R: Motif-related utility functions.

utils-motif {universalmotif}

R Documentation

Motif-related utility functions.

Description

Motif-related utility functions.

Usage

add_gap(motif, gaploc, mingap, maxgap)

compare_columns(x, y, method, bkg1 = rep(1/length(x), length(x)),
  bkg2 = rep(1/length(y), length(y)), nsites1 = 100, nsites2 = 100)

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)

get_scores(motif)

icm_to_ppm(position)

motif_score(motif, threshold = c(0, 1), use.freq = 1)

pcm_to_ppm(position, pseudocount = 0)

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

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

ppm_to_pcm(position, nsites = 100)

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

pwm_to_ppm(position, bkg = numeric())

score_match(motif, match)

summarise_motifs(motifs, na.rm = TRUE)

ungap(motif, delete = FALSE)

Arguments

`motif`	Motif object to calculate scores from, or add/remove gap.
`gaploc`	`numeric` Motif gap locations. The gap occurs immediately after every position value. If missing, uses `round(ncol(motif) / 2)`.
`mingap`	`numeric` Minimum gap size. Must have one value for every location. If missing, set to 1.
`maxgap`	`numeric` Maximum gap size. Must have one value for every location. If missing, set to 5.
`x`	`numeric` First column for comparison.
`y`	`numeric` Second column for comparison.
`method`	`character(1)` Column comparison metric. See `compare_motifs()` for details.
`bkg1`	`numeric` Vector of background probabilities for the first column. Only relevant if `method = "ALLR"`.
`bkg2`	`numeric` Vector of background probabilities for the second column. Only relevant if `method = "ALLR"`.
`nsites1`	`numeric(1)` Number of sites for the first column. Only relevant if `method = "ALLR"`.
`nsites2`	`numeric(1)` Number of sites for the second column. Only relevant if `method = "ALLR"`.
`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.
`score`	`numeric(1)` Logodds motif score.
`threshold`	`numeric(1)` Any number of numeric values between 0 and 1 representing score percentage.
`use.freq`	`numeric(1)` Use regular motif or the respective `multifreq` representation.
`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`.
`delete`	`logical(1)` Clear gap information from motif. If `FALSE`, then it can be reactivated simply with `add_gap(motif)`.

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 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

data(examplemotif)

#######################################################################
## add_gap
## Add gap information to a motif.
m <- create_motif()
# Add a gap size 5-8 between positions 4 and 5:
m <- add_gap(m, gaploc = 4, mingap = 5, maxgap = 8)

#######################################################################
## compare_columns
## Compare two numeric vectors using the metrics from compare_motifs()
compare_columns(c(0.5, 0.1, 0.1, 0.2), c(0.7, 0.1, 0.1, 0.1), "PCC")

#######################################################################
## consensus_to_ppm
## Do the opposite of get_consensus. Note that loss of information is
## inevitable. Generates a sequence matrix.
sapply(c("A", "G", "T", "B"), consensus_to_ppm)

#######################################################################
## consensus_to_ppmAA
## Do the opposite of get_consensusAA and generate a motif matrix.
sapply(c("V", "A", "L"), consensus_to_ppmAA)

#######################################################################
## get_consensus
## Get a consensus string from a DNA/RNA motif.
m <- create_motif()["motif"]
apply(m, 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.
m <- create_motif(alphabet = "AA")["motif"]
apply(m, 2, get_consensusAA, type = "PPM")

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

#######################################################################
## get_scores
## Get all possible scores for a motif
length(get_scores(examplemotif))

#######################################################################
## icm_to_ppm
## Do the opposite of ppm_to_icm.
m <- create_motif(type = "ICM")["motif"]
apply(m, 2, icm_to_ppm)

#######################################################################
## motif_score
## Calculate motif score from different thresholds
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.
m <- create_motif(type = "PCM", nsites = 50)["motif"]
apply(m, 2, pcm_to_ppm, pseudocount = 1)

#######################################################################
## position_icscore
## Similar to ppm_to_icm, except this calculates the position sum.
m <- create_motif()["motif"]
apply(m, 2, position_icscore, type = "PPM", bkg = rep(0.25, 4))

#######################################################################
## ppm_to_icm
## Convert one column from a probability type motif to an information
## content type motif.
m <- create_motif(nsites = 100, pseudocount = 0.8)["motif"]
apply(m, 2, ppm_to_icm, nsites = 100, bkg = rep(0.25, 4))

#######################################################################
## ppm_to_pcm
## Do the opposite of pcm_to_ppm.
m <- create_motif()["motif"]
apply(m, 2, ppm_to_pcm, nsites = 50)

#######################################################################
## ppm_to_pwm
## Go from a probability type motif to a weight type motif.
m <- create_motif()["motif"]
apply(m, 2, ppm_to_pwm, nsites = 100, bkg = rep(0.25, 4))

#######################################################################
## pwm_to_ppm
## Do the opposite of ppm_to_pwm.
m <- create_motif(type = "PWM")["motif"]
apply(m, 2, pwm_to_ppm, bkg = rep(0.25, 4))

#######################################################################
## 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(examplemotif, "TATATAT")
score_match(examplemotif, "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))

#######################################################################
## ungap
## Unset motif's gap status. Does not delete actual gap data unless
## delete = TRUE.
m <- create_motif()
m <- add_gap(m, 3, 2, 4)
m <- ungap(m)
# Restore gap data:
m <- add_gap(m)

[Package universalmotif version 1.4.10 Index]

Motif-related utility functions.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples