clean_beta {MEAT} | R Documentation

## Clean beta-matrix.

### Description

`clean_beta` reduces the beta-matrix stored in the input SummarizedExperiment object `SE` to the right CpGs, imputes missing values if any, and replaces 0 and 1 with min and max values.

### Usage

    clean_beta(SE = NULL)

### Arguments

`SE` | A `SummarizedExperiment-class` object. The "assays" component of `SE` should contain a beta-matrix of DNA methylation beta-values called "beta", with samples in columns and CpGs in rows. `SE` may optionally contain annotation information on the CpGs stored in "rowData" and sample phenotypes stored in "colData".

### Details

`clean_beta` will transform the the beta-matrix stored in `SE` by: 1) reducing it to the 19,401 CpGs used to calibrate DNA methylation profiles to the gold standard 2) checking whether it contains missing values, and impute them with `impute.knn`, 3) check whether it contains 0 and 1 values, and if any, change them to the minimum non-0 and maximum non-1 values in the beta-matrix.

### Value

A clean version of the input `SE` reduced to 19,401 CpGs, with missing values imputed, and without 0 or 1 values.

### See Also

`impute.knn` for imputation of missing values, 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)

[Package MEAT version 0.99.6 Index]