R: Motif-related utility functions.

utils-motif {universalmotif}

R Documentation

Motif-related utility functions.

Description

Motif-related utility functions.

Usage

consensus_to_ppm(letter)

consensus_to_ppmAA(letter)

get_consensus(position, alphabet = "DNA", type = "PPM",
  pseudocount = 1)

get_consensusAA(position, type = "PPM", pseudocount = 0)

get_matches(motif, score)

icm_to_ppm(position)

make_DBscores(db.motifs, method, shuffle.db = TRUE, shuffle.k = 3,
  shuffle.method = "linear", shuffle.leftovers = "asis",
  rand.tries = 1000, normalise.scores = TRUE, min.overlap = 6,
  min.mean.ic = 0, progress = TRUE, BP = FALSE)

motif_score(motif, threshold = c(0, 1))

pcm_to_ppm(position, pseudocount = 0)

position_icscore(position, bkg = 0, type = "PPM", pseudocount = 1,
  nsites = 100, relative_entropy = FALSE)

ppm_to_icm(position, bkg, schneider_correction = FALSE, nsites = 100,
  relative_entropy = FALSE)

ppm_to_pcm(position, nsites = 100)

ppm_to_pwm(position, bkg, pseudocount = 1, nsites = 100,
  smooth = TRUE)

pwm_to_ppm(position, bkg)

score_match(motif, match)

summarise_motifs(motifs, na.rm = TRUE)

Arguments

`letter`	`character(1)` Any DNA, RNA, or AA IUPAC letter. Ambiguity letters are accepted.
`position`	`numeric` A numeric vector representing the frequency or probability for each alphabet letter at a specific position.
`alphabet`	`character(1)` One of `c('DNA', 'RNA')`.
`type`	`character(1)` One of `c('PCM', 'PPM', 'PWM' 'ICM')`.
`pseudocount`	`numeric(1)` Used to prevent zeroes in motif matrix.
`motif`	Motif object to calculate scores from.
`score`	`numeric(1)` Logodds motif score.
`db.motifs`	`list` Database motifs.
`method`	`character(1)` One of `c('PCC', 'MPCC', 'EUCL', 'MEUCL', 'SW', 'MSW', 'KL', 'MKL')`. See `compare_motifs()`.
`shuffle.db`	`logical(1)` Shuffle `db.motifs` rather than generate random motifs with `create_motif()`.
`shuffle.k`	`numeric(1)` See `shuffle_motifs()`.
`shuffle.method`	`character(1)` See `shuffle_motifs()`.
`shuffle.leftovers`	`character(1)` See `shuffle_motifs()`.
`rand.tries`	`numeric(1)` Number of random motifs to create for P-value computation.
`normalise.scores`	`logical(1)` See `compare_motifs()`.
`min.overlap`	`numeric(1)` Minimum required motif overlap. See `compare_motifs()`.
`min.mean.ic`	`numeric(1)` See `compare_motifs()`.
`progress`	`logical(1)` Show progress. Not recommended if `BP = TRUE`.
`BP`	`logical(1)` Use the BiocParallel package. See `BiocParallel::register()` to change the default backend.
`threshold`	`numeric(1)` Any number of numeric values between 0 and 1 representing score percentage.
`bkg`	`Numeric` Should be the same length as the alphabet length.
`nsites`	`numeric(1)` Number of sites motif originated from.
`relative_entropy`	`logical(1)` Calculate information content as relative entropy or Kullback-Leibler divergence.
`schneider_correction`	`logical(1)` Apply sample size correction.
`smooth`	`logical(1)` Apply pseudocount correction.
`match`	`character(1)` Sequence string to calculate score from.
`motifs`	`list` A list of universalmotif motifs.
`na.rm`	`logical` Remove columns where all values are `NA`.

Value

For consensus_to_ppm() and consensus_to_ppmAA(): a numeric vector of length 4 and 20, respectively.

For get_consensus() and get_consensusAA(): a character vector of length 1.

For get_matches(): a character vector of motif matches.

For make_DBscores(): a data.frame with score distributions for the input database.

For motif_score(): a named numeric vector of motif scores.

For position_icscore(): a numeric vector of length 1.

For ppm_to_icm(), icm_to_ppm(), pcm_to_ppm(), ppm_to_pcm(), ppm_to_pwm(), and pwm_to_ppm(): a numeric vector with length equal to input numeric vector.

For score_match(): a numeric vector with the match motif score.

For summarise_motifs(): a data.frame with columns representing the universalmotif slots.

Author(s)

Benjamin Jean-Marie Tremblay, b2tremblay@uwaterloo.ca

Examples

#######################################################################
## Setting up some variables
data(examplemotif)
m <- normalize(examplemotif)
motif <- create_motif(nsites = 100, pseudocount = 0.8)["motif"]
motif.icm <- apply(motif, 2, ppm_to_icm, nsites = 100,
                   bkg = c(0.25, 0.25, 0.25, 0.25))
motif.ppm <- apply(motif.icm, 2, icm_to_ppm)
motif.consensus <- apply(motif.ppm, 2, get_consensus)
motif.aa <- create_motif(alphabet = "AA")["motif"]
motif.aa.consensus <- apply(motif.aa, 2, get_consensusAA, type = "PPM")
#######################################################################

#######################################################################
## consensus_to_ppm
## Do the opposite of get_consensus. Note that loss of information is
## inevitable.
motif.ppm4 <- sapply(motif.consensus, consensus_to_ppm)

#######################################################################
## consensus_to_ppmAA
## Do the opposite of get_consensusAA.
motif.aa2 <- sapply(motif.aa.consensus, consensus_to_ppmAA)

#######################################################################
## get_consensus
## Get a consensus string from a DNA/RNA motif.
motif.consensus <- apply(motif.ppm, 2, get_consensus)

#######################################################################
## get_consensusAA
## Get a consensus string from an amino acid motif. Unless each position
## is clearly dominated by a single amino acid, the resulting string will
## likely be useless.
motif.aa <- create_motif(alphabet = "AA")["motif"]
motif.aa.consensus <- apply(motif.aa, 2, get_consensusAA, type = "PPM")

#######################################################################
## get_match
## Get all possible motif matches above input score
get_matches(m, 10)

#######################################################################
## icm_to_ppm
## Do the opposite of ppm_to_icm.
motif.ppm <- apply(motif.icm, 2, icm_to_ppm)

#######################################################################
## make_DBscores
## Generate P-value database for use with compare_motifs. Note that these
## must be created individually for all combinations of methods and
## normalisation.
## Not run: 
library(MotifDb)
motifs <- convert_motifs(MotifDb[1:100])
make_DBscores(motifs, method = "PCC")

## End(Not run)

#######################################################################
## motif_score
## Calculate motif score from different thresholds
data(examplemotif)
m <- normalize(examplemotif)
motif_score(m, c(0, 0.8, 1))

#######################################################################
## pcm_to_ppm
## Go from a count type motif to a probability type motif.
motif.pcm <- create_motif(type = "PCM", nsites = 50)["motif"]
motif.ppm2 <- apply(motif.pcm, 2, pcm_to_ppm, pseudocount = 1)

#######################################################################
## position_icscore
## Similar to ppm_to_icm, except this calculates a sum for the position.
ic.scores <- apply(motif.ppm, 2, position_icscore, type = "PPM",
                   bkg = c(0.25, 0.25, 0.25, 0.25))

#######################################################################
## ppm_to_icm
## Convert one column from a probability type motif to an information
## content type motif.
motif <- create_motif(nsites = 100, pseudocount = 0.8)["motif"]
motif.icm <- apply(motif, 2, ppm_to_icm, nsites = 100,
                   bkg = c(0.25, 0.25, 0.25, 0.25))

#######################################################################
## ppm_to_pcm
## Do the opposite of pcm_to_ppm.
motif.pcm2 <- apply(motif.ppm2, 2, ppm_to_pcm, nsites = 50)

#######################################################################
## ppm_to_pwm
## Go from a probability type motif to a weight type motif.
motif.pwm <- apply(motif.ppm, 2, ppm_to_pwm, nsites = 100,
                   bkg = c(0.25, 0.25, 0.25, 0.25))

#######################################################################
## pwm_to_ppm
## Do the opposite of ppm_to_pwm.
motif.ppm3 <- apply(motif.pwm, 2, pwm_to_ppm,
                    bkg = c(0.25, 0.25, 0.25, 0.25))

#######################################################################
## Note that not all type conversions can be done directly; for those
## type conversions which are unavailable, universalmotif just chains
## together others (i.e. from PCM -> ICM => pcm_to_ppm -> ppm_to_icm)

#######################################################################
## score_match
## Calculate score of a particular match
score_match(m, "TATATAT")
score_match(m, "TATATAG")

#######################################################################
## summarise_motifs
## Create a data.frame of information based on a list of motifs.
m1 <- create_motif()
m2 <- create_motif()
m3 <- create_motif()
summarise_motifs(list(m1, m2, m3))

[Package universalmotif version 1.2.1 Index]

Motif-related utility functions.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples