This function populates the ‘@groups’ slot of the supplied countData object with all possible models for equivalence/non-equivalence of expression between replicate groups.
The countData class is used to define summaries of count data and establishing prior and posterior parameters on distributions defined upon the count data.
baySeq-package
(Package: baySeq) :
Empirical Bayesian analysis of patterns of differential expression in count data.
This package is intended to identify differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines. We achieve this by empirical bayesian methods, first bootstrapping to estimate prior parameters from the data and then assessing posterior likelihoods of the models proposed.
bimodalSeparator
(Package: baySeq) :
A function that, given a numeric vector, finds the value which
This function takes a numeric vector and finds the value which splits the data into two sets of minimal total variance, weighted by the size of subsets (Otsu's method). It is principally intended to be a quick and easy way of separating bimodally distributed data.
This function fills the '@densityFunction' slot of a ‘countData’ object. It defines the distribution used to estimate posterior likelihoods, and associated values used in these calculations.
getLikelihoods
(Package: baySeq) :
Finds posterior likelihoods for each count or paired count as
These functions calculate posterior probabilities for each of the rows in either a ‘countData’ or ‘pairedData’ object belonging to each of the models specified in the ‘groups’ slot.
getPosteriors
(Package: baySeq) :
An internal function in the baySeq package for calculating
For likelihoods of the data given a set of models, this function calculates the posterior likelihoods of the models given the data. An internal function of baySeq, which should not in general be called by the user.
getPriors
(Package: baySeq) :
Estimates prior parameters for the underlying distributions of
These functions estimate, via maximum or quasi-likelihood methods, the parameters of the underlying distributions for negative binomial distributions on count data, or for beta-binomial distributions on paired count data.
providing 
.