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
epiage_estimation will use the muscle clock to estimate epigenetic age
in each sample.
A character specifying which version of the epigenetic clock
you would like to use. Dy default,
The name of the column in colData from
epiage_estimation estimates epigenetic age for each sample in the
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
to obtain good estimates of epigenetic age.
identical to the input
SE, with components added to colData. If no
phenotypes were provided in the colData of the input
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
epiage_estimation will add to the existing colData three columns:
DNAmage epigenetic age (in years)
AAdiff the difference between predicted and actual age
AAresid the residuals of a linear model
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,
for the adapted version of the BMIQ algorithm, and
for the elastic net model of the muscle clock.
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# 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)
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