getLikelihoods: Finds posterior likelihoods for each count or paired count as...
In baySeq: Empirical Bayesian analysis of patterns of differential expression in count data

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.

getLikelihoods.NB(cD, prs, pET = "BIC", marginalise = FALSE, subset = NULL,
priorSubset = NULL, bootStraps = 1, conv = 1e-4, 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 = 1e-04, 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 = 1e-4, 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 `countData`, or descending from this class.
`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 re-estimation 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 hyper-parameters 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 hyper-parameter, from which the posterior joint distribution on hyper-parameters 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 `'getLikelihoods'` function by the now deprecated functions.

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 log-likelihoods than on the likelihoods.

If 'pET = "none"' then no attempt is made to re-estimate 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 re-estimation 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 non-independent, this approach may substantially mis-estimate 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 non-parallel 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

# 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))