runMotifDiscovery {RCAS} | R Documentation

## runMotifDiscovery

### Description

This function builds a random forest classifier to find the top most discriminative motifs in the query regions compared to the background. The background sequences are automatically generated based on the query regions. First, k-mers of a fixed length are generated. The query and control sequences are searched for k-mers allowing for mismatches. A random forest model is trained to find the most discriminative motifs.

### Usage

    runMotifDiscovery(
      queryRegions,
      resizeN = 0,
      motifWidth = 6,
      sampleN = 0,
      genomeVersion,
      maxMismatch = 1,
      motifN = 5,
      nCores = 1
    )

### Arguments

`queryRegions` | GRanges object containing coordinates of input query regions imported by the `importBed` function
`resizeN` | Integer value (default: 0) to resize query regions if they are shorter than the value of `resize`. Set to 0 to disable resize.
`motifWidth` | A Positive integer (default: 6) for the generated k-mers. Warning: we recommend using values below 10 as the computation gets exponentially difficult as the motif width is increased.
`sampleN` | A positive integer value. The queryRegions are randomly downsampled to include intervals as many as `sampleN`. The input will be downsampled only if this value is larger than zero and less than the total number of input intervals.
`genomeVersion` | A character string to denote the BS genome library required to extract sequences. Example: 'hg19'
`maxMismatch` | A positive integer (default: 1) - maximum number of mismatches to allow when searching for k-mer matches in sequences.
`motifN` | A positive integer (default:5) denoting the maximum number of motifs that should be returned by the `findDifferentialMotifs` function
`nCores` | A positive integer (default:1) number of cores used for parallel execution.

### Value

A list of four objects: k-mer count matrices for query and background and lists of string matches for the top discriminating motifs (motifN).

### Examples

    data(queryRegions)
    motifResults <- runMotifDiscovery(queryRegions = queryRegions[1:1000],
                                      genomeVersion = 'hg19',
                                      motifWidth = 6,
                                      resize = 15,
                                      motifN = 1,
                                      maxMismatch = 1,
                                      nCores = 1)

[Package RCAS version 1.16.0 Index]