injectOutliers {FRASER} | R Documentation

## Inject artificial outliers in an existing fds

### Description

Inject artificial outliers in an existing fds

### Usage

    injectOutliers(
      fds,
      type = c("psi5", "psi3", "theta"),
      freq = 0.001,
      minDpsi = 0.2,
      minCoverage = 2,
      deltaDistr = "uniformDistr",
      verbose = FALSE,
      method = c("samplePSI", "meanPSI", "simulatedPSI"),
      BPPARAM = bpparam()
    )

### Arguments

`fds` | FraserDataSet
`type` | The psi type
`freq` | The injection frequency.
`minDpsi` | The minimal delta psi with which outliers will be injected.
`minCoverage` | The minimal total coverage (i.e. N) required for a junction to be considered for injection of an outlier.
`deltaDistr` | The distribution from which the delta psi value of the injections is drawn (default: uniform distribution).
`verbose` | Should additional information be printed during computation?
`method` | Defines by which method the new psi of injections is computed, i.e. to which value the delta psi of the injection is added: "meanPSI" for adding to the mean psi of the junction over all samples or "samplePSI" to add to the psi value of the junction in the specific sample. "simulatedPSI" is only possible if a simulated dataset is used.
`BPPARAM` | A BiocParallel object to run the computation in parallel

### Value

FraserDataSet

### Examples

    # A generic dataset
    fds <- makeSimulatedFraserDataSet()
    fds <- injectOutliers(fds, minDpsi=0.2, freq=1E-3)

[Package FRASER version 1.6.1 Index]