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

### See Also

`create_motif()`

### 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]