enrich_motifs: Enrich for input motifs in a set of sequences.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/enrich_motifs.R

Description

Given a set of target and background sequences, test if the input motifs are significantly enriched in the targets sequences relative to the background sequences. See the "Sequence manipulation and scanning" vignette.

Usage

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enrich_motifs(motifs, sequences, bkg.sequences, max.p = 1e-05,
  max.q = 1e-05, max.e = 0.001, qval.method = "fdr", threshold = 0.001,
  threshold.type = "pvalue", verbose = 0, RC = FALSE, use.freq = 1,
  shuffle.k = 2, shuffle.method = "euler", return.scan.results = FALSE,
  nthreads = 1, rng.seed = sample.int(10000, 1), motif_pvalue.k = 8,
  use.gaps = TRUE)

Arguments

motifs

See convert_motifs() for acceptable motif formats.

sequences

XStringSet Sequences to scan. Alphabet should match motif.

bkg.sequences

XStringSet Optional. If missing, shuffle_sequences() is used to create background sequences from the input sequences.

max.p

numeric(1) P-value threshold.

max.q

numeric(1) Adjusted P-value threshold. This is only useful if multiple motifs are being enriched for.

max.e

numeric(1). The E-value is calculated by multiplying the adjusted P-value with the number of input motifs times two \insertCitememe2universalmotif.

qval.method

character(1) See stats::p.adjust().

threshold

numeric(1) See details.

threshold.type

character(1) One of c('logodds', 'logodds.abs', 'pvalue'). See details.

verbose

numeric(1) 0 for no output, 4 for max verbosity.

RC

logical(1) If TRUE, check reverse complement of input sequences.

use.freq

numeric(1) The default, 1, uses the motif matrix (from the motif['motif'] slot) to search for sequences. If a higher number is used, then the matching k-let matrix from the motif['multifreq'] slot is used. See add_multifreq().

shuffle.k

numeric(1) The k-let size to use when shuffling input sequences. Only used if no background sequences are input. See shuffle_sequences().

shuffle.method

character(1) One of c('euler', 'markov', 'linear'). See shuffle_sequences().

return.scan.results

logical(1) Return output from scan_sequences(). For large jobs, leaving this as FALSE can save a small amount time by preventing construction of the complete results data.frame from scan_sequences().

nthreads

numeric(1) Run scan_sequences() in parallel with nthreads threads. nthreads = 0 uses all available threads. Note that no speed up will occur for jobs with only a single motif and sequence.

rng.seed

numeric(1) Set random number generator seed. Since shuffling can occur simultaneously in multiple threads using C++, it cannot communicate with the regular R random number generator state and thus requires an independent seed. Each individual sequence in an XStringSet object will be given the following seed: rng.seed * index. See shuffle_sequences().

motif_pvalue.k

numeric(1) Control motif_pvalue() approximation. See motif_pvalue().

use.gaps

logical(1) Set this to FALSE to ignore motif gaps, if present.

Details

To find enriched motifs, scan_sequences() is run on both target and background sequences. stats::fisher.test() is run to test for enrichment.

See scan_sequences() for more info on scanning parameters.

Value

DataFrame Enrichment results in a DataFrame. Function args and (optionally) scan results are stored in the metadata slot.

Author(s)

Benjamin Jean-Marie Tremblay b2tremblay@uwaterloo.ca

References

\insertRef

meme2universalmotif

See Also

scan_sequences(), shuffle_sequences(), add_multifreq(), motif_pvalue()

Examples

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bjmt/universalmotif documentation built on Sept. 19, 2020, 6:51 p.m.