quantilePositions {CAGEr} | R Documentation

## Determine CTSS quantile positions within clusters

### Description

Calculates the positions of “upper” and “lower” quantiles of CAGE signal along tag clusters or consensus clusters in each sample of a `CAGEexp` object.

### Usage

    quantilePositions(
      object,
      clusters = c("tagClusters", "consensusClusters"),
      qLow = 0.1,
      qUp = 0.9,
      useMulticore = FALSE,
      nrCores = NULL
    )

    ## S4 method for signature 'CAGEexp'
    quantilePositions(
      object,
      clusters = c("tagClusters", "consensusClusters"),
      qLow = 0.1,
      qUp = 0.9,
      useMulticore = FALSE,
      nrCores = NULL
    )

### Arguments

`object` | A CAGEexp object.
`clusters` | Either `tagClusters` or `consensusClusters`.
`qLow, qUp` | Which “lower” or “upper” quantiles should be calculated. Numeric vector of values in range `[0,1]`.
`useMulticore` | Logical, should multicore be used. `useMulticore = TRUE` has only effect on Unix-like platforms.
`nrCores` | Number of cores to use when `useMulticore = TRUE`. Default value `NULL` uses all detected cores.

### Details

From the 5' end the position, the position of a quantile q is determined as the first base in which of the cumulative expression is higher or equal to q% of the total CAGE signal of that cluster. Promoter interquantile width is defined as the distance (in base pairs) between a “lower” and an “upper” quantile position.

### Value

Returns the objects, in which the positions of the quantiles are defined relatively to the start point of their cluster, for more efficient `Rle` compression. The quantile data for tag clusters are stored in the `TagClusters` objects directly. The quantile data for `consensus clusters` are stored in `integer` matrices named “q_x”, where x represents the quantile (for instance, `q_0.1`), and these matrices are assays of the `consensusClusters` `RangedSummarizedExperiment`.

### Author(s)

Vanja Haberle

Charles Plessy

### See Also

Other CAGEr object modifiers: `CTSStoGenes()`, `CustomConsensusClusters()`, `aggregateTagClusters()`, `annotateCTSS()`, `clusterCTSS()`, `cumulativeCTSSdistribution()`, `getCTSS()`, `normalizeTagCount()`, `summariseChrExpr()`

Other CAGEr clusters functions: `CTSSclusteringMethod()`, `CTSScumulativesTagClusters()`, `CustomConsensusClusters()`, `aggregateTagClusters()`, `clusterCTSS()`, `consensusClustersDESeq2()`, `consensusClustersGR()`, `cumulativeCTSSdistribution()`, `plotInterquantileWidth()`, `tagClustersGR()`

### Examples

    quantilePositions(exampleCAGEexp, "tagClusters",       qLow = c(0.1, 0.2), qUp = c(0.8, 0.9))
    tagClustersGR(exampleCAGEexp)
    quantilePositions(exampleCAGEexp, "consensusClusters", qLow = c(0.1, 0.2), qUp = c(0.8, 0.9))

[Package CAGEr version 2.0.2 Index]