A compKc object as created by the 'compareSpmCollection' function
The false discovery rate to be used to calculate the significantly different regions from the compKc object
The maximum number of sample points that is allowed to fall under the threshold in a continuous significant region
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.
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.
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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,
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
> spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
 "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)
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)
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)
Created & Maintained by Osamu Ogasawara (firstname.lastname@example.org) and