DML {sesame} | R Documentation

## Test differential methylation on each locus

### Description

The function takes a beta value matrix with probes on the rows and samples on the columns. It also takes a sample information data frame (sample.data) and formula for testing. The function outputs a list of coefficient tables for each factor tested.

### Usage

    DML(
      betas,
      sample.data,
      formula,
      se.lb = 0.06,
      balanced = FALSE,
      cf.test = NULL
    )

### Arguments

`betas` | beta values
`sample.data` | data frame for sample information, column names are predictor variables (e.g., sex, age, treatment, tumor/normal etc) and are referenced in formula. Rows are samples.
`formula` | formula
`se.lb` | lower bound to standard error of slope, lower this to get more difference of small effect size.
`balanced` | whether design is balanced or not. default to FALSE, when unbalanced will use Welch's method to estimate standard error. balance=TRUE is faster.
`cf.test` | factors to test (default to all factors in formula except intercept). Use "all" for all factors.

### Value

cf - a list of coefficient tables for each factor

### Examples

    data <- sesameDataGet('HM450.76.TCGA.matched')
    cf <- DML(data$betas, data$sampleInfo, ~type)

[Package sesame version 1.6.0 Index]