make_DBscores: Create P-value databases.
In universalmotif: Import, Modify, and Export Motifs with R

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

Generate data used by compare_motifs() for P-value calculations. By default, compare_motifs() uses an internal database based on the JASPAR2018 core motifs \insertCitejasparuniversalmotif. Parameters for distributions are are estimated for every combination of motif widths.

make_DBscores(db.motifs, method = c("PCC", "EUCL", "SW", "KL", "WEUCL",
  "ALLR", "BHAT", "HELL", "WPCC", "SEUCL", "MAN", "ALLR_LL"),
  shuffle.db = TRUE, shuffle.k = 3, shuffle.method = "linear",
  rand.tries = 1000, widths = 5:30, min.position.ic = 0,
  normalise.scores = c(FALSE, TRUE), min.overlap = 6, min.mean.ic = 0.25,
  progress = TRUE, nthreads = 1, tryRC = TRUE, score.strat = c("sum",
  "a.mean", "g.mean", "median", "wa.mean", "wg.mean", "fzt"))

`db.motifs`	`list` Database motifs.
`method`	`character(1)` One of PCC, EUCL, SW, KL, ALLR, BHAT, HELL, SEUCL, MAN, ALLR_LL, WEUCL, WPCC. See details.
`shuffle.db`	`logical(1)` Deprecated. Does nothing. generate random motifs with `create_motif()`.
`shuffle.k`	`numeric(1)` See `shuffle_motifs()`.
`shuffle.method`	`character(1)` See `shuffle_motifs()`.
`rand.tries`	`numeric(1)` Approximate number of comparisons to perform for every combination of `widths`.
`widths`	`numeric` Motif widths to use in P-value database calculation.
`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.
`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.
`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.
`progress`	`logical(1)` Show progress.
`nthreads`	`numeric(1)` Run `compare_motifs()` in parallel with `nthreads` threads. `nthreads = 0` uses all available threads.
`tryRC`	`logical(1)` Try the reverse complement of the motifs as well, report the best score.
`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.

See compare_motifs() for more info on comparison parameters.

To replicate the internal universalmotif DB scores, run make_DBscores() with the default settings. Note that this will be a slow process.

Arguments widths, method, normalise.scores and score.strat are vectorized; all combinations will be attempted.

A DataFrame with score distributions for the input database. If more than one make_DBscores() run occurs (i.e. args method, normalise.scores or score.strat are longer than 1), then the function args are included in the metadata slot.

Benjamin Jean-Marie Tremblay, b2tremblay@uwaterloo.ca

\insertRef

jasparuniversalmotif

compare_motifs()

## Not run: 
library(MotifDb)
motifs <- convert_motifs(MotifDb[1:100])
scores <- make_DBscores(motifs, method = "PCC")
compare_motifs(motifs, 1:100, db.scores = scores)

## End(Not run)