FRASER {FRASER} | R Documentation

## FRASER: Find RAre Splicing Events in RNA-seq data

### Description

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.

### Usage

    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")

### Arguments

`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.

### Details

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").

### Value

FraserDataSet

### Functions

- `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.

### Author(s)

Christian Mertes mertes@in.tum.de

Ines Scheller scheller@in.tum.de

### See Also

`fit`

### Examples

    # 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"))

[Package FRASER version 1.4.0 Index]