diff_dss_test: Calculates differential methylation statistics under general...
In methylSig: MethylSig: Differential Methylation Testing for WGBS and RRBS Data

Description Usage Arguments Value Examples

This function is a wrapper for DSS::DMLtest.multiFactor with the added feature of reporting methylation rates alongside the test results via the methylation_group_column and methylation_groups parameters. See documentation below.

diff_dss_test(
  bs,
  diff_fit,
  contrast,
  methylation_group_column = NA,
  methylation_groups = NA
)

`bs`	a `BSseq`, the same used used to create `diff_fit`.
`diff_fit`	a `list` object output by `diff_dss_fit()`.
`contrast`	a contrast matrix for hypothesis testing. The number of rows should match the number of columns `design`. Consult `diff_fit$X` to ensure the contrast correponds to the intended test.
`methylation_group_column`	Optionally, a column from `diff_fit$design` by which to group samples and capture methylation rates. This column can be a `character`, `factor`, or `numeric`. In the case of `numeric` the samples are grouped according to the top and bottom 25 percentiles of the covariate, and the mean methlyation for each group is calculated. If not a `numeric`, use the `methylation_groups` parameter to specify case and control.
`methylation_groups`	Optionally, a named `character` vector indicating the `case` and `control` factors of `methylation_group_column` by which to group samples and capture methylation rates. If specified, must also specify `methylation_group_column`.

A GRanges object containing the following mcols:

stat:: The test statistic.
pvalue:: The p-value.
fdr:: The Benjamini-Hochberg adjusted p-values using p.adjust(method = 'BH').

If methylation_group_column is specified, also the following mcols:

meth_case:: Methylation estimate for case.
meth_control:: Methylation estimate for control.
meth_diff:: The difference meth_case - meth_control.
direction:: The group for which the locus is hyper-methylated. Note, this is not subject to significance thresholds.

data(BS.cancer.ex, package = 'bsseqData')

bs = filter_loci_by_group_coverage(
    bs = BS.cancer.ex,
    group_column = 'Type',
    c('cancer' = 2, 'normal' = 2))

small_test = bs[1:50]

diff_fit = diff_dss_fit(
    bs = small_test,
    design = bsseq::pData(bs),
    formula = '~ Type')

result = diff_dss_test(
    bs = small_test,
    diff_fit = diff_fit,
    contrast = matrix(c(0,1), ncol = 1)
)

result_with_meth = diff_dss_test(
    bs = small_test,
    diff_fit = diff_fit,
    contrast = matrix(c(0,1), ncol = 1),
    methylation_group_column = 'Type',
    methylation_groups = c('case' = 'cancer', 'control' = 'normal')
)