motifDiffEnrichment: Differential motif enrichment
In PWMEnrich: PWM enrichment analysis

Description Usage Arguments Details Examples

Test for differential enrichment between two groups of sequences

motifDiffEnrichment(
  sequences1,
  sequences2,
  pwms,
  score = "autodetect",
  bg = "autodetect",
  cutoff = log2(exp(4)),
  verbose = TRUE,
  res1 = NULL,
  res2 = NULL
)

`sequences1`	First set of sequences. Can be either a single sequence (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object.
`sequences2`	Second set of sequences. Can be either a single sequence (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object.
`pwms`	this parameter can take multiple values depending on the scoring scheme and background correction used. When the `method` parameter is set to "autodetect", the following default algorithms are going to be used: if `pwms` is a list containing either frequency matrices or a list of PWM objects then the "affinity" algorithm is selected. If frequency matrices are given, they are converted to PWMs using uniform background. For best performance, convert frequency matrices to PWMs before calling this function using realistic genomic background. Otherwise, appropriate scoring scheme and background correction are selected based on the class of the object (see below).
`score`	this parameter determines which scoring scheme to use. Following scheme as available: "autodetect" - default value. Scoring method is determined based on the type of `pwms` parameter. "affinity" - use threshold-free affinity scores without a background. The `pwms` parameter can either be a list of frequency matrices, `PWM` objects, or a `PWMLognBackground` object. "cutoff" - use number of motif hits above a score cutoff as a measure of enrichment. No background correction is performed. The `pwms` parameter can either be a list of frequency matrices, `PWM` objects, or a `PWMCutoffBackground` object.
`bg`	this parameter determines which background correction to use, if any. "autodetect" - default value. Background correction is determined based on the type of the `pwms` parameter. "logn" - use a lognormal distribution background pre-computed for a set of PWMs. This requires `pwms` to be of class `PWMLognBackground`. "z" - use a z-score for the number of significant motif hits compared to background number of hits. This requires `pwms` to be of class `PWMCutoffBackground`. "none" - no background correction
`cutoff`	the score cutoff for a significant motif hit if scoring scheme "cutoff" is selected.
`verbose`	if to produce verbose output
`res1`	the output of `motifEnrichment` if already calculated for `sequences1`
`res2`	the output of `motifEnrichment` if already calculated for `sequences2`

This function calls motifEnrichment on two groups of sequences and calculates the difference statistics when possible.

if(requireNamespace("PWMEnrich.Dmelanogaster.background")){
  # load the background file for drosophila and lognormal correction
  data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background")

  # get the differential enrichment
  diff = motifDiffEnrichment(DNAString("TGCATCAAGTGTGTAGTGTGAGATTAGT"), 
    DNAString("TGAACGAGTAGGACGATGAGAGATTGATG"), PWMLogn.dm3.MotifDb.Dmel, verbose=FALSE)

  # motifs differentially enriched in the first sequence (with lognormal background correction)
  head(sort(diff$group.bg, decreasing=TRUE))

  # motifs differentially enriched in the second sequence (with lognormal background correction)
  head(sort(diff$group.bg))
}