dot-calcKmerEnrichment: Calculate k-mer enrichment

In fmicompbio/monaLisa: Binned Motif Enrichment Analysis and Visualization

Calculate k-mer enrichment

Description

Given sequences, foreground/background labels and weights, calculate the enrichment of each k-mer in foreground compared to background. This function is called by calcBinnedKmerEnr() for each bin if background != "model".

The default type of test is "fisher". Alternatively, a binomial test can be used by test = "binomial". Using Fisher's exact test has the advantage that special cases such as zero background counts are handled without ad-hoc adjustments to the k-mer frequencies.

For test = "fisher", fisher.test is used with alternative = "greater", making it a one-sided test for enrichment, as is the case with the binomial test.

Usage

.calcKmerEnrichment(k, df, test = c("fisher", "binomial"), verbose = FALSE)

Arguments

k	Numeric scalar giving the length of k-mers to analyze.
df	a DataFrame with sequence information as returned by .iterativeNormForKmers().
test	type of motif enrichment test to perform.
verbose	A logical scalar. If TRUE, report on progress.

Details

The function works in ZOOPS mode, which means only one or zero occurrences of a k-mer are considered per sequence. This is helpful to reduce the impact of simple sequence repeats occurring in few sequences.

Value

A data.frame containing the motifs as rows and the columns:

motifName

: the motif name

logP

: the log p-value for enrichment (natural logarithm). If test="binomial" (default), this log p-value is identical to the one returned by Homer.

sumForegroundWgtWithHits

: the weighted number of k-mer hits in foreground sequences.

sumBackgroundWgtWithHits

: the weighted number of k-mer hits in background sequences.

totalWgtForeground

: the total sum of weights of foreground sequences.

totalWgtBackground

: the total sum of weights of background sequences.

fmicompbio/monaLisa documentation built on Nov. 2, 2024, 1:33 p.m.