epiage_estimation {MEAT} | R Documentation |
epiage_estimation
takes as input a
SummarizedExperiment-class
object whose
assays contain a beta-matrix called "beta". This beta-matrix should contain
DNA methylation profiles in skeletal muscle that have been cleaned with
clean_beta
and calibrated with BMIQcalibration
.
epiage_estimation
will use the muscle clock to estimate epigenetic age
in each sample.
epiage_estimation(SE = NULL, version = "MEAT2.0", age_col_name = NULL)
SE |
A |
version |
A character specifying which version of the epigenetic clock
you would like to use. Dy default, |
age_col_name |
The name of the column in colData from |
epiage_estimation
estimates epigenetic age for each sample in the
input SE
based on DNA methylation profiles. SE
needs to be a
SummarizedExperiment-class
object containing
a matrix of beta-values called "beta" in assays. Beta must have been
calibrated to the gold standard GSE50498 using BMIQcalibration
to obtain good estimates of epigenetic age.
A SummarizedExperiment-class
object
identical to the input SE
, with components added to colData. If no
phenotypes were provided in the colData of the input SE
,
epiage_estimation
will put in colData a tibble containing a single
column called "DNAmage", corresponding to epigenetic age (in years) for each
sample. If phenotypes were provided in the colData of the input SE
,
epiage_estimation
will add to the existing colData three columns:
DNAmage
epigenetic age (in years)
AAdiff
the difference between predicted and actual age
(in years).
AAresid
the residuals of a linear model
(using lm
) of DNAmage against actual age.
AAresid
is only returned if the number of samples is > 2, as
AAresid
cannot be calculated with < 2 samples.
BMIQ
for the original BMIQ algorithm,
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r115
for the adapted version of the BMIQ algorithm, and
https://onlinelibrary.wiley.com/doi/full/10.1002/jcsm.12556
for the elastic net model of the muscle clock.
# Load matrix of beta-values of two individuals from dataset GSE121961 data("GSE121961", envir = environment()) # Load phenotypes of the two individuals from dataset GSE121961 data("GSE121961_pheno", envir = environment()) # Create a SummarizedExperiment object to coordinate phenotypes and # methylation into one object. library(SummarizedExperiment) GSE121961_SE <- SummarizedExperiment(assays=list(beta=GSE121961), colData=GSE121961_pheno) # Run clean_beta() to clean the beta-matrix GSE121961_SE_clean <- clean_beta(SE = GSE121961_SE, version = "MEAT2.0") # Run BMIQcalibration() to calibrate the clean beta-matrix GSE121961_SE_calibrated <- BMIQcalibration(SE = GSE121961_SE_clean, version = "MEAT2.0") # Run epiage_estimation() to obtain DNAmage + optionally AAdiff and AAresid GSE121961_SE_epiage <- epiage_estimation(SE = GSE121961_SE_calibrated, version = "MEAT2.0", age_col_name = "Age") colData(GSE121961_SE_epiage)