This function has not been properly implemented yet

Description

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

Usage

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findSigLevelFdr(data, observedSpm, n = 1, fdrTarget=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

fdrTarget

Target False Discovery Rate (FDR)

maxmem

This parameter controls memory usage, set to lower value to lower memory consumption

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. The FDR method is less conservatie than the p-value based approach since instead of controlling the family wise error rate (FWER, P(false positive > 1)) it controls the false discovery rate (FDR) (false positives / total number of called data points).

Value

A list with the cutoffs corresponding to the given FDR

pos

The cutoff for the gains

neg

The cutoff for the losses'

Author(s)

Jorma de Ronde

See Also

plotScaleSpace

Examples

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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')