To use methylclock under R<4.1 you need to install the package under main branch or release <= 0.99.0 (https://github.com/isglobal-brge/methylclock/releases/tag/v0.7.7)
To install methylclock to be used with R < 4.1 :
library(devtools)
install_github("isglobal-brge/methylclock@main")
Latest version is available in Bioconductor development release 3.14, to install the Bioconductor package R >= 4.1 is required :
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("methylclock")
Source code for Bioconductor release can be found under master branch.
https://github.com/isglobal-brge/methylclock/tree/master
This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. The package includes the following estimators:
- Horvath's clock: It uses 353 CpGs described in @horvath2013dna. It was trained using 27K and 450K arrays in samples from different tissues. Other three different age-related biomarkers are also computed:
- AgeAcDiff (DNAmAge acceleration difference): Difference between DNAmAge and chronological age.
- IEAA (Intrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age adjusted by cell counts.
- EEAA (Extrinsic Epigenetic Age Acceleration): Residuals obtained after regressing DNAmAge and chronological age. This measure was also known as DNAmAge acceleration residual in the first Horvath's paper.
- Hannum's clock: It uses 71 CpGs described in @hannum2013genome. It was trained using 450K array in blood samples. Another are-related biomarer is also computed:
- AMAR (Apparent Methylomic Aging Rate): Measure proposed in @hannum2013genome computed as the ratio between DNAm age and the chronological age.
- BNN: It uses Horvath's CpGs to train a Bayesian Neural Network (BNN) to predict DNAm age as described in @alfonso2018.
- Horvath's skin+blood clock (Horvath2): Epigenetic clock for skin and blood cells. It uses 391 CpGs described in @horvath2018epigenetic. It was trained using 450K EPIC arrays in skin and blood sampels.
- PedBE clock: Epigenetic clock from buccal epithelial swabs. It's intended purpose is buccal samples from individuals aged 0-20 years old. It uses 84 CpGs described in @mcewen2019pedbe. The authors gathered 1,721 genome-wide DNAm profiles from 11 different cohorts with individuals aged 0 to 20 years old.
- Wu's clock: It uses 111 CpGs described in @wu2019dna. It is designed to predict age in children. It was trained using 27K and 450K.
- Knight's clock: It uses 148 CpGs described in @knight2016epigenetic. It was trained using 27K and 450K arrays in cord blood samples.
- Bohlin's clock: It uses 96 CpGs described in @bohlin2016prediction. It was trained using 450K array in cord blood samples.
- Mayne's clock: It uses 62 CpGs described in @mayne2017accelerated. It was trained using 27K and 450K.
- Lee's clocks: Three different biological clocks described in @lee2019placental are implemented. It was trained for 450K and EPIC arrays in placenta samples.
- RPC clock: Robust placental clock (RPC). It uses 558 CpG sites.
- CPC clock: Control placental clock (CPC). It usses 546 CpG sites.
- Refined RPC clock: Useful for uncomplicated term pregnancies (e.g. gestational age >36 weeks). It uses 396 CpG sites.
The biological DNAm clocks implemented in this package are:
- Levine's clock (also know as PhenoAge): It uses 513 CpGs described in @levine2018epigenetic. It was trained using 27K, 450K and EPIC arrays in blood samples.
- Telomere Length's clock (TL): It uses 140 CpGs described in @lu2019dna It was trained using 450K and EPIC arrays in blood samples.
[1] Dolors Pelegri-Siso, Paula de Prado, Justiina Ronkainen, Mariona Bustamante, Juan R Gonzalez, methylclock: a Bioconductor package to estimate DNA methylation age, Bioinformatics, Volume 37, Issue 12, 15 June 2021, Pages 1759–1760, doi: 10.1093/bioinformatics/btaa825. PMID: 32960939.