diff_methylsig: Calculates differential methylation statistics using a...
In methylSig: MethylSig: Differential Methylation Testing for WGBS and RRBS Data

Description Usage Arguments Value Examples

The function calculates differential methylation statistics between two groups of samples using a beta-binomial approach to calculate differential methylation statistics, accounting for variation among samples within each group. The function can be applied to a BSseq object subjected to filter_loci_by_coverage(), filter_loci_by_snps(), filter_loci_by_group_coverage() or any combination thereof. Moreover, the function can be applied to a BSseq object which has been tiled with tile_by_regions() or tile_by_windows().

diff_methylsig(
  bs,
  group_column,
  comparison_groups,
  disp_groups,
  local_window_size = 0,
  local_weight_function,
  t_approx = TRUE,
  n_cores = 1
)

`bs`	a `BSseq` object.
`group_column`	a `character` string indicating the column of `pData(bs)` to use for determining group membership.
`comparison_groups`	a named `character` vector indicating the `case` and `control` factors of `group_column` for the comparison.
`disp_groups`	a named `logical` vector indicating the whether to use `case`, `control`, or both to estimate the dispersion.
`local_window_size`	an `integer` indicating the size of the window for use in determining local information to improve mean and dispersion parameter estimations. In addition to a the distance constraint, a maximum of 5 loci upstream and downstream of the locus are used. The default is `0`, indicating no local information is used.
`local_weight_function`	a weight kernel function. The default is the tri-weight kernel function defined as `function(u) = (1-u^2)^3`. The domain of any given weight function should be [-1,1], and the range should be [0,1].
`t_approx`	a `logical` value indicating whether to use squared t approximation for the likelihood ratio statistics. Chi-square approximation (`t_approx = FALSE`) is recommended when the sample size is large. Default is `TRUE`.
`n_cores`	an `integer` denoting how many cores should be used for differential methylation calculations.

A GRanges object containing 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.
pvalue:: The p-value from the t-test (t_approx = TRUE) or the Chi-Square test (t_approx = FALSE).
fdr:: The Benjamini-Hochberg adjusted p-values using p.adjust(method = 'BH').
disp_est:: The dispersion estimate.
log_lik_ratio:: The log likelihood ratio.
df:: Degrees of freedom used when t_approx = TRUE.

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[seq(50)]

diff_gr = diff_methylsig(
    bs = small_test,
    group_column = 'Type',
    comparison_groups = c('case' = 'cancer', 'control' = 'normal'),
    disp_groups = c('case' = TRUE, 'control' = TRUE),
    local_window_size = 0,
    t_approx = TRUE,
    n_cores = 1)