• MorganLevineLab/methylCIPHER
  • Installation
  • Calculating Epigenetic Clocks and Predictors
    • Running single “clock” calculations
    • Categories of Epigenetic Clocks
    • Running Multiple Epigenetic Clocks Simultaneously
    • Missing beta values
• Citations!
  • citeMyClocks()
  • calcCoreClocks Function
  • Chronological Age Predictors
  • Cancer and Mitotic Rates
  • Gestational & Pediatric Age
  • Biological Age and Mortality Predictors
  • Trait Predictors
  • Bespoke Clocks

On installation from Github, in R, install learnr and run learnr::run_tutorial(name = "instructions", package = "methylCIPHER") for an interactive tutorial.

The goal of methylCIPHER is to allow users to easily calculate their choice of CpG clocks using simple commands, from a single source. CpG epigenetic clocks are currently found in many places, and some require users to send data to external portals, which is not advisable when working with protected or restricted data. The current package allows you to calculate reported epigenetic clocks, or where not precisely disclosed, our best estimates–all performed locally on your own machine in R Studio. We would like to acknowledge the authors of the original clocks and their valuable contributions to aging research, and the requisite citations for their clocks can be found at the bottom of the current page. Please do not forget to cite them in your work!

You can install the released version of methylCIPHER and its imported packages from Github with:

devtools::install_github("MorganLevineLab/prcPhenoAge")
devtools::install_github("danbelsky/DunedinPoAm38")
devtools::install_github("MorganLevineLab/methylCIPHER")

The current package contains a large number of currently available CpG clocks or CpG based predictors. While we strove to be inclusive of such published CpG-based epigenetic clocks to our knowledge, if you find we are missing a clock, please contact us and we will do our best to promptly include it, if possible. You can do so by raising an issue on this repo or emailing us directly at <kyra[dot]thrush[at]yale[dot]edu>.

In order to calculate a CpG clock, you simply need to use the appropriate function, typically named “calc[ClockNameHere]”. For example:

library(methylCIPHER)
calcPhenoAge(exampleBetas, examplePheno, imputation = F)

Alternatively, if you would just like to receive a vector with the clock values to use, rather than appending it to an existing phenotype/ demographic dataframe, simply use:

calcPhenoAge(exampleBetas, imputation = F)
#> 7786915023_R02C02 7786915135_R04C02 7471147149_R06C01 7786915035_R05C01 
#>          52.29315          41.05867          43.54460          43.96697 
#> 7786923035_R01C01 
#>          40.35242

Due to the abundance of epigenetic clocks are overlapping reasons for use, it is important to keep track of which clocks are most related to each other. This will allow you to steer clear of multiple testing and collinearity problems if you use all available clocks for your analysis.

Another important note is that with a few exceptions, the list of following clocks were trained and validated almost exclusively in blood. This can lead to a number of observed effects, which include unreasonable shifts in age (intercept)As bespoke clocks are developed for use in additional human tissues, we will include these in their own section below.

• Bocklandt (calcBocklandt)
• Garagnani (calcGaragnani)
• Hannum (calcHannum)
• Horvath MultiTissue (calcHorvath1)
• Lin 99 CpG clock (calcLin)
• Vidal-Bralo (calcVidalBralo)
• Weidner (calcWeidner)
• Zhang 10 CpG clock (calcZhang)

• Epigenetic Timers of Cancer:
  • EpiTOC (calcEpiTOC)
  • EpiTOC2 (calcEpiTOC2)
• solo-WCGWsites, or hypoclock (calcHypoClock)
• MiAge Calculator (calcMiAge)

• Bohlin (calcBohlin)
• Knight (calcKnight)
• Lee Gestational Age:
  • calcLeeControl
  • calcLeeRobust
  • calcLeeRefinedRobust
• Mayne (calcMayne)
• Pediatric-Buccal-Epigenetic Clock (calcPEDBE)

• Dunedin Pace of Aging (DunedinPoAm38)
  • Updated DunedinPACE: available elsewhere
• PhenoAge:
  • Original 513 CpG PhenoAge (calcPhenoAge)
    • Polycomb Repressor Complex Associated Subscore (calcprcPhenoAge)
    • PRC dissociated Subscore (calcnonprcPhenoAge)
  • Updated PhenoAge (calcHRSInChPhenoAge)
• GrimAge: not available, use formatHorvathOnline Horvath Online Calculator

• McCartney’s Predictors:
  • Alcohol (calcAlcoholMcCartney)
  • BMI (calcBMIMcCartney)
  • Smoking (calcSmokingMcCartney)
• PhosphatidylEthanol: Hazardous Alcohol Use (calcPEth)

• DNAmAge_Cortical
  • A brain tissue trained clock.
• Horvath Skin & Blood (calcHorvath2)

There are a number of epigenetic clocks available in the literature, but the majority of studies currently focus upon comparing a select few. Therefore, we have provided a helper function to quickly calculate these core clocks in just one line of code.

calcCoreClocks(exampleBetas, examplePheno)
#> Please remember to cite the core Clocks you have used! Please refer to the README.md file for assistance.

The user is welcome to specify a vector of clocks that they would like to calculate, rather than running each individual clock calculation. In this case, you will need to choose from the following options:

clockOptions()
#>  [1] "calcAlcoholMcCartney"            "calcBMIMcCartney"               
#>  [3] "calcBocklandt"                   "calcBohlin"                     
#>  [5] "calcDNAmClockCortical"           "calcDNAmTL"                     
#>  [7] "calcDunedinPoAm38"               "calcEpiTOC"                     
#>  [9] "calcEpiTOC2"                     "calcGaragnani"                  
#> [11] "calcHannum"                      "calcHorvath1"                   
#> [13] "calcHorvath2"                    "calcHRSInChPhenoAge"            
#> [15] "calcHypoClock"                   "calcKnight"                     
#> [17] "calcLeeControl"                  "calcLeeRefinedRobust"           
#> [19] "calcLeeRobust"                   "calcLin"                        
#> [21] "calcMayne"                       "calcMiAge"                      
#> [23] "calcPEDBE"                       "calcPhenoAge"                   
#> [25] "calcSmokingMcCartney"            "calcVidalBralo"                 
#> [27] "calcWeidner"                     "calcZhang"                      
#> [29] "calcZhang2019"                   "prcPhenoAge::calcPRCPhenoAge"   
#> [31] "prcPhenoAge::calcnonPRCPhenoAge" "DunedinPoAm38::PoAmProjector"

To do so, here is an example:

userClocks <- c("calcSmokingMcCartney","calcPhenoAge","calcEpiTOC2")
calcUserClocks(userClocks, exampleBetas, examplePheno, imputation = F)

Of course, all of the CpG clocks work best when you have all of the necessary probes’ beta values for each sample. However, sometimes after preprocessing, beta values will be removed for a variety of reasons. For each CpG clock, you have the option to impute missing values for CpGs that were removed across all samples. In this case, you will need to impute using a vector of your choice (e.g. mean methylation values across CpGs from an independent tissue-matched dataset). However, by default, imputation will not be performed and the portion of the clock that is reliant upon those CpGs will not be considered. To check quickly whether this is the case for your data and clock(s) of interest, we have created the following helper function:

getClockProbes(exampleBetas)

Please note that this will not count columns of NAs for named CpGs as missing! If you want to check for this you can run the following line of code to find the column numbers that are all NAs. If you get “named integer(0)” then you don’t have any. We recommend that you remove any identified columns from your beta matrix entirely to avoid errors, and then rerun the code producing the table above.

which(apply(exampleBetas, 2, function(x)all(is.na(x))))

In the case that you have CpGs missing from only some samples, we encourage you to be aware of this early on. Run the following line and check that it is 0.

sum(is.na(betaMatrix))

If this does not end up being 0, you might consider running mean imputation within your data so that NA values for single/ few samples at least have mean values rather than being ignored.

If you would like to lower the noise in your data, which is helpful for applications in clinical, intervention, or longitudinal data in particular, you might consider instead using our recently developed PC Clocks. You can use this code and refer to this publication.

[should we include this?]

The current package is intended to increase visibility and accesibility for the many epigenetic clocks currently in circulation. Please do not forget to cite not only this work, but the works you use in your research the correspond to the appropriate epigenetic clocks.

As an alternative to manually checking the list below, you can simply use the function citeMyClocks to automatically print out APA style citations of the appropriate publications. You can use a character vector or input with the function name(s) you used, or the same userClocks list you may have used for calcUserClocks.

citeMyClocks(userClocks, prettyprint = TRUE)
#> Clock Citations:
#> Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., … Horvath, S. (2018). 
#>  An epigenetic biomarker of aging for lifespan and healthspan. 
#>  Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414
#> McCartney, D. L., Hillary, R. F., Stevenson, A. J., Ritchie, S. J., Walker, R. M., Zhang, Q., … Marioni, R. E. (2018). 
#>  Epigenetic prediction of complex traits and death. 
#>  Genome Biology, 19(1), 136. https://doi.org/10.1186/s13059-018-1514-1
#> Teschendorff, A. E. (2020). 
#>  A comparison of epigenetic mitotic-like clocks for cancer risk prediction. 
#>  Genome Medicine, 12(1), 56. https://doi.org/10.1186/s13073-020-00752-3

Please note that by default the function formats the output for nicer console output. Change to prettyprint = FALSE for a vector you can save in your workspace.

If you used the calcCoreClocks function then the list of citations you will need is as below. Otherwise, please find the appropriate citations for the calculations you did run in their respective sections below.

• Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S. V., … Zhang, K. (2013). Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Molecular Cell, 49(2), 359–367. https://doi.org/10.1016/j.molcel.2012.10.016
• Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), 3156. https://doi.org/10.1186/gb-2013-14-10-r115
• Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., … Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414
• Teschendorff, A. E. (2020). A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Medicine, 12(1), 56. https://doi.org/10.1186/s13073-020-00752-3

• calcBocklandt: Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011). Epigenetic Predictor of Age. PLOS ONE, 6(6), e14821. https://doi.org/10.1371/journal.pone.0014821
• calcGaragnani: Garagnani, P., Bacalini, M. G., Pirazzini, C., Gori, D., Giuliani, C., Mari, D., … Franceschi, C. (2012). Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell, 11(6), 1132–1134. https://doi.org/10.1111/acel.12005
• calcHannum: Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S. V., … Zhang, K. (2013). Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Molecular Cell, 49(2), 359–367. https://doi.org/10.1016/j.molcel.2012.10.016
• calcHorvath1: Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), 3156. https://doi.org/10.1186/gb-2013-14-10-r115
• calcLin: Lin, Q., Weidner, C. I., Costa, I. G., Marioni, R. E., Ferreira, M. R. P., Deary, I. J., & Wagner, W. (2016). DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging, 8(2), 394–401. https://doi.org/10.18632/aging.100908
• calcVidalBralo: Vidal-Bralo, L., Lopez-Golan, Y., & Gonzalez, A. (2016). Simplified assay for epigenetic age estimation in whole blood of adults. Frontiers in Genetics, 7(JUL), 1–7. https://doi.org/10.3389/fgene.2016.00126
• calcWeidner: Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., … Wagner, W. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biology, 15(2). https://doi.org/10.1186/gb-2014-15-2-r24
• calcZhang: Zhang, Y., Wilson, R., Heiss, J., Breitling, L. P., Saum, K. U., Schöttker, B., … Brenner, H. (2017). DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nature Communications, 8. https://doi.org/10.1038/ncomms14617
• calcZhang2019: Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., … Visscher, P. M. (2019). Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Medicine, 11(1), 54. https://doi.org/10.1186/s13073-019-0667-1

• calcDNAmTL: Lu, A. T., Seeboth, A., Tsai, P. C., Sun, D., Quach, A., Reiner, A. P., … Horvath, S. (2019). DNA methylation-based estimator of telomere length. Aging, 11(16), 5895–5923. https://doi.org/10.18632/aging.102173
• calcEpiTOC: Yang, Z., Wong, A., Kuh, D., Paul, D. S., Rakyan, V. K., Leslie, R. D., … Teschendorff, A. E. (2016). Correlation of an epigenetic mitotic clock with cancer risk. Genome Biology, 17(1), 1–18. https://doi.org/10.1186/s13059-016-1064-3
• calcEpiTOC2 + calcHypoClock: Teschendorff, A. E. (2020). A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Medicine, 12(1), 56. https://doi.org/10.1186/s13073-020-00752-3
• calcMiAge: Youn, A., & Wang, S. (2018). The MiAge Calculator: a DNA methylation-based mitotic age calculator of human tissue types. Epigenetics, 13(2), 192–206. https://doi.org/10.1080/15592294.2017.1389361

• calcBohlin: Bohlin, J., Håberg, S. E., Magnus, P., Reese, S. E., Gjessing, H. K., Magnus, M. C., … Nystad, W. (2016). Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biology, 17(1), 1–9. https://doi.org/10.1186/s13059-016-1063-4
• calcKnight: Knight, A. K., Craig, J. M., Theda, C., Bækvad-Hansen, M., Bybjerg-Grauholm, J., Hansen, C. S., … Smith, A. K. (2016). An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biology, 17(1), 1–11. https://doi.org/10.1186/s13059-016-1068-z
• Lee, Y., Choufani, S., Weksberg, R., Wilson, S. L., Yuan, V., Burt, A., … Horvath, S. (2019). Placental epigenetic clocks: Estimating gestational age using placental DNA methylation levels. Aging, 11(12), 4238–4253. https://doi.org/10.18632/aging.102049
  • calcLeeControl: Control Placental Sample Age Calculator
  • calcLeeRobust: Robust predictor of Placental Age
  • calcLeeRefinedRobust: A refined version of the Robust Predictor of Placental Age
• calcMayne: Mayne, B. T., Leemaqz, S. Y., Smith, A. K., Breen, J., Roberts, C. T., & Bianco-Miotto, T. (2017). Accelerated placental aging in early onset preeclampsia pregnancies identified by DNA methylation. Epigenomics, 9(3), 279–289. https://doi.org/10.2217/epi-2016-0103
• calcPEDBE: McEwen, L. M., O’Donnell, K. J., McGill, M. G., Edgar, R. D., Jones, M. J., MacIsaac, J. L., … Kobor, M. S. (2020). The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. PNAS, 117(38), 23329–23335. https://doi.org/10.1073/pnas.1820843116

• calcDunedinPoAm38: Belsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., … Moffitt, T. E. (2020). Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. ELife, 9, e54870. https://doi.org/10.7554/eLife.54870
• dunedinPACE (DunedinPACE::PoAmProjector()) Belsky, D. W., Caspi, A., Corcoran, D. L., Sugden, K., Poulton, R., Arseneault, L., … Moffitt, T. E. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. ELife, 11, e73420. https://doi.org/10.7554/eLife.73420
• calcPhenoAge: Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., … Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414
• prcPhenoAge::calcprcPhenoAge() / calcnonprcPhenoAge() Add prcPhenoAge manuscript citation
• calcHRSInChPhenoAge: [cite PC Clocks paper here]
• GrimAge: Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., … Horvath, S. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging, 11(2), 303–327. https://doi.org/10.18632/aging.101684

• McCartney, D. L., Hillary, R. F., Stevenson, A. J., Ritchie, S. J., Walker, R. M., Zhang, Q., … Marioni, R. E. (2018). Epigenetic prediction of complex traits and death. Genome Biology, 19(1), 136. https://doi.org/10.1186/s13059-018-1514-1
  • calcAlcoholMcCartney
  • calcBMIMcCartney
  • calcSmokingMcCartney
• calcPEth: Liang, X., Justice, A. C., So-Armah, K., Krystal, J. H., Sinha, R., & Xu, K. (2021). DNA methylation signature on phosphatidylethanol, not on self-reported alcohol consumption, predicts hazardous alcohol consumption in two distinct populations. Molecular Psychiatry, 26(6), 2238–2253. https://doi.org/10.1038/s41380-020-0668-x

• calcDNAmClockCortical: Shireby, G. L., Davies, J. P., Francis, P. T., Burrage, J., Walker, E. M., Neilson, G. W. A., … Mill, J. (2020). Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex. Brain, 1–13. https://doi.org/10.1093/brain/awaa334
• calcPEDBE: McEwen, L. M., O’Donnell, K. J., McGill, M. G., Edgar, R. D., Jones, M. J., MacIsaac, J. L., … Kobor, M. S. (2020). The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. Proceedings of the National Academy of Sciences of the United States of America, 117(38), 23329–23335. https://doi.org/10.1073/pnas.1820843116
• calcHorvath2: Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., … Raj, K. (2018). Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging, 10(7), 1758–1775. https://doi.org/10.18632/aging.101508