BMIQcalibration {MEAT} | R Documentation

## Calibrate methylation data to a gold standard.

### Description

`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).

### Usage

    BMIQcalibration(SE, version = "MEAT2.0")

### Arguments

`SE` | A `SummarizedExperiment-class` object. The "assays" component of `SE` should contain a beta-matrix of DNA methylation beta-values called "beta" that has been cleaned with `clean_beta`. `SE` may optionally contain annotation information on the CpGs stored in "rowData" and sample phenotypes stored in "colData".
`version` | A character specifying which version of the epigenetic clock you would like to use. Dy default, `version` is set to "MEAT2.0" for the second version of the epigenetic clock. If you would like to use the original version, set `version` to "MEAT".

### Details

`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.

### Value

A calibrated version of the input `SE` calibrated to the gold standard dataset GSE50498.

### See Also

`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.

### Examples

    # 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")

[Package MEAT version 1.0.4 Index]