BMIQcalibration {MEAT} | R Documentation |
BMIQcalibration
uses an adapted version of the BMIQ algorithm to
calibrate the beta-matrix stored in the input SummarizedExperiment object
SE
to the gold standard dataset used in the muscle clock (GSE50498).
BMIQcalibration(SE, version = "MEAT2.0")
SE |
A |
version |
A character specifying which version of the epigenetic clock
you would like to use. Dy default, |
BMIQcalibration
was created by Steve Horvath,
largely based on the BMIQ
function from
Teschendorff (2013) to adjust for the type-2 bias in Illumina HM450
and HMEPIC arrays. BMIQ stands for beta mixture quantile normalization.
Horvath fixed minor errors in the v_1.2 version of the BMIQ algorithm
and changed the optimization algorithm to make the code more robust.
He used method = "Nelder-Mead" in optim
since
the other optimization method sometimes gets stuck. Toward this end,
the function blc
was replaced by blc2
.
SE
needs to be a SummarizedExperiment object containing a matrix of
beta-values that has been cleaned using clean_beta
.
Each sample in SE
is iteratively calibrated to the
gold standard values, so the time it takes to run
BMIQcalibration
is directly proportional to the number
of samples in SE
. This step is essential to estimate
epigenetic age with accuracy.
A calibrated version of the input SE
calibrated to the gold
standard dataset GSE50498.
clean_beta
to get the DNA methylation matrix ready
for calibration,
BMIQ
for the original BMIQ algorithm and
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r115
for the original paper describing Horvath's adapted BMIQ algorithm, and
SummarizedExperiment-class
for more
details on how to create and manipulate SummarizedExperiment objects.
# 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")