Description Usage Arguments Details Value See Also
Maximize the posterior distribution of pepBayes models. The generic function has methods for peptideSet objects and for pepBayesFit objects for restarting an optimization from its last position.
1 2 3 4 5 6 7 8 9 10 11 | pepBayesEcm(x, ...)
## S4 method for signature 'peptideSet'
pepBayesEcm(x, position_data = NULL,
control_id = NULL, n_iter = 30, n_threads = 1, n_rule = 10,
n_isamp = 20, prior_control_list = NULL, iter_control_list = NULL,
schedule_iterations = TRUE)
## S4 method for signature 'pepBayesFit'
pepBayesEcm(x, n_iter, n_threads = 1, n_rule = 10,
n_isamp = 20, schedule_iterations = FALSE, join_output = TRUE)
|
x |
A pepBayesFit or peptideSet object containing experiment data. |
... |
Additional arguments for sampling control. |
position_data |
A data.frame or GRanges object with peptide position information. |
n_iter |
The number of EM iterations to perform. |
n_threads |
The number of parallel threads to use in the E-step. |
n_rule |
The number of nodes to use for Gauss-Hermite numerical integration. |
n_isamp |
The number of importance samples to estimate the E-step. |
prior_control_list |
A named list controlling initial parameter values. |
iter_control_list |
A named list controlling which parameters are updated. |
schedule_iterations |
A boolean. Should the number of importance samples
gradually increase to |
join_output |
A boolean. Should input be merged with output? |
PepBayes estimates the posterior probability of
a differential antibody response against each peptide in a peptide microarray
assay, for each subject,
when compared with control samples. Two common experimental designs are automatically
detected. In an unpaired design, differences between a population of interest and
a control population are detected. A paired design has matched samples for each subject
before and after a treatment is applied. The function pepBayesEcm
automatically detects the experimental design from the data.
When x
is a peptideSet
object, the paired or unpaired model is
chosen depending on the 'ptid' column of pData(x)
. Paired data is detected
when each unique value of 'ptid' appears exactly twice among all slides.
The position_data
argument supplies information about peptide position
information. This argument may be a 'data.frame' with columns 'start',
'end' or 'width', and 'peptide'. If a 'GRanges' object, then it must have either peptide
as a name or have peptide as a metadata column. Response probability hyperparamters are
fixed for EM estimation, but if position data are omitted (position_data = NULL
),
they will also be fixed if the resulting fitted object is used to initialize a
pepBayesMcmc
.
When x
is a pepBayesFit object, the optimization is started from the most
recent parameter values in the fitted object. In the event that x
is
the output of a previous ECM fit, the argument join_output
becomes
relevant. If TRUE
, parameter traces are appended to those in x
.
An object of type pepBayesFit containing sorted slide metadata, parameter trace, posterior probabilities of binding, and iteration information.
pepBayesFit
pepBayesFit-methods
peptideSet
pepBayesMcmc
pepBayesCalls
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