Compare motifs from segregated sequences — motif

Compute motif similarity scores between motifs discovered from segregated sequences. Wrapper around compare_motifs to compare motifs from different groups of sequences. To see the possible similarity measures available, refer to details.

Usage

motif_similarity(
  streme_out,
  method = "PCC",
  normalise.scores = TRUE,
  BPPARAM = BiocParallel::bpparam(),
  ...
)

Arguments

streme_out

Output from denovo_motifs.

method

character(1) One of PCC, EUCL, SW, KL, ALLR, BHAT, HELL, SEUCL, MAN, ALLR_LL, WEUCL, WPCC. See details.

normalise.scores

logical(1) Favour alignments which leave fewer unaligned positions, as well as alignments between motifs of similar length. Similarity scores are multiplied by the ratio of aligned positions to the total number of positions in the larger motif, and the inverse for distance scores.

BPPARAM

A BiocParallelParam-class object specifying run parameters. (default = bpparam())

...

Arguments passed on to universalmotif::compare_motifs

motifs: See convert_motifs() for acceptable motif formats.
compare.to: numeric If missing, compares all motifs to all other motifs. Otherwise compares all motifs to the specified motif(s).
db.scores: data.frame or DataFrame. See details.
use.freq: numeric(1). For comparing the multifreq slot.
use.type: character(1) One of 'PPM' and 'ICM'. The latter allows for taking into account the background frequencies if relative_entropy = TRUE. Note that 'ICM' is not allowed when method = c("ALLR", "ALLR_LL").
tryRC: logical(1) Try the reverse complement of the motifs as well, report the best score.
min.overlap: numeric(1) Minimum overlap required when aligning the motifs. Setting this to a number higher then the width of the motifs will not allow any overhangs. Can also be a number between 0 and 1, representing the minimum fraction that the motifs must overlap.
min.mean.ic: numeric(1) Minimum mean information content between the two motifs for an alignment to be scored. This helps prevent scoring alignments between low information content regions of two motifs. Note that this can result in some comparisons failing if no alignment passes the mean IC threshold. Use average_ic() to filter out low IC motifs to get around this if you want to avoid getting NAs in your output.
min.position.ic: numeric(1) Minimum information content required between individual alignment positions for it to be counted in the final alignment score. It is recommended to use this together with normalise.scores = TRUE, as this will help punish scores resulting from only a fraction of an alignment.
relative_entropy: logical(1) Change the ICM calculation affecting min.position.ic and min.mean.ic. See convert_type().
max.p: numeric(1) Maximum P-value allowed in reporting matches. Only used if compare.to is set.
max.e: numeric(1) Maximum E-value allowed in reporting matches. Only used if compare.to is set. The E-value is the P-value multiplied by the number of input motifs times two.
nthreads: numeric(1) Run compare_motifs() in parallel with nthreads threads. nthreads = 0 uses all available threads.
score.strat: character(1) How to handle column scores calculated from motif alignments. "sum": add up all scores. "a.mean": take the arithmetic mean. "g.mean": take the geometric mean. "median": take the median. "wa.mean", "wg.mean": weighted arithmetic/geometric mean. "fzt": Fisher Z-transform. Weights are the total information content shared between aligned columns.
output.report: character(1) Provide a filename for compare_motifs() to write an html ouput report to. The top matches are shown alongside figures of the match alignments. This requires the knitr and rmarkdown packages. (Note: still in development.)
output.report.max.print: numeric(1) Maximum number of top matches to print.

Value

A list of matrices containing the similarity scores between motifs from different groups of sequences. The order of comparison is as follows, with first element representing the rows and second element representing the columns of the matrix:

1. Common motifs comparison: Common seqs from reference (1) <-> comparison (2)
2. Unique motifs comparison: Unique seqs from reference (1) <-> comparison (2)
3. Cross motifs comparison 1: Unique seqs from reference (1) <-> comparison (1)
4. Cross motifs comparison 2: Unique seqs from comparison (2) <-> reference (1)

The list is repeated for each set of comparison groups in input.

Details

Available metrics

The following metrics are available:

Euclidean distance (EUCL) (Choi et al. 2004)
Weighted Euclidean distance (WEUCL)
Kullback-Leibler divergence (KL) (Kullback and Leibler 1951; Roepcke et al. 2005)
Hellinger distance (HELL) (Hellinger 1909)
Squared Euclidean distance (SEUCL)
Manhattan distance (MAN)
Pearson correlation coefficient (PCC)
Weighted Pearson correlation coefficient (WPCC)
Sandelin-Wasserman similarity (SW), or sum of squared distances (Sandelin and Wasserman 2004)
Average log-likelihood ratio (ALLR) (Wang and Stormo 2003)
Lower limit ALLR (ALLR_LL) (Mahony et al. 2007)
Bhattacharyya coefficient (BHAT) (Bhattacharyya 1943)

Comparisons are calculated between two motifs at a time. All possible alignments are scored, and the best score is reported. In an alignment scores are calculated individually between columns. How those scores are combined to generate the final alignment scores depends on score.strat.

See the "Motif comparisons and P-values" vignette for a description of the various metrics. Note that PCC, WPCC, SW, ALLR, ALLR_LL and BHAT are similarities; higher values mean more similar motifs. For the remaining metrics, values closer to zero represent more similar motifs.

Small pseudocounts are automatically added when one of the following methods is used: KL, ALLR, ALLR_LL, IS. This is avoid zeros in the calculations.

Calculating P-values

To note regarding p-values: P-values are pre-computed using the make_DBscores() function. If not given, then uses a set of internal precomputed P-values from the JASPAR2018 CORE motifs. These precalculated scores are dependent on the length of the motifs being compared. This takes into account that comparing small motifs with larger motifs leads to higher scores, since the probability of finding a higher scoring alignment is higher.

The default P-values have been precalculated for regular DNA motifs. They are of little use for motifs with a different number of alphabet letters (or even the multifreq slot).

Examples

if (memes::meme_is_installed()) {
    data("CTCF_TIP_peaks", package = "MotifPeeker")
    data("CTCF_ChIP_peaks", package = "MotifPeeker")

    if (requireNamespace("BSgenome.Hsapiens.UCSC.hg38")) {
        genome_build <-
            BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38
        segregated_peaks <- segregate_seqs(CTCF_TIP_peaks, CTCF_ChIP_peaks)
        denovo_motifs <- denovo_motifs(unlist(segregated_peaks),
                            trim_seq_width = 50,
                            genome_build = genome_build,
                            discover_motifs_count = 1,
                            filter_n = 6,
                            maxw = 8,
                            minw = 8,
                            out_dir = tempdir())
        similarity_matrices <- motif_similarity(denovo_motifs)
        print(similarity_matrices)
    }
}
#> [[1]]
#>                m01_YGCCCYCTRSTGGCMRV
#> m01_GAGGDSARAS             0.2258419
#> 
#> [[2]]
#>                   m01_CMYCTRSTGGYY
#> m01_TGASCTCHADWGW        0.4360961
#> 
#> [[3]]
#>                   m01_YGCCCYCTRSTGGCMRV
#> m01_TGASCTCHADWGW             0.4128632
#> 
#> [[4]]
#>                  m01_GAGGDSARAS
#> m01_CMYCTRSTGGYY      0.2843023
#>