utils-motif: Motif-related utility functions.

In bjmt/universalmotif: Import, Modify, and Export Motifs with R

Motif-related utility functions.

Description

Motif-related utility functions.

Usage

add_gap(motif, gaploc = ncol(motif)%/%2, mingap = 1, maxgap = 5)

average_ic(motifs, average = c("a.mean", "g.mean", "median", "fzt"))

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, allow.nonfinite = FALSE)

get_scores(motif, allow.nonfinite = FALSE)

icm_to_ppm(position)

motif_range(motif, use.freq = 1, allow.nonfinite = FALSE)

motif_score(motif, threshold = c(0, 1), use.freq = 1,
  allow.nonfinite = FALSE, threshold.type = c("total", "fromzero"))

log_string_pval(pval)

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)

prob_match(motif, match, allow.zero = TRUE)

prob_match_bkg(bkg, match)

pwm_to_ppm(position, bkg = numeric())

round_motif(motif, pct.tolerance = 0.05)

score_match(motif, match, allow.nonfinite = FALSE)

summarise_motifs(motifs, na.rm = TRUE)

ungap(motif, delete = FALSE)

Arguments

motif	Motif object to calculate scores from, or add/remove gap, or round.
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.
motifs	list A list of universalmotif motifs.
average	character(1) One of c("a.mean", "g.mean", "median", "fzt"). How to calculate the average motif information content.
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.
allow.nonfinite	logical(1) If FALSE, then apply a pseudocount if non-finite values are found in the PWM. Note that if the motif has a pseudocount greater than zero and the motif is not currently of type PWM, then this parameter has no effect as the pseudocount will be applied automatically when the motif is converted to a PWM internally. This value is set to FALSE by default in order to stay consistent with pre-version 1.8.0 behaviour. A message will be printed if a pseudocount is applied. To disable this, set options(pseudocount.warning=FALSE).
use.freq	numeric(1) Use regular motif or the respective multifreq representation.
threshold	numeric(1) Any number of numeric values between 0 and 1 representing score percentage.
threshold.type	character For "total", a threshold of zero represents the minimum possible score. This means the range of scores that can be extracted is from the minimum to the maximum possible scores. For "fromzero", a threshold of zero is a score of zero. This means the range of scores is from zero to the maximum. The "total" threshold type can only be used if no non-finite values are present in the PWM.
pval	character(1) String-formatted p-value.
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 Sequence string to calculate score from.
allow.zero	logical(1) If FALSE, apply a pseudocount if zero values are found in the background frequencies.
pct.tolerance	numeric(1) or character(1) The minimum tolerated proportion each letter must represent per position in order not to be rounded off, either as a numeric value from 0 to 1 or a percentage written as a string from "0%" to "100%".
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_range(): a named numeric vector of motif scores.

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

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

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 prob_match(): a numeric vector of probabilities.

For round_motif(): the input motif, rounded.

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, [e-mail]

See Also

create_motif()

Examples

data(examplemotif)
examplemotif0 <- examplemotif
examplemotif0["pseudocount"] <- 0

#######################################################################
## 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)

#######################################################################
## average_ic
## Calculate the average information content for a list of motifs.
m <- create_motif()
average_ic(m, "fzt")

#######################################################################
## 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, 0)
get_matches(examplemotif0, 0, allow.nonfinite = TRUE)

#######################################################################
## get_scores
## Get all possible scores for a motif
length(get_scores(examplemotif))
get_scores(examplemotif)
get_scores(examplemotif0, allow.nonfinite = TRUE)

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

#######################################################################
## motif_range
## Calculate the range of possible logodds scores for a motif
motif_range(examplemotif)
motif_range(examplemotif, allow.nonfinite = TRUE)

#######################################################################
## motif_score
## Calculate motif score from different thresholds
m <- normalize(examplemotif)
motif_score(m, c(0, 0.8, 1))
motif_score(examplemotif0, c(0, 0.8, 1), allow.nonfinite = TRUE,
   threshold.type = "fromzero")

#######################################################################
## log_string_pval
## Get the log of a string-formatted p-value
log_string_pval("1e-200")

#######################################################################
## 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))

#######################################################################
## prob_match, prob_match_bkg
## Calculate probability of a particular match based on background
## frequencies
prob_match(examplemotif, "TATATAT")
## Since this motif has a uniform background, the probability of
## finding any motif hit within the sequence is equal
prob_match(examplemotif, "TATATAG")
m <- examplemotif
m["bkg"] <- c(0.3, 0.2, 0.2, 0.3)
prob_match(m, "TATATAT")
## The prob_match_bkg alternative allows you to simply pass along the
## background frequencies
prob_match_bkg(c(A=0.3, C=0.2, G=0.2, T=0.3), c("TATATAT", "TATATAG"))

#######################################################################
## 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)

#######################################################################
## round_motif
## Round down letter scores to 0
m <- create_motif()
## Remove letters from positions which are less than 5% of the total
## position:
round_motif(m, pct.tolerance = 0.05)

#######################################################################
## score_match
## Calculate score of a particular match
score_match(examplemotif, "TATATAT")
score_match(examplemotif, "TATATAG")
score_match(examplemotif0, "TATATAT", allow.nonfinite = TRUE)
score_match(examplemotif0, "TATATAG", allow.nonfinite = TRUE)

#######################################################################
## 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)

bjmt/universalmotif documentation built on Nov. 16, 2024, 7:38 a.m.