Last data update: 2014.03.03
R: Find significance level
findSigLevelTrad R Documentation
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
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.
Value
A list with the cutoffs corresponding to the given alpha level
pos
The cutoff for the gains
neg
The cutoff for the losses'
Author(s)
Jorma de Ronde
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')
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(KCsmart)
Loading required package: siggenes
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: multtest
Loading required package: splines
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/KCsmart/findSigLevelTrad.Rd_%03d_medium.png", width=480, height=480)
> ### Name: findSigLevelTrad
> ### Title: Find significance level
> ### Aliases: findSigLevelTrad
> ### Keywords: manip
>
> ### ** Examples
>
> data(hsSampleData)
> data(hsMirrorLocs)
>
> spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)
[1] "Mirror locations looking fine"
[1] "Splitting data .."
[1] "Summing data .."
[1] "Mirroring data .."
[1] "Calculating sample point matrix .."
Processing chromosome 1
Processing chromosome 10
Processing chromosome 11
Processing chromosome 12
Processing chromosome 13
Processing chromosome 14
Processing chromosome 15
Processing chromosome 16
Processing chromosome 17
Processing chromosome 18
Processing chromosome 19
Processing chromosome 2
Processing chromosome 20
Processing chromosome 21
Processing chromosome 22
Processing chromosome 3
Processing chromosome 4
Processing chromosome 5
Processing chromosome 6
Processing chromosome 7
Processing chromosome 8
Processing chromosome 9
Processing chromosome X
Processing chromosome Y
[1] "Done"
>
> sigLevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)
[1] "Calculating alpha = 0.05 significance cut-off"
[1] "Found 584 pos peaks and 598 neg peaks in observed sample point matrix"
[1] "Calculating Mirror Positions"
[1] "Starting permutations .."
At iteration 1 of 3[1] "Permuting"
[1] "Combining"
[1] "Returning"
At iteration 2 of 3[1] "Permuting"
[1] "Combining"
[1] "Returning"
At iteration 3 of 3[1] "Permuting"
[1] "Combining"
[1] "Returning"
>
> plot(spm1mb, sigLevels=sigLevel1mb)
> plotScaleSpace(list(spm1mb), list(sigLevel1mb), type='g')
>
>
>
>
>
> dev.off()
null device
1
>