find_dames {DAMEfinder} | R Documentation

## Find DAMEs

### Description

This function finds Differential Allele-specific MEthylated regions (DAMEs). It uses the `regionFinder` function from `bumphunter`, and asigns p-values either empirically or using the Simes method.

### Usage

    find_dames(
      sa,
      design,
      coef = 2,
      contrast = NULL,
      smooth = TRUE,
      Q = 0.5,
      pvalAssign = "simes",
      maxGap = 20,
      verbose = TRUE,
      maxPerms = 10,
      method = "ls",
      trend = FALSE,
      ...
    )

### Arguments

`sa` | A `SummarizedExperiment` containing ASM values where each row correspond to a tuple/site and a column to sample/replicate.
`design` | A design matrix created with `model.matrix`.
`coef` | Column in `design` specifying the parameter to estimate. Default = 2.
`contrast` | a contrast matrix, generated with `makeContrasts`.
`smooth` | Whether smoothing should be applied to the t-Statistics. Default = TRUE.
`Q` | The percentile set to get a cutoff value K. K is the value on the Qth quantile of the absolute values of the given (smoothed) t-statistics. Only necessary if `pvalAssign` = 'empirical'. Default = 0.5.
`pvalAssign` | Choose method to assign pvalues, either 'simes' (default) or 'empirical'. This second one performs `maxPerms` number of permutations to calculate null statistics, and runs `regionFinder`.
`maxGap` | Maximum gap between CpGs in a cluster (in bp). NOTE: Regions can be as small as 1 bp. Default = 20.
`verbose` | If the function should be verbose. Default = TRUE.
`maxPerms` | Maximum possible permutations generated. Only necessary if `pvalAssign` = 'empirical'. Default = 10.
`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.
`...` | Arguments passed to `get_tstats`.

### Details

The simes method has higher power to detect DAMEs, but the consistency in signal across a region is better controlled with the empirical method, since it uses `regionFinder` and `getSegments` to find regions with t-statistics above a cuttof (controled with parameter `Q`), whereas with the 'simes' option, we initially detects clusters of CpG sites/tuples, and then test if at least 1 differential site/tuple is present in the cluster.

We recommend trying out different `maxGap` and `Q` parameters, since the size and the effect-size of obtained DAMEs change with these parameters.

### Value

A data frame of detected DAMEs ordered by the p-value. Each row is a DAME and the following information is provided in the columns (some column names change depending on the `pvalAssign` choice):

- chr: on which chromosome the DAME is found.

- start: The start position of the DAME.

- end: The end position of the DAME.

- pvalSimes: p-value calculated with the Simes method.

- pvalEmp: Empirical p-value obtained from permuting covariate of interest.

- sumTstat: Sum of t-stats per segment/cluster.

- meanTstat: Mean of t-stats per segment/cluster.

- segmentL: Size of segmented cluster (from `getSegments`).

- clusterL: Size of original cluster (from `clusterMaker`).

- FDR: Adjusted p-value using the method of Benjamini, Hochberg. (from `p.adjust`).

- numup: Number of sites with ASM increase in cluster (only for Simes).

- numdown: Number of sites with ASM decrease in cluster (only for Simes).

### 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)
    dames <- find_dames(ASM, mod, verbose = FALSE)

[Package DAMEfinder version 1.6.0 Index]