Entering edit mode
Hello,
I am conducting an EWAS of methylation data from human adult whole blood samples. The data are from the Illumina EPIC chip v2, and I'm using the IlluminaHumanMethylationEPICv2anno.20a1.hg38 package from Zuguang Gu (Thanks, again!) to annotate the reults.
There are some variables from the EPIC v1 annotation (IlluminaHumanMethylationEPICanno.ilm10b4.hg19) which I like to pull for downstream analysis but are not included in v2 annotation, including UCSC_RefGene_Group and GencodeBasicV12_Group.
I'm wondering if there are equivalent/similar variables in the v2 annotation or, if not, a way to obtain these?
Thank you very much!
Courtney
sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] parallel stats4 stats graphics grDevices utils
[7] datasets methods base
other attached packages:
[1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[2] sandwich_3.0-2
[3] sfsmisc_1.1-16
[4] lmtest_0.9-40
[5] zoo_1.8-12
[6] MASS_7.3-60
[7] janitor_2.2.0
[8] shinyMethyl_1.36.1
[9] methylclock_1.6.0
[10] quadprog_1.5-8
[11] devtools_2.4.5
[12] usethis_2.2.2
[13] methylclockData_1.8.1
[14] futile.logger_1.4.3
[15] wateRmelon_2.6.0
[16] illuminaio_0.42.0
[17] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
[18] ROC_1.76.0
[19] lumi_2.52.0
[20] methylumi_2.46.0
[21] FDb.InfiniumMethylation.hg19_2.2.0
[22] org.Hs.eg.db_3.17.0
[23] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[24] GenomicFeatures_1.52.2
[25] AnnotationDbi_1.62.2
[26] reshape2_1.4.4
[27] scales_1.2.1
[28] limma_3.56.2
[29] sesame_1.18.4
[30] sesameData_1.18.0
[31] FlowSorted.BloodExtended.EPIC_1.1.1
[32] FlowSorted.Blood.EPIC_2.4.2
[33] IlluminaHumanMethylationEPICv2anno.20a1.hg38_0.99.0
[34] IlluminaHumanMethylationEPICv2manifest_0.99.1
[35] ExperimentHub_2.8.1
[36] AnnotationHub_3.8.0
[37] BiocFileCache_2.8.0
[38] dbplyr_2.3.3
[39] data.table_1.14.8
[40] minfi_1.46.0
[41] bumphunter_1.42.0
[42] locfit_1.5-9.8
[43] iterators_1.0.14
[44] foreach_1.5.2
[45] Biostrings_2.68.1
[46] XVector_0.40.0
[47] SummarizedExperiment_1.30.2
[48] Biobase_2.60.0
[49] MatrixGenerics_1.12.3
[50] matrixStats_1.0.0
[51] GenomicRanges_1.52.0
[52] GenomeInfoDb_1.36.4
[53] IRanges_2.34.1
[54] S4Vectors_0.38.1
[55] BiocGenerics_0.46.0
[56] pwr_1.3-0
[57] readxl_1.4.3
[58] lubridate_1.9.2
[59] forcats_1.0.0
[60] stringr_1.5.0
[61] dplyr_1.1.2
[62] purrr_1.0.2
[63] readr_2.1.4
[64] tidyr_1.3.0
[65] tibble_3.2.1
[66] ggplot2_3.4.4
[67] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] fs_1.6.3
[2] bitops_1.0-7
[3] httr_1.4.7
[4] RColorBrewer_1.1-3
[5] dynamicTreeCut_1.63-1
[6] backports_1.4.1
[7] profvis_0.3.8
[8] tools_4.3.1
[9] doRNG_1.8.6
[10] utf8_1.2.3
[11] R6_2.5.1
[12] HDF5Array_1.28.1
[13] mgcv_1.9-0
[14] rhdf5filters_1.12.1
[15] urlchecker_1.0.1
[16] withr_2.5.0
[17] gridExtra_2.3
[18] prettyunits_1.1.1
[19] base64_2.0.1
[20] preprocessCore_1.62.1
[21] quantreg_5.97
[22] cli_3.6.1
[23] pacman_0.5.1
[24] formatR_1.14
[25] AnnotationHubData_1.30.0
[26] genefilter_1.82.1
[27] askpass_1.2.0
[28] Rsamtools_2.16.0
[29] siggenes_1.74.0
[30] stringdist_0.9.10
[31] AnnotationForge_1.42.2
[32] sessioninfo_1.2.2
[33] scrime_1.3.5
[34] impute_1.74.1
[35] rstudioapi_0.15.0
[36] RSQLite_2.3.1
[37] generics_0.1.3
[38] BiocIO_1.10.0
[39] car_3.1-2
[40] Matrix_1.6-1
[41] fansi_1.0.4
[42] abind_1.4-5
[43] lifecycle_1.0.3
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[55] lattice_0.21-8
[56] annotate_1.78.0
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[58] pillar_1.9.0
[59] knitr_1.45
[60] beanplot_1.3.1
[61] rjson_0.2.21
[62] codetools_0.2-19
[63] glue_1.6.2
[64] remotes_2.4.2.1
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[68] cellranger_1.1.0
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[71] cachem_1.0.8
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[73] S4Arrays_1.0.6
[74] mime_0.12
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[91] compiler_4.3.1
[92] curl_5.0.2
[93] graph_1.78.0
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[96] xml2_1.3.5
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[99] rtracklayer_1.60.1
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[105] rmarkdown_2.24
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[107] htmltools_0.5.6
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[110] fastmap_1.1.1
[111] htmlwidgets_1.6.2
[112] rlang_1.1.1
[113] shiny_1.7.5
[114] DelayedMatrixStats_1.22.6
[115] jsonlite_1.8.7
[116] BiocParallel_1.34.2
[117] mclust_6.0.0
[118] wheatmap_0.2.0
[119] RCurl_1.98-1.12
[120] magrittr_2.0.3
[121] polynom_1.4-1
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[130] plyr_1.8.8
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[132] splines_4.3.1
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[134] hms_1.1.3
[135] ps_1.7.5
[136] ggpubr_0.6.0
[137] RUnit_0.4.32
[138] ggsignif_0.6.4
[139] rngtools_1.5.2
[140] pkgload_1.3.2.1
[141] biomaRt_2.56.1
[142] futile.options_1.0.1
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[144] XML_3.99-0.14
[145] evaluate_0.21
[146] lambda.r_1.2.4
[147] BiocManager_1.30.22
[148] tzdb_0.4.0
[149] httpuv_1.6.11
[150] MatrixModels_0.5-2
[151] openssl_2.1.0
[152] reshape_0.8.9
[153] broom_1.0.5
[154] xtable_1.8-4
[155] restfulr_0.0.15
[156] rstatix_0.7.2
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[158] OrganismDbi_1.42.0
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[162] writexl_1.4.2
[163] timechange_0.2.0
Hello,
Related question to this post. How could I combine arrays EPIC v1 and v2? I generate mVals matrix tables by separate for the EPIC v1 and v2 data sets that I have. However, now I am not sure how to continue to match the different probes names.
Thanks a lot!
For what's worth, sesame has a liftOver function that maps EPICv1 to v2 or backward. https://github.com/zwdzwd/sesame/blob/2d5c2ab371430a8ecb1b5f09792457505a59d192/R/impute.R#L44 Hope it's helpful.
Thanks a lot! I'll take a look at it!
Hello again! Kind of related question, I read the Sesame manual but I could not find a way to plot the beta value index. I am refering to number 1 to 0 and the color that is associated. Thanks a lot and sorry for the naive question!