Description Usage Arguments Details Value Functions Author(s) See Also Examples
View source: R/Fraserpipeline.R
This help page describes the FRASER function which can be used run the default FRASER pipeline. This pipeline combines the betabinomial fit, the computation of Z scores and p values as well as the computation of deltaPSI values.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  FRASER(
fds,
q,
implementation = c("PCA", "PCABBDecoder", "AEweighted", "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 
q 
The encoding dimensions to be used during the fitting proceadure.
Should be fitted using 
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 pvalues are calculated. Possible are betabinomial, binomial and normal. 
capN 
Counts are capped at this value to speed up the pvalue 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 "AEweighted"), 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 ("PCABBDecoder"). 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 betabinomial fit, the computation of Z scores and p values as well as
the computation of deltaPSI values.
calculateZscore
: This function calculates zscores based on the
observed and expected logit
psi.
calculatePvalues
: This function calculates twosided pvalues based on
the betabinomial 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
pvalues per sample for multiple testing.
Christian Mertes mertes@in.tum.de
Ines Scheller scheller@in.tum.de
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  # 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 fdsobject 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"))

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.