filtering {FRASER} | R Documentation

## Filtering FraserDataSets

### Description

This method can be used to filter out introns that are not reliably detected and to remove introns with no variablity between samples.

### Usage

    filterExpressionAndVariability(
      object,
      minExpressionInOneSample = 20,
      quantile = 0.05,
      quantileMinExpression = 1,
      minDeltaPsi = 0,
      filter = TRUE,
      delayed = ifelse(ncol(object) <= 300, FALSE, TRUE),
      BPPARAM = bpparam()
    )

    ## S4 method for signature 'FraserDataSet'
    filterExpression(
      object,
      minExpressionInOneSample = 20,
      quantile = 0.05,
      quantileMinExpression = 1,
      filter = TRUE,
      delayed = ifelse(ncol(object) <= 300, FALSE, TRUE),
      BPPARAM = bpparam()
    )

    filterVariability(
      object,
      minDeltaPsi = 0,
      filter = TRUE,
      delayed = ifelse(ncol(object) <= 300, FALSE, TRUE),
      BPPARAM = bpparam()
    )

### Arguments

`object` | A `FraserDataSet` object
`minExpressionInOneSample` | The minimal read count in at least one sample that is required for an intron to pass the filter.
`quantile` | Defines which quantile should be considered for the filter.
`quantileMinExpression` | The minimum read count an intron needs to have at the specified quantile to pass the filter.
`minDeltaPsi` | Only introns for which the maximal difference in the psi value of a sample to the mean psi of the intron is larger than this value pass the filter.
`filter` | If TRUE, a subsetted fds containing only the introns that passed all filters is returned. If FALSE, no subsetting is done and the information of whether an intron passed the filters is only stored in the mcols.
`delayed` | If FALSE, count matrices will be loaded into memory, otherwise the function works on the delayedMatrix representations. The default value depends on the number of samples in the fds-object.
`BPPARAM` | the BiocParallel parameters for the parallelization

### Value

A FraserDataSet with information about which junctions passed the filters. If `filter=TRUE`, the filtered FraserDataSet is returned.

### Functions

- `filterExpressionAndVariability`: This functions filters out both introns with low read support and introns that are not variable across samples.

- `filterExpression,FraserDataSet-method`: This function filters out introns and corresponding splice sites that have low read support in all samples.

- `filterVariability`: This function filters out introns and corresponding splice sites which do not show variablity across samples.

### Examples

    fds <- createTestFraserDataSet()
    fds <- filterExpressionAndVariability(fds, minDeltaPsi=0.1, filter=FALSE)
    mcols(fds, type="psi5")[, c(
            "maxCount", "passedExpression", "maxDPsi3", "passedVariability")]

    plotFilterExpression(fds)
    plotFilterVariability(fds)

[Package FRASER version 1.4.0 Index]