get_tstats {DAMEfinder} | R Documentation

## Get t-Statistics

### Description

This function calculates a moderated t-Statistic per site or tuple using `limma`'s `lmFit` and `eBayes` functions. It then smoothes the obtained t-Statistics using `bumphunter`'s `smoother` function.

### Usage

    get_tstats(
      sa,
      design,
      contrast = NULL,
      method = "ls",
      trend = FALSE,
      smooth = FALSE,
      maxGap = 20,
      coef = 2,
      verbose = TRUE,
      filter = TRUE,
      ...
    )

### Arguments

`sa` | A SummarizedExperiment containing ASM values where each row and column correspond to a tuple/site and sample respectively.
`design` | a design matrix created with `model.matrix`.
`contrast` | a contrast matrix, generated with `makeContrasts`.
`method` | The method to be used in limma's `lmFit`. The default is set to 'ls' but can also be set to 'robust', which is recommended on a real data set.
`trend` | Passed to `eBayes`. Should an intensity-trend be allowed for the prior variance? Default is that the prior variance is constant, e.g. FALSE.
`smooth` | Whether smoothing should be applied to the t-Statistics. Default = FALSE. If TRUE, wherever smoothing is not possible, the un-smoothed t-stat is used instead.
`maxGap` | The maximum allowed gap between genomic positions for clustering of genomic regions to be used in smoothing. Default = 20.
`coef` | Column in model.matrix specifying the parameter to estimate. Default = 2. If `contrast` specified, column with contrast of interest.
`verbose` | Set verbose. Default = TRUE.
`filter` | Remove empty tstats. Default = TRUE.
`...` | Arguments passed to `loessByCluster`. Only used if `smooth` = TRUE.

### Details

The smoothing is done on genomic clusters consisting of CpGs that are close to each other. In the case of tuples, the midpoint of the two genomic positions in each tuple is used as the genomic position of that tuple, to perform the smoothing.The function takes a `RangedSummarizedExperiment` generated by `calc_derivedasm` or `calc_asm` containing ASM across samples, and the index of control and treatment samples.

### Value

A vector of t-Statistics within the `RangedSummarizedExperiment`.

### Examples

    data(readtuples_output)
    ASM <- calc_asm(readtuples_output)
    grp <- factor(c(rep('CRC',3),rep('NORM',2)), levels = c('NORM', 'CRC'))
    mod <- model.matrix(~grp)
    tstats <- get_tstats(ASM, mod)

[Package DAMEfinder version 1.6.0 Index]