1 Overview

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 (Hovestadt and Zapatka, n.d.), ChAMP (Tian et al. 2017) 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.

2 Description

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.

3 Data

We selected the data from different studies accessible in the Gene Expression Omnibus (GEO). In particular, for 450k arrays samples from GSE49618 (Ley et al. 2013), GSE61441 (Wei et al. 2015) and GSE106089 (Tomlinson et al. 2017) were chosen. For EPIC arrays, normal or control samples from series GSE86831/GSE86833 (Pidsley et al. 2016), GSE98990 (Zhou, Laird, and Shen 2017) and GSE100825 (Guastafierro et al. 2017) were chosen.

4 Example with conumee

First, we load the data we want to analyse and rename it. We will use the examples provided by the minfiData (Daniel, Aryee, and Timp 2018) 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
#> class: RGChannelSet 
#> dim: 622399 3 
#> metadata(0):
#> assays(2): Green Red
#> rownames(622399): 10600313 10600322 ... 74810490 74810492
#> rowData names(0):
#> colnames(3): GroupB_3 GroupB_1 GroupB_2
#> colData names(13): Sample_Name Sample_Well ... Basename filenames
#> Annotation
#>   array: IlluminaHumanMethylation450k
#>   annotation: ilmn12.hg19

After loading the data we normalize it:

MsetEx <- preprocessIllumina(RGsetEx)
MsetEx
#> class: MethylSet 
#> dim: 485512 3 
#> metadata(0):
#> assays(2): Meth Unmeth
#> rownames(485512): cg00050873 cg00212031 ... ch.22.47579720R
#>   ch.22.48274842R
#> rowData names(0):
#> colnames(3): GroupB_3 GroupB_1 GroupB_2
#> colData names(13): Sample_Name Sample_Well ... Basename filenames
#> Annotation
#>   array: IlluminaHumanMethylation450k
#>   annotation: ilmn12.hg19
#> Preprocessing
#>   Method: Illumina, bg.correct = TRUE, normalize = controls, reference = 1
#>   minfi version: 1.33.0
#>   Manifest version: 0.4.0

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
#> class: MethylSet 
#> dim: 485512 51 
#> metadata(0):
#> assays(2): Meth Unmeth
#> rownames(485512): cg00050873 cg00212031 ... ch.22.47579720R
#>   ch.22.48274842R
#> rowData names(0):
#> colnames(51): GSM1185582 GSM1185583 ... GSM2829413 GSM2829418
#> colData names(7): ID gsm ... source_name_ch1 characteristics_ch1
#> Annotation
#>   array: IlluminaHumanMethylation450k
#>   annotation: ilmn12.hg19
#> Preprocessing
#>   Method: Illumina, bg.correct = TRUE, normalize = controls, reference = 1
#>   minfi version: 1.33.0
#>   Manifest version: 0.4.0

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)
#> using genome annotations from UCSC
#> getting 450k annotations
#>  - 470870 probes used
#> importing regions to exclude from analysis
#> importing regions for detailed analysis
#> creating bins
#>  - 53891 bins created
#> merging bins
#>  - 15820 bins remaining

# 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 analysis object
#>    created   : Thu Oct 31 12:27:29 2019
#>   @name      : GroupB_1
#>   @anno      : 22 chromosomes, 470870 probes, 15820 bins
#>   @fit       : available (noise: 2.32)
#>   @bin       : available (shift: 0.005)
#>   @detail    : available (20 regions)
#>   @seg       : available (29 segments)

CNV.genomeplot(cnv)