type='g')