diff_binomial {methylSig} | R Documentation |
This function calculates differential methylation statistics using a binomial-based approach. See ‘Warning’ message below.
diff_binomial(bs, group_column, comparison_groups)
bs |
A |
group_column |
a |
comparison_groups |
a named |
This function uses a binomial-based model to calculate differential methylation statistics. It is nearly identical to the methylKit::calculateDiffMeth
function in the methylKit
R package except that only the likelihood ratio test and p.adjust(..., method='BH')
are used to calculate significance levels. It is significantly faster than methylKit::calculateDiffMeth
function.
A GRanges
object containing the following mcols
:
Methylation estimate for case.
Methylation estimate for control.
The difference meth_case - meth_control
.
The group for which the lcous is hyper-methylated. Note, this is not subject to significance thresholds.
The p-value from the t-test (t_approx = TRUE
) or the Chi-Square test (t_approx = FALSE
).
The Benjamini-Hochberg adjusted p-values using p.adjust(method = 'BH')
.
The log likelihood ratio.
This function does not take into account the variability among samples in each group being compared.
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_gr = diff_binomial( bs = small_test, group_column = 'Type', comparison_groups = c('case' = 'cancer', 'control' = 'normal'))