CopyNeutralIMA provides reference samples for performing copy-number variation (CNV) analysis using Illumina Infinium 450k or EPIC DNA methylation arrays. There is a number of R/Bioconductor packages that do genomic copy number profiling, including conumee [@conumee], ChAMP [@champ] or CopyNumber450k, now deprecated. In order to extract information about the copy number alterations, a set of copy neutral samples is required as a reference. The package CopyNumber450kData, usually used to provide the reference, is no longer available. Additionally, there has never been an effort to provide reference samples for the EPIC arrays. To fill this gap of lacking reference samples, we here introduce the CopyNeutralIMA package.
In this package we provide a set of 51 IlluminaHumanMethylation450k and 13 IlluminaHumanMethylationEPIC samples. The provided samples consist of material from healthy individuals with nominally no copy number aberrations. Users of conumee or other copy number profiling packages may use this data package as reference genomes.
We selected the data from different studies accessible in the Gene Expression Omnibus (GEO). In particular, for 450k arrays samples from GSE49618 [@GSE49618], GSE61441 [@GSE61441] and GSE106089 [@GSE106089] were chosen. For EPIC arrays, normal or control samples from series GSE86831/GSE86833 [@GSE86831], GSE98990 [@GSE98990] and GSE100825 [@GSE100825] were chosen.
First, we load the data we want to analyse and rename it. We will use the examples provided by the minfiData [@minfiData] package and will follow the steps described in the vignette of conumee.
library(minfi) library(conumee) library(minfiData) data(RGsetEx) sampleNames(RGsetEx) <- pData(RGsetEx)$Sample_Name cancer <- pData(RGsetEx)$status == 'cancer' RGsetEx <- RGsetEx[,cancer] RGsetEx
After loading the data we normalize it:
MsetEx <- preprocessIllumina(RGsetEx) MsetEx
Now we load our control samples, from the same array type as our test samples and normalize them:
library(CopyNeutralIMA) ima <- annotation(MsetEx)[['array']] RGsetCtrl <- getCopyNeutralRGSet(ima) # preprocess as with the sample data MsetCtrl <- preprocessIllumina(RGsetCtrl) MsetCtrl
Finally we can run the conumee analysis following the author's indications:
# use the information provided by conumee to create annotation files or define # them according to the package instructions data(exclude_regions) data(detail_regions) anno <- CNV.create_anno(array_type = "450k", exclude_regions = exclude_regions, detail_regions = detail_regions) # load in the data from the reference and samples to be analyzed control.data <- CNV.load(MsetCtrl) ex.data <- CNV.load(MsetEx) cnv <- CNV.fit(ex.data["GroupB_1"], control.data, anno) cnv <- CNV.bin(cnv) cnv <- CNV.detail(cnv) cnv <- CNV.segment(cnv) cnv CNV.genomeplot(cnv) CNV.genomeplot(cnv, chr = 'chr18') head(CNV.write(cnv, what = 'segments')) head(CNV.write(cnv, what='probes'))
nocite: | @minfi ...
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