CopyNeutralIMA
2024-05-02
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.hg19After 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: 1.50.0
#> Manifest version: 0.4.0Now 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: 1.50.0
#> Manifest version: 0.4.0Finally 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 May 2 10:50:31 2024
#> @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)
CNV.genomeplot(cnv, chr = 'chr18')
head(CNV.write(cnv, what = 'segments'))
#> ID chrom loc.start loc.end num.mark bstat pval seg.mean
#> 1 GroupB_1 chr1 635684 148927230 931 21.475711 1.422312e-99 -0.194
#> 2 GroupB_1 chr1 149077230 149379823 5 26.044755 7.787661e-147 3.058
#> 3 GroupB_1 chr1 149579823 249195311 657 NA NA -0.077
#> 4 GroupB_1 chr10 105000 135462374 840 NA NA -0.054
#> 5 GroupB_1 chr11 130000 134873258 914 NA NA 0.081
#> 6 GroupB_1 chr12 172870 65175000 413 8.802509 1.958883e-16 -0.006
#> seg.median
#> 1 -0.180
#> 2 0.621
#> 3 -0.070
#> 4 -0.050
#> 5 0.068
#> 6 -0.015
head(CNV.write(cnv, what='probes'))
#> Chromosome Start End Feature GroupB_1
#> 1 chr1 15864 15865 cg13869341 -0.064
#> 2 chr1 18826 18827 cg14008030 -0.321
#> 3 chr1 29406 29407 cg12045430 0.109
#> 4 chr1 29424 29425 cg20826792 -0.264
#> 5 chr1 29434 29435 cg00381604 -0.069
#> 6 chr1 68848 68849 cg20253340 -0.360Session info
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] CopyNeutralIMA_1.22.0
#> [2] minfiData_0.50.0
#> [3] conumee_1.38.0
#> [4] IlluminaHumanMethylationEPICmanifest_0.3.0
#> [5] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
#> [6] IlluminaHumanMethylation450kmanifest_0.4.0
#> [7] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
#> [8] minfi_1.50.0
#> [9] bumphunter_1.46.0
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Daniel, K, M Aryee, and W Timp. 2018. minfiData: Example data for the Illumina Methylation 450k array.
Guastafierro, T, MG Bacalini, A Marcoccia, D Gentilini, S Pisoni, AM Di Blasio, A Corsi, et al. 2017. “Genome-wide DNA methylation analysis in blood cells from patients with Werner syndrome.” Clinical Epigenetics 9: 92. https://doi.org/10.1186/s13148-017-0389-4.
Hovestadt, V, and M Zapatka. n.d. conumee: Enhanced copy-number variation analysis using Illumina DNA methylation arrays. Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. http://bioconductor.org/packages/conumee/.
Ley, TJ, C Miller, L Ding, and The Cancer Genome Atlas Research Network. 2013. “Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia.” The New England of Journal Medicine 368 (22): 2059–74. https://doi.org/10.1056/NEJMoa1301689.
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Tian, Y, TJ Morris, AP Webster, Z Yang, S Beck, A Feber, and AE Teschendorff. 2017. “ChAMP: updated methylatioon analysis pipeline for Illumina BeadChips.” Bioinformatics 33 (24): 3982–4. https://doi.org/10.1093/bioinformatics/btx513.
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