The methylclockData package is a repository of a few public datasets that needs the methylclock package to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks.
The biological DNAm clocks implemented in our package are:
In the below example, we show how one can download this dataset from ExperimentHub.
library(ExperimentHub)
library(methylclockData)
# Get experimentHub records
eh <- ExperimentHub()
# Get data about methylclockData experimentHub
pData <- query(eh , "methylclockData")
# Get information rows about methylclockData
df <- mcols(pData)
df## DataFrame with 20 rows and 15 columns
## title dataprovider species taxonomyid genome
## <character> <character> <character> <integer> <character>
## EH3913 Datasets to estimate.. NA Homo sapiens 9606 hg19
## EH6068 CpGs BNN clock NA Homo sapiens 9606 hg19
## EH6069 Coefficients Bohlin'.. NA Homo sapiens 9606 hg19
## EH6070 Coefficients Hannum'.. NA Homo sapiens 9606 hg19
## EH6071 Coefficients Hobarth.. NA Homo sapiens 9606 hg19
## ... ... ... ... ... ...
## EH6082 Test Dataset NA Homo sapiens 9606 hg19
## EH6083 References NA Homo sapiens 9606 hg19
## EH7367 Coefficients BLUPclock NA Homo sapiens 9606 hg19
## EH7368 Coefficients EN clock NA Homo sapiens 9606 hg19
## EH7369 Coefficients EPIC cl.. NA Homo sapiens 9606 hg19
## description coordinate_1_based maintainer
## <character> <integer> <character>
## EH3913 Predefined datasets .. 0 Juan R Gonzalez <jua..
## EH6068 Horvath’s CpGs to tr.. 0 Juan R Gonzalez <jua..
## EH6069 96 CpGs described in.. 0 Juan R Gonzalez <jua..
## EH6070 71 CpGs described in.. 0 Juan R Gonzalez <jua..
## EH6071 353 CpGs described i.. 0 Juan R Gonzalez <jua..
## ... ... ... ...
## EH6082 Test dataset 0 Juan R Gonzalez <jua..
## EH6083 References 0 Juan R Gonzalez <jua..
## EH7367 319607 CpGs describe.. 0 Juan R Gonzalez <jua..
## EH7368 514 CpGs described i.. 0 Juan R Gonzalez <jua..
## EH7369 176 CpGs described i.. 0 Juan R Gonzalez <jua..
## rdatadateadded preparerclass tags
## <character> <character> <AsIs>
## EH3913 2020-11-18 methylclockData GEO,ExperimentHub,Tissue
## EH6068 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH6069 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH6070 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH6071 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## ... ... ... ...
## EH6082 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH6083 2021-05-18 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH7367 2022-03-29 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH7368 2022-03-29 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## EH7369 2022-03-29 methylclockData Homo_sapiens_Data,OrganismData,Tissue
## rdataclass rdatapath sourceurl sourcetype
## <character> <character> <character> <character>
## EH3913 List methylclockData/meth.. https://github.com/p.. RDA
## EH6068 character methylclockData/cpgs.. https://github.com/i.. RDA
## EH6069 data.frame methylclockData/coef.. https://github.com/i.. RDA
## EH6070 data.frame methylclockData/coef.. https://github.com/i.. RDA
## EH6071 data.frame methylclockData/coef.. https://github.com/i.. RDA
## ... ... ... ... ...
## EH6082 Lists methylclockData/Test.. https://github.com/i.. RDA
## EH6083 data.frame methylclockData/refe.. https://github.com/i.. RDA
## EH7367 data.frame methylclockData/coef.. https://github.com/i.. RDA
## EH7368 data.frame methylclockData/coef.. https://github.com/i.. RDA
## EH7369 data.frame methylclockData/coef.. https://github.com/i.. RDA# Retrieve data with Hobarth's clock coefficients
pData["EH6071"]## ExperimentHub with 1 record
## # snapshotDate(): 2022-04-19
## # names(): EH6071
## # package(): methylclockData
## # $dataprovider: NA
## # $species: Homo sapiens
## # $rdataclass: data.frame
## # $rdatadateadded: 2021-05-18
## # $title: Coefficients Hobarth's clock
## # $description: 353 CpGs described in Horvath (2013)
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: RDA
## # $sourceurl: https://github.com/isglobal-brge/methylclock/blob/master/data
## # $sourcesize: NA
## # $tags: c("Homo_sapiens_Data", "OrganismData", "Tissue")
## # retrieve record with 'object[["EH6071"]]'We also implemented some functions to easy access to the different datasets , for example, we can access to Hovarths CpGs to train a Bayesian Neural Network with function get_cpgs_bn or to get_coefHannum for Hannum’s clock coefficients
# Hovarths CpGs to train a Bayesian Neural Network
cpgs.bn <- get_cpgs_bn()
head(cpgs.bn)## [1] "cg00029931" "cg00043004" "cg00059225" "cg00141845" "cg00143376"
## [6] "cg00152644"# Hannum's clock coefficients
coefHannum <- get_coefHannum()
head(coefHannum)## CpGmarker Chrom Pos Genes CpG.Island CoefficientTraining
## 1 cg20822990 1 17338766 ATP13A2,SDHB No -15.70
## 2 cg22512670 1 26855765 RPS6KA1 No 1.05
## 3 cg25410668 1 28241577 RPA2,SMPDL3B No 3.87
## 4 cg04400972 1 117665053 TRIM45,TTF2 Yes 9.62
## 5 cg16054275 1 169556022 F5,SELP No -11.10
## 6 cg10501210 1 207997020 C1orf132 No -6.46# Hobarth's clock coefficients
coefHorvath <- get_coefHorvath()
head(coefHorvath)## CpGmarker CoefficientTraining CoefficientTrainingShrunk varByCpG minByCpG
## 1 (Intercept) 0.695507258 0.8869198 NA NA
## 2 cg00075967 0.129336610 0.1080961 0.02600 0.0160
## 3 cg00374717 0.005017857 NA 0.01200 0.0031
## 4 cg00864867 1.599764050 1.8639686 0.00087 0.0000
## 5 cg00945507 0.056852418 NA 0.01600 0.0000
## 6 cg01027739 0.102862854 NA 0.01100 0.0000
## maxByCpG medianByCpG medianByCpGYoung medianByCpGOld Gene_ID GenomeBuild Chr
## 1 NA NA NA NA NA NA NA
## 2 0.97 0.750 0.720 0.750 64220 36 15
## 3 1.00 0.890 0.870 0.900 22901 36 17
## 4 0.58 0.049 0.045 0.053 5074 36 12
## 5 0.96 0.240 0.230 0.250 23480 36 7
## 6 0.99 0.071 0.067 0.074 57171 36 9
## MapInfo SourceVersion TSS_Coordinate Gene_Strand Symbol Synonym
## 1 NA NA NA
## 2 72282407 36.1 72282245 - STRA6 PP14296; FLJ12541;
## 3 63814740 36.1 63815191 + ARSG KIAA1001;
## 4 78609399 36.1 78608921 - PAWR PAR4; Par-4;
## 5 54795171 36.1 54794433 - SEC61G SSS1;
## 6 130882559 36.1 130883227 + DOLPP1 LSFR2;
## Accession GID
## 1
## 2 NM_022369.2 GI:21314699
## 3 NM_014960.2 GI:45430056
## 4 NM_002583.2 GI:55769532
## 5 NM_014302.3 GI:60279263
## 6 NM_020438.3 GI:48976059
## Annotation
## 1
## 2 synonyms: PP14296; FLJ12541
## 3 go_function: hydrolase activity; go_function: calcium ion binding; go_function: sulfuric ester hydrolase activity; go_process: metabolism
## 4 ###############################################################################################################################################################################################################################################################
## 5 ###############################################################################################################################################################################################################################################################
## 6 ###############################################################################################################################################################################################################################################################
## Product Marginal.Age.Relationship
## 1
## 2 stimulated by retinoic acid gene 6 homolog positive
## 3 Arylsulfatase G positive
## 4 PRKC; apoptosis; WT1; regulator positive
## 5 Sec61 gamma subunit positive
## 6 dolichyl pyrophosphate phosphatase 1 positive# Knight's clock coefficients
coefKnightGA <- get_coefKnightGA()
head(coefKnightGA)## CpGmarker CoefficientTraining
## 1 (Intercept) 41.7257976
## 2 cg00022866 0.6935522
## 3 cg00466249 -0.8255749
## 4 cg00546897 -1.3585156
## 5 cg00575744 -3.8292857
## 6 cg00689340 0.9603426# Lee's Gestational Age clock coefficients
coefLeeGA <- get_coefLeeGA()
head(coefLeeGA)## CpGmarker Coefficient_RPC Coefficient_CPC Coefficient_refined_RPC
## 1 (Intercept) 24.997721330 13.06182050 30.7496621
## 2 cg00009871 -0.124656981 0.00000000 -0.8186162
## 3 cg00035630 -0.006478271 0.00000000 0.0000000
## 4 cg00056066 0.859460073 0.01974736 0.1672244
## 5 cg00057476 0.372322642 0.78281228 0.3493741
## 6 cg00063979 -0.509028605 0.00000000 -1.0678955
## Coefficient_sex_classifier
## 1 1.801244
## 2 0.000000
## 3 0.000000
## 4 0.000000
## 5 0.000000
## 6 0.000000# Levine's clock coefficients
coefLevine <- get_coefLevine()
head(coefLevine)## CpGmarker Chromosome Map.Info Gene.Symbol Entrez.ID CoefficientTraining
## 1 Intercept NA NA NA 60.66400
## 2 cg15611364 3 25806427 OXSM 54995 63.12415
## 3 cg17605084 12 53177758 HEM1 3071 -44.00939
## 4 cg26382071 17 6485627 TXNL5 84817 40.42085
## 5 cg12743894 11 30301513 C11orf46 120534 36.78818
## 6 cg19287114 9 107046432 SLC44A1 23446 -36.49384
## Univariate.Age.Correlation Horvath.Overlap Hannum.Overlap
## 1 NA
## 2 0.003807203 No No
## 3 -0.029169140 No No
## 4 0.002996738 No No
## 5 -0.008386638 No No
## 6 -0.118250325 No No# Mayne's clock coefficients
coefMayneGA <- get_coefMayneGA()
head(coefMayneGA)## CpGmarker CoefficientTraining Gene Chromosome Position
## 1 (Intercept) 24.9902644 <NA> NA NA
## 2 cg12146151 -1.9282536 ITGB4 17 73716945
## 3 cg16127845 -26.4451900 GPER1 7 1126423
## 4 cg17133388 -17.3515068 FAM162A 3 122102727
## 5 cg13997435 -0.9527281 S100A2 1 153538406
## 6 cg12360736 -5.6655794 MBNL1 3 151985869
## Correlation.with.GA
## 1 NA
## 2 -0.6415864
## 3 -0.6350808
## 4 -0.6118330
## 5 -0.6076825
## 6 -0.6018116# PedBE's clock coefficients
coefPedBE <- get_coefPedBE()
head(coefPedBE)## CpGmarker CoefficientTraining corAgeTraining corAgeTest
## 1 (Intercept) -2.097349336 NA NA
## 2 cg00059225 0.021960020 0.8661036 0.8903716
## 3 cg00085493 -0.100396107 -0.7801136 -0.8090162
## 4 cg00095976 0.007872419 0.6874745 0.7573932
## 5 cg00609333 0.022823643 0.6291968 0.7982873
## 6 cg01287592 -0.055414703 -0.7201259 -0.5782867# Horvath's skin+blood clock coefficients
coefSkin <- get_coefSkin()
head(coefSkin)## CpGmarker CoefficientTraining
## 1 (Intercept) -0.447119319
## 2 cg12140144 0.363181170
## 3 cg26933021 -0.090500090
## 4 cg20822990 -0.007025234
## 5 cg07312601 -0.135092400
## 6 cg09993145 -0.042639340# Telomere Length clock coefficients
coefTL <- get_coefTL()
head(coefTL)## CpGmarker CoefficientTraining CHR bp19
## 1 Intercept 7.92478005 NA
## 2 cg05528516 -0.03490186 1 1070040
## 3 cg00060374 0.19265502 1 1355235
## 4 cg12711627 0.17826976 1 2025769
## 5 cg06853416 0.05488373 1 2778841
## 6 cg01901101 -0.28813724 1 9486939
## SourceSeq Strand
## 1
## 2 CGGACCCCCACCGGCCTCCAAATGTGCAAACACAGGCGCCTCTCAGGCAC R
## 3 CGGCTTCATGCTGGTGGCAGCAACAGACTCTCCGCCAGCGCCGGGCCTGT R
## 4 CGGGCACCACACAGCATCCCAGGCACCATCATGGTAGGAGAAGAGTTCAG R
## 5 AGAGTCCCCCTCTGGATTCACACACCTGGAGGCGTCTGAGTGACTCCTCG R
## 6 CAAAAAAACCCCAGCTTTTGTCCAGAGGTTGCTTTTTGTGGGTCTGTACG F
## Probe.SNP.based.on.Illumina.annotation Gene location
## 1
## 2 C1orf159-TTLL10
## 3 LOC441869 Body;Body
## 4 PRKCZ Body;5'UTR
## 5 MMEL1-ACTRT2
## 6 SPSB1-SLC25A33
## Relation.to..CpGIsland Probe.SNP.based.on.1000.Genome SNP CHR.SNP
## 1 NA
## 2 N_Shelf N rs74048017 1
## 3 Island N rs1240717 1
## 4 Island NA
## 5 S_Shore N rs12409348 1
## 6 N_Shore N rs10864418 1
## bp.SNP ALLELE BSGS.P BSGS.EFFECT BSGS.H2 LBC.P LBC.EFFECT LBC.R2 mQTL
## 1 NA NA NA NA NA NA NA
## 2 1069602 T 2.61e-42 0.258 0.38864 2.56e-47 0.16960 0.14200 cis
## 3 1346257 A 2.98e-58 -0.440 0.45741 9.87e-84 -0.51230 0.24090 cis
## 4 NA NA NA NA NA NA NA
## 5 2779043 C 2.59e-39 -0.206 0.34692 3.24e-78 -0.24150 0.22670 cis
## 6 9489972 G 8.21e-08 0.037 0.06047 3.51e-17 0.06817 0.05077 cis# BLUP clock coefficients
coefBLUP <- get_coefBLUP()
head(coefBLUP)## CpGmarker CoefficientTraining
## 1 Intercept 91.1539600000
## 2 cg18478105 -0.0111273610
## 3 cg14361672 0.0005570068
## 4 cg01763666 -0.0176955658
## 5 cg02115394 0.0090890982
## 6 cg13417420 -0.0163150063# EN clock coefficients
coefEN <- get_coefEN()
head(coefEN)## CpGmarker CoefficientTraining
## 1 Intercept 65.792950000
## 2 cg24611351 -0.001810743
## 3 cg24173182 -0.203954555
## 4 cg09604333 -0.703967823
## 5 cg13617776 -0.011524460
## 6 cg09432590 1.048977229# EPIC clock coefficients
coefEPIC <- get_coefEPIC()
head(coefEPIC)## # A tibble: 6 × 2
## CpGmarker CoefficientTraining
## <chr> <dbl>
## 1 (Intercept) 293.
## 2 cg12701018 -0.0916
## 3 cg00078456 -2.31
## 4 cg14958032 -0.479
## 5 cg00735586 -11.7
## 6 cg09035049 -8.51# Wu's clock coefficients
Wu <- get_coefWu()
head(Wu)## CpGmarker CoefficientTraining GeneID GenomeBuild Chr MapInfo UGRepAcc
## 1 (Intercept) 2.3768538 NA NA NA NA
## 2 cg00343092 -1.1359977 706 36 22 41877918 NM_000714.4
## 3 cg00563932 -2.2549679 5730 36 9 138990870 NM_000954.5
## 4 cg00571634 1.2051302 54554 36 3 123617510 NM_019069.3
## 5 cg00629217 -1.4431147 84708 36 4 54120106 NM_032622.1
## 6 cg01511567 0.5572547 6749 36 11 56860207 NM_003146.2
## Symbol
## 1
## 2 TSPO
## 3 PTGDS
## 4 WDR5B
## 5 LNX1
## 6 SSRP1# # references
references <- get_references()
load(references)For more information in how loading and use of the data, please, refer to MethylClock vignette
sessionInfo()## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] methylclockData_1.4.0 futile.logger_1.4.3 ExperimentHub_2.4.0
## [4] AnnotationHub_3.4.0 BiocFileCache_2.4.0 dbplyr_2.1.1
## [7] BiocGenerics_0.42.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] matrixStats_0.62.0 bitops_1.0-7
## [3] bit64_4.0.5 filelock_1.0.2
## [5] progress_1.2.2 httr_1.4.2
## [7] GenomeInfoDb_1.32.0 tools_4.2.0
## [9] bslib_0.3.1 utf8_1.2.2
## [11] R6_2.5.1 DBI_1.1.2
## [13] withr_2.5.0 tidyselect_1.1.2
## [15] prettyunits_1.1.1 bit_4.0.4
## [17] curl_4.3.2 compiler_4.2.0
## [19] graph_1.74.0 cli_3.3.0
## [21] Biobase_2.56.0 BiocCheck_1.32.0
## [23] formatR_1.12 AnnotationHubData_1.26.0
## [25] xml2_1.3.3 DelayedArray_0.22.0
## [27] rtracklayer_1.56.0 bookdown_0.26
## [29] sass_0.4.1 RBGL_1.72.0
## [31] rappdirs_0.3.3 stringr_1.4.0
## [33] digest_0.6.29 Rsamtools_2.12.0
## [35] rmarkdown_2.14 stringdist_0.9.8
## [37] AnnotationForge_1.38.0 XVector_0.36.0
## [39] pkgconfig_2.0.3 htmltools_0.5.2
## [41] MatrixGenerics_1.8.0 fastmap_1.1.0
## [43] rlang_1.0.2 RSQLite_2.2.12
## [45] shiny_1.7.1 jquerylib_0.1.4
## [47] BiocIO_1.6.0 generics_0.1.2
## [49] jsonlite_1.8.0 BiocParallel_1.30.0
## [51] dplyr_1.0.8 RCurl_1.98-1.6
## [53] magrittr_2.0.3 GenomeInfoDbData_1.2.8
## [55] Matrix_1.4-1 Rcpp_1.0.8.3
## [57] S4Vectors_0.34.0 fansi_1.0.3
## [59] lifecycle_1.0.1 stringi_1.7.6
## [61] yaml_2.3.5 SummarizedExperiment_1.26.0
## [63] zlibbioc_1.42.0 biocViews_1.64.0
## [65] grid_4.2.0 blob_1.2.3
## [67] parallel_4.2.0 promises_1.2.0.1
## [69] crayon_1.5.1 lattice_0.20-45
## [71] Biostrings_2.64.0 GenomicFeatures_1.48.0
## [73] hms_1.1.1 KEGGREST_1.36.0
## [75] knitr_1.39 pillar_1.7.0
## [77] RUnit_0.4.32 GenomicRanges_1.48.0
## [79] rjson_0.2.21 codetools_0.2-18
## [81] biomaRt_2.52.0 stats4_4.2.0
## [83] futile.options_1.0.1 XML_3.99-0.9
## [85] glue_1.6.2 BiocVersion_3.15.2
## [87] evaluate_0.15 lambda.r_1.2.4
## [89] ExperimentHubData_1.22.0 BiocManager_1.30.17
## [91] png_0.1-7 vctrs_0.4.1
## [93] httpuv_1.6.5 purrr_0.3.4
## [95] assertthat_0.2.1 cachem_1.0.6
## [97] xfun_0.30 mime_0.12
## [99] xtable_1.8-4 restfulr_0.0.13
## [101] later_1.3.0 tibble_3.1.6
## [103] OrganismDbi_1.38.0 GenomicAlignments_1.32.0
## [105] AnnotationDbi_1.58.0 memoise_2.0.1
## [107] IRanges_2.30.0 ellipsis_0.3.2
## [109] interactiveDisplayBase_1.34.0