This a function is addressed to estimate the posterior probabilities of methylation levels, assuming that the methylation levels follows Beta distribution and taking abventage that ehe Beta distribution is a conjugate prior for Binomial distribution.
beta_bin_meth(x, ...)
# S4 method for matrix_OR_data.frame
beta_bin_meth(
x,
init.pars = NULL,
via.optim = TRUE,
loss.fun = c("linear", "huber", "smooth", "cauchy", "arctg"),
verbose = TRUE,
...
)
# S4 method for GRanges
beta_bin_meth(
x,
init.pars = NULL,
via.optim = TRUE,
loss.fun = c("linear", "huber", "smooth", "cauchy", "arctg"),
verbose = TRUE,
...
)
# S4 method for GRangesList
beta_bin_meth(
x,
init.pars = NULL,
via.optim = TRUE,
loss.fun = c("linear", "huber", "smooth", "cauchy", "arctg"),
num.cores = multicoreWorkers(),
tasks = 0L,
verbose = TRUE,
...
)A GRanges-class carrying a matrix of
counts on its metacolumn, with the counts of methylated (mC) and
unmethylated (uC) cytosines for at least 10 or more cytosine sites.
Alternatively, it can be just the 'matrix' or 'data.frame' of counts.
initial parameter values. Defaults is NULL and an initial
guess is estimated using optim function. If the initial
guessing fails initial parameter values are to alpha = 1 &
beta = 1, which imply the parsimony pseudo-counts greater than zero.
Logical. Whether to estimate beta distribution parameters
via optim or nls.lm. If any of
this approaches fail then parameters used init.pars will be returned.
Loss function(s) used in the estimation of the best fitted model to beta distribution (only applied when Bayesian=TRUE; see (Loss function)). This fitting uses the approach followed in in the R package usefr. After \(z = 1/2 * sum((f(x) - y)^2)\) we have:
"linear": linear function which gives a standard least squares: \(loss(z) = z\).
"huber": Huber loss, \(loss(z) = ifelse(z \leq 1, z, sqrt(z) -1)\).
"smooth": Smooth approximation to the sum of residues absolute values: \(loss(z) = 2*(sqrt(z + 1) - 1)\).
"cauchy": Cauchy loss: \(loss(z) = log(z + 1)\).
"arctg": arc-tangent loss function: \(loss(x) = atan(z)\).
Loss 'linear' function works well for most of the methylation datasets with acceptable quality.