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Calculate observed and expected k-mer frequencies

Source: R/motif_enrichment_kmers.R
getKmerFreq.Rd

Given a set of sequences, calculate observed and expected k-mer frequencies. Expected frequencies are based on a Markov model of order MMorder.

Usage

getKmerFreq(
  seqs,
  kmerLen = 5,
  MMorder = 1,
  pseudocount = 1,
  zoops = TRUE,
  strata = rep(1L, length(seqs)),
  p.adjust.method = "BH",
  includeRevComp = TRUE
)

Arguments

seqs

Set of sequences, either a character vector or a DNAStringSet.

kmerLen

A numeric scalar giving the k-mer length.

MMorder

A numeric scalar giving the order of the Markov model used to calculate the expected frequencies.

pseudocount

A numeric scalar - will be added to the observed counts for each k-mer to avoid zero values.

zoops

A logical scalar. If TRUE (the default), only one or zero occurences of a k-mer are considered per sequence.

strata

A factor or a numeric scalar defining the strata of sequences. A separate Markov model and expected k-mer frequencies are estimated for the set of sequences in each stratum (level in a strata factor). If strata is a scalar value, it will be interpreted as the number of strata to split the sequences into according to their CpG observed-over-expected counts using kmeans(CpGoe, centers = strata).

p.adjust.method

A character scalar selecting the p value adjustment method (used in p.adjust).

includeRevComp

A logical scalar. If TRUE (default), count k-mer occurrences in both seqs and their reverse-complement, by concatenating seqs and their reverse-complemented versions before the counting. This is useful if motifs can be expected to occur on any strand (e.g. DNA sequences of ChIP-seq peaks). If motifs are only expected on the forward strand (e.g. RNA sequences of CLIP-seq peaks), includeRevComp = FALSE should be used. Note that if strata is a vector of the same length as seqs, each reverse-complemented sequence will be assigned to the same stratum as the forward sequence.

Value

A list with observed and expected k-mer frequencies (freq.obs and freq.exp, respectively), and enrichment statistics for each k-mer.

Examples

res <- getKmerFreq(seqs = c("AAAAATT", "AAATTTT"), kmerLen = 3)
names(res)
#>  [1] "freq.obs"    "freq.exp"    "log2enr"     "sqrtDelta"   "z"          
#>  [6] "p"           "padj"        "strata"      "freq.strata" "CpGoe"      
head(res$freq.obs)
#> AAA AAC AAG AAT ACA ACC 
#>   3   0   0   4   0   0 
head(res$freq.exp)
#>       AAA       AAC       AAG       AAT       ACA       ACC 
#> 1.8295455 0.1663223 0.1663223 1.4969008 0.3326446 0.3326446