Last data update: 2014.03.03

R: KCsmart Comparative calculate the signficant regions
getSigRegionsCompKCR Documentation

KCsmart Comparative calculate the signficant regions

Description

Extract the significant regions from a compKC object for a given false discovery rate (FDR).

Usage

getSigRegionsCompKC(compKc, fdr=.01, maxRegionGap=10)

Arguments

compKc

A compKc object as created by the 'compareSpmCollection' function

fdr

The false discovery rate to be used to calculate the significantly different regions from the compKc object

maxRegionGap

The maximum number of sample points that is allowed to fall under the threshold in a continuous significant region

Details

The false discovery rate that is set is used to determine the significant regions. When the compKc object was created by the siggenes method the corresponding cutoff is looked up in the siggenes results table, otherwise it is calculated from the permuted data. The maxRegionGap determines how many sample points can be under this threshold in a continuous significant region.

Value

Returns a compKcSigRegions object that contains the significant regions for the given FDR in the 'regionTable' slot. The method used to determine the cutoff, the fdr and the cutoff itself are stored in their corresponding slots. Use 'plot' to visualize the results.

Author(s)

Jorma de Ronde

See Also

compareSpmCollection, getSigRegionsCompKC

Examples

data(hsSampleData)
data(hsMirrorLocs)

spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)

plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)

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 'contributors()' for more information and
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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/getSigRegionsCompKC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: getSigRegionsCompKC
> ### Title: KCsmart Comparative calculate the signficant regions
> ### Aliases: getSigRegionsCompKC
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(hsSampleData)
> data(hsMirrorLocs)
> 
> spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
[1] "Mirror locations looking fine"
Processing sample 1 / 20  Processing sample 2 / 20  Processing sample 3 / 20  Processing sample 4 / 20  Processing sample 5 / 20  Processing sample 6 / 20  Processing sample 7 / 20  Processing sample 8 / 20  Processing sample 9 / 20  Processing sample 10 / 20  Processing sample 11 / 20  Processing sample 12 / 20  Processing sample 13 / 20  Processing sample 14 / 20  Processing sample 15 / 20  Processing sample 16 / 20  Processing sample 17 / 20  Processing sample 18 / 20  Processing sample 19 / 20  Processing sample 20 / 20  
> spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
Warning messages:
1: There are 3294 genes with at least one missing expression value.
The NAs are replaced by the gene-wise mean. 
2: 3294 of the 3294 genes with at least one NA have no and 0 have one non-missing expression value.
All these 3294 genes are removed, and their d-values are set to NA. 
> spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)
Warning message:
In findNumber(object, fdr, delta = delta, isSAM = isSAM, prec = prec,  :
  Since the FDR does not always decrease with increasing delta
the results of findDelta should be considered with caution.
> 
> plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>