Plot multiple significant regions in one figure

Description

Plots significant regions in different scale spaces in one figure

Usage

plotScaleSpace(spms, sigLevels, chromosomes=NULL, type='b')

Arguments

Details

Takes sample point matrices that were calculated using (different) kernel widths (sigma), then calculates the significant regions given the cutoffs as defined by 'sigLevels' and plots these in one figure.

Value

Depending on the 'type' parameter, produces one or two plots, one for the gains and one for the losses. The heatmap color indicates the level of the gain or loss.

Author(s)

Jorma de Ronde

See Also

plot

Examples

data(hsSampleData)
data(hsMirrorLocs)

spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)
spm4mb <- calcSpm(hsSampleData, hsMirrorLocs, sigma=4000000)

siglevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)
siglevel4mb <- findSigLevelTrad(hsSampleData, spm4mb, n=3)

plotScaleSpace(list(spm1mb, spm4mb), list(siglevel1mb, siglevel4mb), type='g')

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 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/plotScaleSpace.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotScaleSpace
> ### Title: Plot multiple significant regions in one figure
> ### Aliases: plotScaleSpace
> ### Keywords: hplot
> 
> ### ** 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"
> spm4mb <- calcSpm(hsSampleData, hsMirrorLocs, sigma=4000000)
[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"

> siglevel4mb <- findSigLevelTrad(hsSampleData, spm4mb, n=3)
[1] "Calculating alpha =  0.05 significance cut-off"
[1] "Found  169  pos peaks and  174  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"

> 
> plotScaleSpace(list(spm1mb, spm4mb), list(siglevel1mb, siglevel4mb), type='g')
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>