merge_motifs {universalmotif} | R Documentation |
Merge motifs.
Description
Aligns the motifs using compare_motifs()
, then averages the
motif PPMs. Currently the multifreq
slot, if filled in any of the motifs,
will be dropped. Only 0-order background probabilities will be kept.
Motifs are merged one at a time, starting with the first entry in the
list.
Usage
merge_motifs(motifs, method = "ALLR", use.type = "PPM", min.overlap = 6,
min.mean.ic = 0.25, tryRC = TRUE, relative_entropy = FALSE,
normalise.scores = FALSE, min.position.ic = 0, score.strat = "sum",
new.name = NULL)
Arguments
motifs |
See convert_motifs() for acceptable motif formats.
|
method |
character(1) One of PCC, EUCL, SW, KL, ALLR, BHAT, HELL,
SEUCL, MAN, ALLR_LL, WEUCL, WPCC. See details.
|
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") .
|
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 NA s in your output.
|
tryRC |
logical(1) Try the reverse complement of the motifs as well,
report the best score.
|
relative_entropy |
logical(1) Change the ICM calculation affecting
min.position.ic and min.mean.ic . See convert_type() .
|
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.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.
|
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.
|
new.name |
character(1) , NULL Instead of collapsing existing names (if NULL ),
assign a new one manually for the merged motif.
|
Details
See compare_motifs()
for more info on comparison parameters.
If using a comparison metric where 0s are not allowed (KL
, ALLR
, ALLR_LL
, IS
),
then pseudocounts will be added internally. These pseudocounts are only used for
comparison and alignment, and are not used in the final merging step.
Note: score.strat = "a.mean"
is NOT recommended, as merge_motifs()
will
not discriminate between two alignments with equal mean scores, even if one
alignment is longer than the other.
Value
A single motif object. See convert_motifs()
for
available formats.
Author(s)
Benjamin Jean-Marie Tremblay, benjamin.tremblay@uwaterloo.ca
See Also
compare_motifs()
Examples
## Not run:
library(MotifDb)
merged.motif <- merge_motifs(MotifDb[1:5])
## End(Not run)
m1 <- create_motif("TTAAACCCC", name = "1")
m2 <- create_motif("AACC", name = "2")
m3 <- create_motif("AACCCCGG", name = "3")
view_motifs(merge_motifs(c(m1, m2, m3)))
[Package
universalmotif version 1.12.4
Index]