KCsmart (version 2.30.0)

findSigLevelTrad: Find significance level

Description

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

Usage

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

Arguments

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

Value

A list with the cutoffs corresponding to the given alpha level

pos

The cutoff for the gains

neg

The cutoff for the losses'

Details

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.

See Also

plotScaleSpace

Examples

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')