analyzeRLFS {RLSeq} | R Documentation

## Analyze RLFS

### Description

Analyzes the enrichment of ranges within R-loop forming sequences (RLFS). See details.

### Usage

    analyzeRLFS(
      object,
      mask = NULL,
      quiet = FALSE,
      useMask = TRUE,
      noZ = FALSE,
      ntimes = 100,
      stepsize = 50,
      ...
    )

### Arguments

`object` | An RLRanges object.
`mask` | GRanges object containing masked genomic ranges. Not needed unless masked genome unavailable (see genomeMasks). Custom masks can be generated using regioneR::getMask.
`quiet` | If TRUE, messages are suppressed. Default: FALSE.
`useMask` | If FALSE, masked genome is not used. This is not recommended unless a mask is unavailable as it can lead to spurious results. Default: TRUE.
`noZ` | If TRUE, Z-score distribution is not calculated. Default: FALSE.
`ntimes` | Number of permutations to perform (default: 100).
`stepsize` | The step size for calculating the Z score distribution. Default: 50. See also regioneR::localZScore.
`...` | Arguments passed to regioneR::permTest.

### Details

R-loop forming sequences are regions of the genome with sequences that are favorable for R-loop formation. They are computationally predicted with the QmRLFS-finder software program and serve as a data-independent test of whether a sample has mapped R-loops robustly or not.

#### Method

Permutation testing is implemented via regioneR::permTest such that, for each permutation, R-loop peaks were randomized using regioneR::circularRandomizeRegions and then the number of overlaps with RLFS are counted. 100 permutations are used by default to build an empirical distribution for peak/RLFS overlap. Then the true number of overlaps from non-randomized peaks and RLFS are compared to the null distribution to calculate Z-score and significance of enrichment. Finally, a Z-score distribution was calculated (using regioneR::localZScore) 5kb upstream and downstream of the average RLFS midpoint.

These results are subsequently used in the binary classification of the sample as "POS" (maps R-loops) or "NEG" (does not map R-loops). See also predictCondition.

### Value

An RLRanges object with RLFS analysis results accessible via `RLSeq::rlresult(object, "rlfsRes")`. Contains the following structure:

- `perTestResults`

  - An object of the class `permTestResultsList` from `regioneR` with the results of permutation testing. See also regioneR::permTest for full description.

- `Z-scores`

  - An object of the class `localZScoreResultsList` from `regioneR`. Contains the results of local Z-score analysis +/-5kb around each RLFS. See also regioneR::localZScore.

### Examples

    # Example dataset
    rlr <- readRDS(system.file("extdata", "rlrsmall.rds", package = "RLSeq"))

    # Perform RLFS analysis (remove ntimes=2 and noZ=TRUE for a typical analysis)
    rlr <- analyzeRLFS(rlr, ntimes = 2, noZ = TRUE)

[Package RLSeq version 1.0.0 Index]