baySeq-classes: baySeq - classes

In baySeq: Empirical Bayesian analysis of patterns of differential expression in count data

Description

The countData class is used to define summaries of count data and establishing prior and posterior parameters on distributions defined upon the count data.

Slots

Objects of these class contain the following components:

data:	Count data (matrix).
replicates:	The replicate structure of the data. Stored as a factor, but can be given in any form.
groups:	Group (model) structure to test on the data (list).
annotation:	Annotation data for each count (data.frame).
priorType:	Character string describing the type of prior information available in slot 'priors'.
priors:	Prior parameter information. Calculated by the functions described in getPriors.
posteriors:	Estimated (log)-posterior likelihoods for each group (matrix). Calculated by the functions described in getLikelihoods.
estProps:	Estimated proportion of tags belonging to each group (numeric). Calculated by the functions described in getLikelihoods.
nullPosts:	If calculated, the posterior likelihoods for the data having no true expression of any kind.
seglens:	Lengths of segments containing the counts described in data. A matrix, but may be initialised with a vector, or ignored altogether. See Details.

Details

The seglens slot describes, for each row of the data object, the length of the 'segment' that contains the number of counts described by that row. For example, if we are looking at the number of hits matching genes, the seglens object would consist of transcript lengths. Exceptionally, we may want to use different segment lengths for different samples and so the slot takes the form of a matrix. If the matrix has only one column, it is duplicated for all samples. Otherwise, it should have the same number of columns as the '@data' slot. If the slot is the empty matrix, then it is assumed that all segments have the same length.

Methods

The standard methods 'new', 'dim', '[', 'show', 'rbind' and 'c' have been defined for these classes. The methods 'groups', 'groups<-', 'replicates', 'replicates<-', 'libsizes' and 'libsizes<-' have also been defined in order to access and modify these slots, and their use is recommended. The method 'flatten' can be used to produce a data.frame object containing much of the basic data in a member of this class.

Author(s)

Thomas J. Hardcastle

Examples

#load test data
data(simData)

# Create a 'countData' object from test data.
replicates <- c("simA", "simA", "simA", "simA", "simA", "simB", "simB", "simB", "simB", "simB")
groups <- list(NDE = c(1,1,1,1,1,1,1,1,1,1), DE = c(1,1,1,1,1,2,2,2,2,2))
CD <- new("countData", data = simData, replicates = replicates, groups = groups)

#estimate library sizes for countData object
libsizes(CD) <- getLibsizes(CD)

CD[1:10,]
dim(CD)

baySeq documentation built on Nov. 8, 2020, 5:43 p.m.