FRASER: FRASER: Find RAre Splicing Events in RNA-seq data
In FRASER: Find RAre Splicing Events in RNA-Seq Data

Description Usage Arguments Details Value Functions Author(s) See Also Examples

This help page describes the FRASER function which can be used run the default FRASER pipeline. This pipeline combines the beta-binomial fit, the computation of Z scores and p values as well as the computation of delta-PSI values.

FRASER(
  fds,
  q,
  implementation = c("PCA", "PCA-BB-Decoder", "AE-weighted", "AE", "BB"),
  iterations = 15,
  BPPARAM = bpparam(),
  correction,
  ...
)

calculateZscore(fds, type = currentType(fds), logit = TRUE)

calculatePvalues(
  fds,
  type = currentType(fds),
  implementation = "PCA",
  BPPARAM = bpparam(),
  distributions = c("betabinomial"),
  capN = 5 * 1e+05
)

calculatePadjValues(fds, type = currentType(fds), method = "BY")

`fds`	A `FraserDataSet` object
`q`	The encoding dimensions to be used during the fitting proceadure. Should be fitted using `optimHyperParams` if unknown. If a named vector is provided it is used for the different splicing types.
`implementation`	The method that should be used to correct for confounders.
`iterations`	The maximal number of iterations. When the autoencoder has not yet converged after these number of iterations, the fit stops anyway.
`BPPARAM`	A BiocParallel object to run the computation in parallel
`correction`	Deprecated. The name changed to implementation.
`...`	Additional parameters passed on to the internal fit function
`type`	The type of PSI (psi5, psi3 or theta for theta/splicing efficiency)
`logit`	Indicates if z scores are computed on the logit scale (default) or in the natural (psi) scale.
`distributions`	The distribution based on which the p-values are calculated. Possible are beta-binomial, binomial and normal.
`capN`	Counts are capped at this value to speed up the p-value calculation
`method`	The p.adjust method that should be used.

All computed values are returned as an FraserDataSet object. To have more control over each analysis step, one can call each function separately.

fit to control for confounding effects and fit the beta binomial model parameters
calculatePvalues to calculate the nominal p values
calculatePadjValues to calculate adjusted p values (per sample)
calculateZscore to calculate the Z scores

Available methods to correct for the confounders are currently: a denoising autoencoder with a BB loss ("AE" and "AE-weighted"), PCA ("PCA"), a hybrid approach where PCA is used to fit the latent space and then the decoder of the autoencoder is fit using the BB loss ("PCA-BB-Decoder"). Although not recommended, it is also possible to directly fit the BB distrbution to the raw counts ("BB").

FraserDataSet

FRASER: This function runs the default FRASER pipeline combining the beta-binomial fit, the computation of Z scores and p values as well as the computation of delta-PSI values.
calculateZscore: This function calculates z-scores based on the observed and expected logit psi.
calculatePvalues: This function calculates two-sided p-values based on the beta-binomial distribution (or binomial or normal if desired). The returned p values are already adjusted with Holm's method per donor or acceptor site, respectively.
calculatePadjValues: This function adjusts the previously calculated p-values per sample for multiple testing.

Christian Mertes mertes@in.tum.de

Ines Scheller scheller@in.tum.de

fit

# set default parallel backend
register(SerialParam())

# preprocessing
fds <- createTestFraserDataSet()

# filtering not expressed introns
fds <- calculatePSIValues(fds)
fds <- filterExpressionAndVariability(fds)

# Run the full analysis pipeline: fits distribution and computes p values
fds <- FRASER(fds, q=2, implementation="PCA")

# afterwards, the fitted fds-object can be saved and results can 
# be extracted and visualized, see ?saveFraserDataSet, ?results and 
# ?plotVolcano
 
# The functions run inside the FRASER function can also be directly 
# run themselves. 
# To directly run the fit function:
fds <- fit(fds, implementation="PCA", q=2, type="psi5")

# To directly run the nomial and adjusted p value and z score 
# calculation, the following functions can be used:
fds <- calculatePvalues(fds, type="psi5")
head(pVals(fds, type="psi5"))
fds <- calculatePadjValues(fds, type="psi5", method="BY")
head(padjVals(fds, type="psi5"))
fds <- calculateZscore(fds, type="psi5")
head(zScores(fds, type="psi5"))