getter_setter_functions: Getter/Setter functions
In FRASER: Find RAre Splicing Events in RNA-Seq Data

Description Usage Arguments Value Functions Examples

This is a collection of small accessor/setter functions for easy access to the values within the FRASER model.

featureExclusionMask(fds, type = currentType(fds))

featureExclusionMask(fds, type = currentType(fds)) <- value

rho(fds, type = currentType(fds))

zScores(fds, type = currentType(fds), byGroup = FALSE, ...)

pVals(fds, type = currentType(fds), level = "site", dist = "BetaBinomial", ...)

padjVals(fds, type = currentType(fds), dist = c("BetaBinomial"), ...)

predictedMeans(fds, type = currentType(fds))

deltaPsiValue(fds, type = currentType(fds))

currentType(fds)

currentType(fds) <- value

pseudocount(value = NULL)

hyperParams(fds, type = currentType(fds), all = FALSE)

bestQ(fds, type = currentType(fds))

dontWriteHDF5(fds)

dontWriteHDF5(fds) <- value

verbose(fds)

verbose(fds) <- value

`fds`	An FraserDataSet object.
`type`	The type of psi (psi5, psi3 or theta)
`value`	The new value to be assigned.
`byGroup`	If TRUE, aggregation by donor/acceptor site will be done.
`...`	Internally used parameteres.
`level`	Indicates if the retrieved p values should be adjusted on the donor/acceptor site-level (default) or if unadjusted junction-level p values should be returned.
`dist`	Distribution for which the p-values should be extracted.
`all`	Logical value indicating whether `hyperParams(fds)` should return the results of all evaluated parameter combinations or only for the optimal parameter combination.

A (delayed) matrix or vector dependent on the type of data retrieved.

featureExclusionMask: Retrieves a logical vector indicating for each junction whether it is included or excluded during the fitting procedure.
featureExclusionMask<-: To remove certain junctions from being used in the train step of the encoding dimension we can set the featureExclusion vector to FALSE. This can be helpfull if we have local linkage between features which we do not want to model by the autoencoder.
rho: Returns the fitted rho values for the beta-binomial distribution
zScores: This returns the calculated z-scores.
pVals: This returns the calculated p-values.
padjVals: This returns the adjusted p-values.
predictedMeans: This returns the fitted mu (i.e. psi) values.
deltaPsiValue: Returns the difference between the observed and the fitted psi values.
currentType: Returns the psi type that is used within several methods in the FRASER package.
currentType<-: Sets the psi type that is to be used within several methods in the FRASER package.
pseudocount: Sets and returns the pseudo count used within the FRASER fitting procedure.
hyperParams: This returns the results of the hyperparameter optimization NULL if the hyperparameter opimization was not run yet.
bestQ: This returns the optimal size of the latent space according to the hyperparameter optimization or a simple estimate of about a tenth of the number of samples if the hyperparameter opimization was not run yet.
dontWriteHDF5: Gets the current value of whether the assays should be stored as hdf5 files.
dontWriteHDF5<-: Sets whether the assays should be stored as hdf5 files.
verbose: Dependend on the level of verbosity the algorithm reports more or less to the user. 0 means being quiet and 10 means everything.
verbose<-: Sets the verbosity level to a value between 0 and 10. 0 means being quiet and 10 means reporting everything.

fds <- createTestFraserDataSet()

# should assays be saved as hdf5? 
dontWriteHDF5(fds)
dontWriteHDF5 <- TRUE

# get/set the splice metric for which results should be retrieved 
currentType(fds) <- "psi5"
currentType(fds)

# get fitted parameters
bestQ(fds)
predictedMeans(fds)
rho(fds)

# get statistics
pVals(fds)
padjVals(fds)
zScores(fds)

# set and get pseudocount
pseudocount(4L)
pseudocount()

# retrieve or set a mask to exclude certain junctions in the fitting step
featureExclusionMask(fds, type="theta") <- sample(
        c(FALSE, TRUE), nrow(mcols(fds, type="theta")), replace=TRUE)
featureExclusionMask(fds, type="theta")

# controlling the verbosity level of the output of some algorithms
verbose(fds) <- 2
verbose(fds)