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DNA Methylation

Source: vignettes/omics-methylation.Rmd
omics-methylation.Rmd

This vignette demonstrates DNA methylation preprocessing using the Rtoolset::beta2M() helper.

Overview

DNA methylation data is often represented as beta values between 0 and 1, while many statistical analyses work better with M values. The beta2M() function provides a simple way to convert beta values to M values while handling edge cases and preserving the input structure.

Beta to M Value Conversion

Basic Usage 

library(Rtoolset)

beta2M(0.5)
#> [1] 0

beta_values <- c(0.1, 0.5, 0.9, 0.01, 0.99)
M_values <- beta2M(beta_values)
M_values
#> [1] -3.169912  0.000000  3.169912 -6.629214  6.629214

Matrix or Data Frame Input 

beta_matrix <- matrix(
  runif(100, 0, 1),
  nrow = 10,
  ncol = 10
)
M_matrix <- beta2M(beta_matrix)

beta_df <- data.frame(
  Sample1 = runif(10, 0, 1),
  Sample2 = runif(10, 0, 1),
  Sample3 = runif(10, 0, 1)
)
M_df <- beta2M(beta_df)

Custom alpha Parameter

The alpha parameter prevents log(0) when beta values are exactly 0 or 1:

beta2M(c(0, 0.5, 1))
#> [1] -19.93157   0.00000  19.93157
beta2M(c(0, 0.5, 1), alpha = 1e-8)
#> [1] -26.57542   0.00000  26.57542

Why Convert to M Values?

M values are often preferred for modeling because they:

  1. are closer to a normal distribution
  2. have more stable variance across the range
  3. work better with linear modeling frameworks

Example Workflow 

library(Rtoolset)

beta_data <- read.csv("methylation_beta_values.csv", row.names = 1)
M_data <- beta2M(beta_data)

Downstream Analysis

Differential Methylation with limma 

library(limma)

M_data <- beta2M(beta_data)

design <- model.matrix(~ group)
fit <- lmFit(M_data, design)
fit <- eBayes(fit)
top_sites <- topTable(fit, coef = 2, number = 100)

Quality Check for Extreme Beta Values 

extreme_beta <- sum(beta_data == 0 | beta_data == 1, na.rm = TRUE)
cat("Number of extreme beta values:", extreme_beta, "\n")

M_data <- beta2M(beta_data, alpha = 1e-6)

Conversion Formula

The beta-to-M transformation is:

M = \log_2\left(\frac{\beta + \alpha}{1 - \beta + \alpha}\right)

where:

  • beta is the methylation beta value
  • alpha is a small constant added for numerical stability
  • M is the transformed M value

Best Practices

  1. Check for extreme values before conversion
  2. Use the default alpha unless you have a specific reason to change it
  3. Record the alpha value used for reproducibility
  4. Convert to M values before fitting linear models

See Also