Description Usage Arguments Details Value Author(s) References See Also Examples
These functions calculate posterior probabilities for each of the rows in a ‘countData’ object belonging to each of the models specified in the ‘groups’ slot.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  getLikelihoods.NB(cD, prs, pET = "BIC", marginalise = FALSE, subset = NULL,
priorSubset = NULL, bootStraps = 1, conv = 1e4, nullData = FALSE,
returnAll = FALSE, returnPD = FALSE, verbose = TRUE, discardSampling =
FALSE, cl, ...)
getLikelihoods.BB(cD, prs, pET = "BIC", marginalise = FALSE, subset =
NULL, priorSubset = NULL, bootStraps = 1, conv = 1e04, nullData = FALSE,
returnAll = FALSE, returnPD = FALSE, verbose = TRUE, discardSampling =
FALSE, cl, ...)
getLikelihoods(cD, prs, pET = "BIC", marginalise = FALSE, subset = NULL,
priorSubset = NULL, bootStraps = 1, bsNullOnly = TRUE, conv = 1e4, nullData = FALSE,
weightByLocLikelihoods = TRUE, modelPriorSets = list(),
modelPriorValues = list(), returnAll = FALSE, returnPD = FALSE, verbose
= TRUE, discardSampling = FALSE, modelLikes = TRUE, cl = NULL, tempFile
= NULL, largeness = 1e+08)

cD 
An object of type 
prs 
(Initial) prior probabilities for each of the groups in the ‘cD’ object. Should sum to 1, unless nullData is TRUE, in which case it should sum to less than 1. 
pET 
What type of prior reestimation should be attempted? Defaults to "BIC"; "none" and "iteratively" are also available. 
marginalise 
Should an attempt be made to numerically marginalise over a prior distribution iteratively estimated from the posterior distribution? Defaults to FALSE, as in general offers little performance gain and increases computational cost considerably. 
subset 
Numeric vector giving the subset of counts for which posterior likelihoods should be estimated. 
priorSubset 
Numeric vector giving the subset of counts which may be used to estimate prior probabilities on each of the groups. See Details. 
bootStraps 
How many iterations of bootstrapping should be used in the (re)estimation of priors in the negative binomial method. 
bsNullOnly 
If TRUE (default, bootstrap hyperparameters based on the likelihood of the null model and its inverse only; otherwise, on the likelihood of all models. 
conv 
If not null, bootstrapping iterations will cease if the mean squared difference between posterior likelihoods of consecutive bootstraps drops below this value. 
nullData 
If TRUE, looks for segments or counts with no true expression. See Details. 
weightByLocLikelihoods 
If a locLikelihoods slot is present in the ‘cD’ object, and nullData = TRUE, then the initial weighting on nulls will be determined from the locLikelihoods slot. Defaults to TRUE. 
modelPriorSets 
If given, a list object, which defines subsets of the data for which different priors on the different models might be expected. See Details. 
modelPriorValues 
If given, a list object which defines priors on the different models. See Details. 
returnAll 
If TRUE, and bootStraps > 1, then instead of returning a single countData object, the function returns a list of countData objects; one for each bootstrap. Largely used for debugging purposes. 
returnPD 
If TRUE, then the function returns the (log) likelihoods of the data given the models, rather than the posterior (log) likelihoods of the models given the data. Not recommended for general use. 
verbose 
Should status messages be displayed? Defaults to TRUE. 
discardSampling 
If TRUE, discards information about which data rows are sampled to generate prior information. May slightly degrade the results but reduce computational time required. Defaults to FALSE. 
modelLikes 
If TRUE (default), returns likelihoods for each model. If FALSE, returns likelihoods for each hyperparameter, from which the posterior joint distribution on hyperparameters can be inferred. 
cl 
A SNOW cluster object. 
tempFile 
Temporary file prefix for saving data likelihoods. Primarily for debugging purposes at this stage. Defaults to NULL, in which case no temporary data are saved. 
largeness 
The maximum size over which data likelihoods are calculated. Objects larger than this are split. This is most useful in combination with the saving of temporary files in the case of excessively large analyses. 
... 
Any additional information to be passed to the

These functions estimate, under the assumption of various
distributions, the (log) posterior likelihoods that each count belongs
to a group defined by the @group
slot of the input
object. The posterior likelihoods are stored on the natural log scale
in the @posteriors
slot of the countData
object
generated by this function. This is because the posterior likelihoods
are calculated in this form, and ordering of the counts is better done
on these loglikelihoods than on the likelihoods.
If 'pET = "none"'
then no attempt is made to reestimate the
prior likelihoods given in the 'prs'
variable. However, if
'pET = "BIC"'
, then the function will attempt to estimate the
prior likelihoods by using the Bayesian Information Criterion to
identify the proportion of the data best explained by each
model and taking these proportions as prior. Alternatively, an
iterative reestimation of priors is possible ('pET = "iteratively"'
),
in which an inital estimate for the prior likelihoods of the models is
used to calculated the posteriors and then the priors are updated by
taking the mean of the posterior likelihoods for each model across all
data. This often works well, particularly if the 'BIC' method is used
(see Hardcastle & Kelly 2010 for details). However, if the data are
sufficiently nonindependent, this approach may substantially
misestimate the true priors. If it is possible to select a
representative subset of the data by setting the variable
'subsetPriors'
that is sufficiently independent, then better
estimates may be acquired.
In certain circumstances, it may be expected that certain subsets of the data are likely to behave differently to others; for example, if a set of genes are expected in advance to be differentially expressed, while the majority of the data are not. In this case, it may be advantageous (in terms of improving false discovery rates) to specify these different subsets in the modelPriorSets variable. However, care should be taken downstream to avoid confirmation bias.
Filtering the data may be extremely advantageous in reducing run time. This can be done by passing a numeric vector to 'subset' defining a subset of the data for which posterior likelihoods are required.
See Hardcastle & Kelly (2010) for a definition of the negative binomial methods.
A 'cluster' object is strongly recommended in order to parallelise
the estimation of posterior likelihoods, particularly for the
negative binomial method. However, passing NULL to the cl
variable will allow the functions to run in nonparallel mode.
The ‘getLikelihoods.NB’ and ‘getLikelihoods.BB’ functions are now deprecated and will soon be removed.
A countData
object.
Thomas J. Hardcastle
Hardcastle T.J., and Kelly, K. baySeq: Empirical Bayesian Methods For Identifying Differential Expression In Sequence Count Data. BMC Bioinformatics (2010)
countData
, getPriors
,
topCounts
, getTPs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  # See vignette for more examples.
# If we do not wish to parallelise the functions we set the cluster
# object to NULL.
cl < NULL
# Alternatively, if we have the 'snow' package installed we
# can parallelise the functions. This will usually (not always) offer
# significant performance gain.
## Not run: try(library(snow))
## Not run: try(cl < makeCluster(4, "SOCK"))
# 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)
# set negative binomial density function
densityFunction(CD) < nbinomDensity
#estimate library sizes for countData object
libsizes(CD) < getLibsizes(CD)
# Get priors for negative binomial method
## Not run: CDPriors < getPriors(CD, samplesize = 10^5, estimation = "QL", cl = cl)
# To speed up the processing of this example, we have already created
# the `CDPriors' object.
data(CDPriors)
# Get likelihoods for data with negative binomial method.
CDPost < getLikelihoods(CDPriors, pET = "BIC", cl = cl)
try(stopCluster(cl))

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