This package provides annotations for Illumina methylation EPIC array v2.0. The data is based on the file https://support.illumina.com/content/dam/illumina-support/documents/downloads/productfiles/methylationepic/MethylationEPIC%20v2%20Files.zip from https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit/downloads.html.
When using with the minfi package, you can manually set the “annotation” element by providing the suffix (removing the string “IlluminaHumanMethylationEPICv2anno.”):
RGset = read.metharray.exp(...)
# explained in the IlluminaHumanMethylationEPICv2manifest package
annotation(RGset)["array"] = "IlluminaHumanMethylationEPICv2"
annotation(RGset)["annotation"] = "20a1.hg38"We compare CpG annotation in the package IlluminaHumanMethylationEPICv2anno.20a1.hg38 and the package IlluminaHumanMethylationEPICanno.ilm10b4.hg19.
library(IlluminaHumanMethylationEPICv2anno.20a1.hg38)
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
cgi1 = IlluminaHumanMethylationEPICanno.ilm10b4.hg19::Islands.UCSC
cgi2 = IlluminaHumanMethylationEPICv2anno.20a1.hg38::Islands.UCSCFollowing code shows the number of probes in each CpG feature. Note in IlluminaHumanMethylationEPICanno.ilm10b4.hg19, CGI shores and shelves are additionally classified as “N_Shore”/“S_Shore” and “N_Shelf”/“S_Shelf”, while they are simply “Shore” and “Shelf” in IlluminaHumanMethylationEPICv2anno.20a1.hg38.
t1 = table(gsub("(N|S)_", "", cgi1$Relation_to_Island))
t1##
## Island OpenSea Shelf Shore
## 161441 488181 61691 154546t2 = table(cgi2$Relation_to_Island)
t2##
## Island OpenSea Shelf Shore
## 150679 560737 60836 157823t2 - t1##
## Island OpenSea Shelf Shore
## -10762 72556 -855 3277(t2 - t1)/t1##
## Island OpenSea Shelf Shore
## -0.06666212 0.14862520 -0.01385940 0.02120404We can see there are more new probes in the CpG seas.
In IlluminaHumanMethylationEPICanno.ilm10b4.hg19 and other related EPIC-v1 packages, probe IDs (e.g. in the format of “cg18478105”) are unique in the array. But in the packages related to EPIC-v2, probe IDs may be duplicated. Thus, we use the “illumina_ID” (column “IlmnID” in the manifest file, https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001568) as the ID type for probes in v2-packages. The duplicated probes have the same probe sequence, but locate randomly on the array. The illumina ID is a combination of probe ID and a “duplication ID”.
Let’s take cgi1 and cgi2 as an example:
dim(cgi1)## [1] 865859 2head(cgi1)## DataFrame with 6 rows and 2 columns
## Islands_Name Relation_to_Island
## <character> <character>
## cg18478105 chr20:61846843-61848.. Island
## cg09835024 chrX:24072558-24073135 Island
## cg14361672 chr9:131464843-13146.. N_Shore
## cg01763666 OpenSea
## cg12950382 OpenSea
## cg02115394 chr13:115000148-1150.. Islanddim(cgi2)## [1] 930075 2head(cgi2)## DataFrame with 6 rows and 2 columns
## Islands_Name Relation_to_Island
## <character> <character>
## cg25324105_BC11 chr19:37691892-37692.. Island
## cg25383568_TC11 chr19:38726890-38727.. Island
## cg25455143_BC11 chr19:1591428-159162.. Island
## cg25459778_BC11 chr16:1610053-1615094 Island
## cg25487775_BC11 chr2:161237949-16123.. Shore
## cg25595446_BC11 chr19:49676753-49677.. Islandany(duplicated(rownames(cgi1)))## [1] FALSELet’s check how many probes have duplicated probe IDs. First remove the suffix to only keep the probe IDs.
illumina_ID = rownames(cgi2)
probe_ID = gsub("_.*$", "", illumina_ID)Check the duplication of probe IDs:
tb = table(probe_ID)
table(tb)## tb
## 1 2 3 4 5 6 10
## 918546 4174 942 23 47 3 1We can see in the most extreme case, a probe ID is repeated 10 times in the array. Let’s check what it is:
tb[tb == 10]## cg06373096
## 10illumina_ID[probe_ID == "cg06373096"]## [1] "cg06373096_TC11" "cg06373096_TC12" "cg06373096_TC13" "cg06373096_TC14"
## [5] "cg06373096_TC15" "cg06373096_TC16" "cg06373096_TC17" "cg06373096_TC18"
## [9] "cg06373096_TC19" "cg06373096_TC110"But the locations of these 10 probes are the same:
cgi2[probe_ID == "cg06373096", ]## DataFrame with 10 rows and 2 columns
## Islands_Name Relation_to_Island
## <character> <character>
## cg06373096_TC11 chr19:12880876-12881.. Shore
## cg06373096_TC12 chr19:12880876-12881.. Shore
## cg06373096_TC13 chr19:12880876-12881.. Shore
## cg06373096_TC14 chr19:12880876-12881.. Shore
## cg06373096_TC15 chr19:12880876-12881.. Shore
## cg06373096_TC16 chr19:12880876-12881.. Shore
## cg06373096_TC17 chr19:12880876-12881.. Shore
## cg06373096_TC18 chr19:12880876-12881.. Shore
## cg06373096_TC19 chr19:12880876-12881.. Shore
## cg06373096_TC110 chr19:12880876-12881.. ShoreIf you think illumina IDs as “probe IDs” as in the v1 packages, you can basically do all the same analyses without worrying aboutn the format of the probe IDs. But if you really need the original probe IDs, you can use the aggregate_to_probes() function. It works for both annotation data frames in IlluminaHumanMethylationEPICv2anno.20a1.hg38 and the beta matrix normalized by the minfi package.
The following example reads raw data from one sample and performs preprocessing with minfi. aggregate_to_probes() calculates mean value for duplicated probes.
tempdir = tempdir()
datadir = paste0(tempdir, "/206891110001")
dir.create(datadir, showWarnings = FALSE)
url = "https://github.com/jokergoo/IlluminaHumanMethylationEPICv2manifest/files/11008723/206891110001_R01C01.zip"
local = paste0(tempdir, "/206891110001_R01C01.zip")
download.file(url, dest = local, quiet = TRUE)
unzip(local, exdir = datadir)
library(minfi)
RGset = read.metharray.exp(datadir)
annotation(RGset)["array"] = "IlluminaHumanMethylationEPICv2"
obj = preprocessRaw(RGset)
# there can be more intermediate steps ...
beta = getBeta(obj)
dim(beta)## [1] 936990 1head(beta)## 206891110001_R01C01
## cg25324105_BC11 0.86065794
## cg25383568_TC11 0.93799357
## cg25455143_BC11 0.02141582
## cg25459778_BC11 0.01751512
## cg25487775_BC11 0.94253932
## cg25595446_BC11 0.02561140beta2 = aggregate_to_probes(beta)
dim(beta2)## [1] 930596 1head(beta2)## 206891110001_R01C01
## cg25324105 0.86065794
## cg25383568 0.93799357
## cg25455143 0.02141582
## cg25459778 0.01751512
## cg25487775 0.94253932
## cg25595446 0.02561140aggregate_to_probes() can also be applied to the annotation data frames in IlluminaHumanMethylationEPICv2anno.20a1.hg38.
head(aggregate_to_probes(cgi2))## DataFrame with 6 rows and 2 columns
## Islands_Name Relation_to_Island
## <character> <character>
## cg25324105 chr19:37691892-37692.. Island
## cg25383568 chr19:38726890-38727.. Island
## cg25455143 chr19:1591428-159162.. Island
## cg25459778 chr16:1610053-1615094 Island
## cg25487775 chr2:161237949-16123.. Shore
## cg25595446 chr19:49676753-49677.. Island## R Under development (unstable) (2023-10-25 r85412)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/lorikern/R-Installs/bin/R-devel/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## time zone: America/New_York
## tzcode source: system (glibc)
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] IlluminaHumanMethylationEPICv2manifest_0.99.2
## [2] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [3] IlluminaHumanMethylationEPICv2anno.20a1.hg38_1.0.0
## [4] minfi_1.49.1
## [5] bumphunter_1.45.1
## [6] locfit_1.5-9.9
## [7] iterators_1.0.14
## [8] foreach_1.5.2
## [9] Biostrings_2.71.5
## [10] XVector_0.43.1
## [11] SummarizedExperiment_1.33.3
## [12] Biobase_2.63.0
## [13] MatrixGenerics_1.15.0
## [14] matrixStats_1.2.0
## [15] GenomicRanges_1.55.4
## [16] GenomeInfoDb_1.39.9
## [17] IRanges_2.37.1
## [18] S4Vectors_0.41.5
## [19] BiocGenerics_0.49.1
## [20] knitr_1.45
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## [3] magrittr_2.0.3 GenomicFeatures_1.55.4
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## [7] zlibbioc_1.49.3 vctrs_0.6.5
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