Type: Package
Title: Dependency Logo
Version: 1.2.1
Maintainer: Jan Grau <grau@informatik.uni-halle.de>
Description: Plots dependency logos from a set of aligned input sequences.
License: GPL-3
NeedsCompilation: no
RoxygenNote: 7.3.1
Encoding: UTF-8
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, testthat
Packaged: 2024-02-15 16:21:10 UTC; dev
Author: Jan Grau [aut, cre], Jens Keilwagen [aut], Martin Nettling [aut]
Repository: CRAN
Date/Publication: 2024-02-15 17:00:02 UTC

built alphabet

Description

builts an object of class Alphabet from the given set of symbols and colors

Usage

Alphabet(chars, cols)

Arguments

chars

set of symbols

cols

set of colors; one for each symbol

Value

the Alphabet object

Author(s)

Martin Nettling

Examples

DNA <- Alphabet(c("A", "C", "G", "T"), c("green4", "blue", "orange", "red"))

Create DLData object

Description

Creates a new DLData object from a set of input sequences.

Usage

DLData(
  sequences,
  weights = NULL,
  symbols = alphabet.dna$alphabet,
  colors = alphabet.dna$colors,
  delim = "",
  sortByWeights = !is.null(weights),
  axis.labels = NULL
)

Arguments

sequences

the input sequences, may be provided as i) character vector or ii) a data.frame with sequences organized in rows and one symbol per column

weights

weights associated with the sequences, numeric vector of the same length as sequences has sequences

symbols

the symbols (alphabet) over which the sequences are defined

colors

colors for each of the symbols, not necessarily unique

delim

delimiter between the symbols in the input sequences, ignored if sequences as a data.frame

sortByWeights

if TRUE, sequences will be ordered by their weight in decreasing order

axis.labels

the labels of the individual sequence positions; if NULL, indexes from 1 to to total number of positions will be used

Details

Sequences may either be provided as a character vector or as a data.frame. All symbols occurring in these sequences need to be defined and assigned to colors, which are used for plotting later. Colors do not need to be unique, but symbols with identical colors may become indistinguishable in subsequent plots (which might even be desired, for instance, when visualizing protein properties instead of amino acids). Sequences may have an associated weight, which is used to order sequences, e.g., for creating chunks/blocks of sequences in subsequent plots (see chunks parameter of plotDeplogo).

Value

the DLData object

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

See Also

plotDeplogo

Examples

# creating a DLData object using default (DNA) alphabet and colors
# from a character vector with two entries
data <- DLData(c("ACGT", "ATTA"))

# creating a DLData object using a custom, binary alphabet and custom colors
data2 <- DLData(c("A,B,B,A,B", "A,B,B,A,A", "A,B,A,A,B"),
    symbols = c("A", "B"), colors = c("red","green"), delim = ",")

# creating a DLData object from a data frame 
# (created from a character vector, in this case)
vec <- c("A,B,B,A,B", "A,B,B,A,A", "A,B,A,A,B")
df <- as.data.frame(t(sapply(vec, function(a){strsplit(a, ",")[[1]]})))
data.df <- DLData(df, symbols = c("A", "B"), colors = c("red", "green"))

# creating a DLData object from sequences and weights, read from a tabular file
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data3 <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

DNA alphabet

Description

DNA alphabet

Usage

alphabet.dna

Format

An object of class list of length 2.

DNA alphabet with gaps

Description

DNA alphabet with gaps

Usage

alphabet.dna.gap

Format

An object of class list of length 2.

Amino acid alphabet

Description

Amino acid alphabet

Usage

alphabet.protein

Format

An object of class list of length 2.

Amino acid alphabet with gaps

Description

Amino acid alphabet with gaps

Usage

alphabet.protein.gap

Format

An object of class list of length 2.

RNA alphabet

Description

RNA alphabet

Usage

alphabet.rna

Format

An object of class list of length 2.

RNA alphabet with gaps

Description

RNA alphabet with gaps

Usage

alphabet.rna.gap

Format

An object of class list of length 2.

Plots a colorchart representation of a set of sequences

Description

This function is a low-level plotting function (using image with add=TRUE, internally).

Usage

colorchart(part, yoff, ic.scale = TRUE)

Arguments

part

the set of sequences as DLData object

yoff

the offset in y-direction within the current plot

ic.scale

ignored for colorcharts

Value

the vertical (y) offset after this plot

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[,1], weights = log1p(seqs[, 2]) )

# create high-level plot
plot(NULL, xlim = c(1, ncol(data$data) - 1), ylim = c(0, nrow(data$data)), 
    ylab = nrow(data$data), axes = FALSE)
# and add colorchart and axis
colorchart(data, yoff = nrow(data$data))
axis(1)

Rectangles of averaged colors

Description

Plots a representation of a set of sequences by rectangles of (scaled) averaged color values of the symbols at each position

Usage

deprects(part, yoff, ic.scale = TRUE)

Arguments

part

the set of sequences as DLData object

yoff

the offset in y-direction within the current plot

ic.scale

if TRUE, alpha values of colors will be assigned based on "information content" of the distribution at each position

Details

This function is a low-level plotting function (using rect, internally).

Value

the vertical (y) offset after this plot

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"),  
   stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1],weights = log1p(seqs[, 2]) )

# create high-level plot
plot(NULL, xlim = c(1, ncol(data$data) - 1), ylim = c(0, nrow(data$data)), 
    ylab = nrow(data$data), axes = FALSE)
# and add deprects and axis
deprects(data, yoff = nrow(data$data))
axis(1)

Filters columns (sequence positions) by conservation

Description

Filters columns based on the relative information content of each column which is the standard information content normalized to the interval [0,1], where 0 corresponds to uniform distribution and 1 to perfect conservation of one nucleotide or amino acid, respectively.

Usage

filter.by.conservation(relative.ic)

Arguments

relative.ic

the maximum relative information content allowed to retain a position

Value

function that, given a DLData object, returns TRUE for every column that does not exceed the specified relative information content

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

fun <- filter.by.conservation(relative.ic = 0.9)

Filters columns (sequence positions) by dependency

Description

Filters columns based on the average or maximum mutual information of a column to all other columns. Mutual information is normalized to to interval [0,1], where 0 corresponds to independence and 1 to perfect dependence.

Usage

filter.by.dependencies(mi.threshold, use.max = FALSE)

Arguments

mi.threshold

the minimum average or maximum mutual information required

use.max

if TRUE, the maximum and otherwise the average mutual information will be considered

Value

function that, given a DLData object, returns TRUE for every column that does exceed the specified average mutual information

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

fun <- filter.by.dependencies(mi.threshold = 0.3)

Filters columns (sequence positions) by gaps

Description

Filters columns (sequence positions) by gaps

Usage

filter.by.gaps(percent.gap)

Arguments

percent.gap

the maximum fraction of gaps allowed to retain a column

Value

function that, given a DLData object, returns TRUE for every column that does not exceed the specified number of gaps

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

fun <- filter.by.gaps(percent.gap = 0.1)

Filters data columns by some filter function

Description

Filters the columns of the input data, i.e., positions of input sequences, by a filter function that, given a DLData object, returns a list containing i) as element $selected a vector with entries TRUE for every column that should be retained in the filtered data and ii) as element $range the range of values obtained for the filtering criterion.

Usage

filterColumns(data, filter.fun)

Arguments

data

the data as DLData object

filter.fun

the filter function

Value

a DLData object containing the filtered columns and the indexes of the remaining in its axis.labels field

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

See Also

filter.by.gaps

filter.by.dependencies

filter.by.conservation

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

# create a filter function based on the percentage of gap symbols (at most 10%)
fun <- filter.by.gaps(percent.gap = 0.1)
data2 <- filterColumns(data, fun)

Compute dependencies between positions

Description

Computes the dependencies (as measures by mutual information) between all positions (columns) of discrete data. Specifically, it returns for each pair of positions (i,j) the mutual information I(X_i,X_j) multiplied by the number N of sequences (rows), which may also be used for testing the statistical significance of mutual information values, as for large N, 2*N*I(X_i,X_j) is approximately chi squared.

Usage

getDeps(data, ...)

## S3 method for class 'DLData'
getDeps(data, ...)

## S3 method for class 'data.frame'
getDeps(data, alphabet, ...)

Arguments

data

the data for computing mutual information. Either a DLData object or a data.frame; In the latter case, the symbols of the alphabet must be provided as a second parameter

...

the symbols of the alphabet as character vector, only if data is a data.frame

alphabet

only required when called on a data.frame

Value

a matrix of the mutual information values, where the diagonal is fixed to zero

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

data <- DLData(c("ACGT", "ATTA"))
deps <- getDeps(data)

Position weight matrix from DLData object

Description

Determines the position weight matrix from a DLData object as relative frequency of symbols in each column of the data slot.

Usage

getPWM(part)

## S3 method for class 'DLData'
getPWM(part)

Arguments

part

the DLData object

Value

the position weight matrix, where columns correspond to positions (columns of the DLData$data slot) and rows to symbols

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

data <- DLData(c("ACGT", "ATTA"))
getPWM(data)

Description

Plots a representation of a set of sequences as a sequence logo

Usage

logo(part, yoff, ic.scale = TRUE)

Arguments

part

the set of sequences as DLData object

yoff

the offset in y-direction within the current plot

ic.scale

if TRUE, symbols are scaled by "information content" of the distribution at each position

Details

This function is a low-level plotting function (using polygon, internally).

Value

the vertical (y) offset after this plot

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[,2]) )

# create high-level plot
plot(NULL, xlim = c(1, ncol(data$data) - 1), ylim = c(0, nrow(data$data)),  
   ylab = nrow(data$data), axes = FALSE)
# and add sequence logo and axis
logo(data, yoff = nrow(data$data))
axis(1)

Paritions data by most inter-dependent positions

Description

Partitions data by the nucleotides at the most inter-dependent positions as measures by pairwise mutual information. Paritioning is performed recursively on the resulting subsets until i) the number of sequences in a partition is less then minElements, ii) the average pairwise dependency between the current position and numBestForSorting other positions with the largest mutual information value drops below threshold, or iii) maxNum recursive splits have already been performed. If splitting results in smaller partitions than minElements, these are added to the smallest partition with more than minElements sequences.

Usage

partition(
  data,
  minElements = 10,
  threshold = 0.1,
  numBestForSorting = 3,
  maxNum = 6,
  sortByWeights = NULL,
  partition.by = NULL
)

## S3 method for class 'DLData'
partition(
  data,
  minElements = 10,
  threshold = 0.1,
  numBestForSorting = 3,
  maxNum = 6,
  sortByWeights = NULL,
  partition.by = NULL
)

Arguments

data

the data as DLData object

minElements

the minimum number of elements to perform a further split

threshold

the threshold on the average mutual information value

numBestForSorting

the number of dependencies to other positions considered

maxNum

the maximum number of recursive splits

sortByWeights

if TRUE, partitions are ordered by their average weight value, if false by frequency of symbols at the partitioning position otherwise. If NULL, the $sortByWeights value of the DLData object is used

partition.by

specify fixed positions to partition by

Value

the partitions as list of DLData objects

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"),
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[,2]) )

# partition data using default parameters
partitions <- partition(data)

# partition data using a threshold of 0.3 on the mutual 
# information value to the most dependent position, 
# sorting the resulting partitions by weight
partitions2 <- partition(data = data, threshold = 0.3, numBestForSorting = 1, sortByWeights = TRUE)

Plots blocks of data

Description

Plots the blocks of data in data by successive, vertically arranged sub-plots of the function provided as block.fun. If data is a single DLData object, one block is plotted. Further arguments are provided to block.fun.

Usage

plotBlocks(
  data,
  show.number = TRUE,
  block.fun = deprects,
  ic.scale = TRUE,
  add = FALSE,
  ...
)

## S3 method for class 'DLData'
plotBlocks(
  data,
  show.number = TRUE,
  block.fun = deprects,
  ic.scale = TRUE,
  add = FALSE,
  ...
)

## S3 method for class 'list'
plotBlocks(
  data,
  show.number = TRUE,
  block.fun = deprects,
  ic.scale = TRUE,
  add = FALSE,
  ...
)

Arguments

data

the data, a single DLData object or a list of DLData objects

show.number

if true, the number of sequences (in total) in data is displayed on the left side of the plot

block.fun

the function called for each of the blocks

ic.scale

if TRUE, output of block.fun may be scaled by "information content"

add

if TRUE, the plot is added to an existing plot

...

if add=FALSE forwarded to the internal call to plot

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

See Also

deprects

logo

colorchart

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

# plot all data
plotBlocks(data)

# partition data
partitions <- partition(data, threshold = 0.3)
# and plot partitions
plotBlocks(partitions)

# or plot partitions as sequence logos
plotBlocks(partitions, block.fun = logo)

Plots a graph representation of dependency values

Description

Plots a representation of dependency values as arcs between the sequence positions. Internally, dependency values are computed using getDeps on the data object.

Usage

plotDeparcs(
  data,
  axis.at.bottom = TRUE,
  add.legend = TRUE,
  show.pvals = FALSE,
  axis.labels = NULL,
  threshold = 0.1
)

Arguments

data

the DLData object containing the data

axis.at.bottom

if TRUE, the x-axis is shown at the bottom (side=1) of the plot, and at the top (side=3) otherwise

add.legend

if TRUE a legend of the color scale is added to the plot

show.pvals

if TRUE, -log10 p-values (computed by pchisq) are shown instead of mutual information values

axis.labels

the labels of the x-axis

threshold

threshold in mutual information values, edges below this value are not shown; ignored in show.pvals=TRUE

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[,1], weights = log1p(seqs[, 2]) )

# plot using default parameters
plotDeparcs(data)

# plot with axis at top, without a legend (color scale), and using p-values
plotDeparcs(data, axis.at.bottom = FALSE, add.legend = FALSE, show.pvals = TRUE)

Description

Plots a dependency logo

Usage

plotDeplogo(
  data,
  dep.fun = plotDeparcs,
  block.fun = deprects,
  summary.fun = logo,
  weight.fun = NULL,
  chunks = NULL,
  chunk.height = 800,
  summary.height = 100,
  minPercent = 0.03,
  threshold = 0.1,
  numBestForSorting = 3,
  maxNum = 6,
  sortByWeights = NULL,
  dep.fun.legend = TRUE,
  show.dependency.pvals = FALSE,
  axis.labels = NULL,
  weight.ratio = 5,
  partition.by = NULL,
  ...
)

## S3 method for class 'DLData'
plotDeplogo(
  data,
  dep.fun = plotDeparcs,
  block.fun = deprects,
  summary.fun = logo,
  weight.fun = NULL,
  chunks = NULL,
  chunk.height = 800,
  summary.height = 100,
  minPercent = 0.03,
  threshold = 0.1,
  numBestForSorting = 3,
  maxNum = 6,
  sortByWeights = NULL,
  dep.fun.legend = TRUE,
  show.dependency.pvals = FALSE,
  axis.labels = NULL,
  weight.ratio = 5,
  partition.by = NULL,
  ...
)

Arguments

data

the data, currently implemented for DLData objects

dep.fun

the function for plotting the representation of dependency values (as computed by getDeps)

block.fun

the function for plotting a representation of the individual partitions of the data generated in dependency logos.

summary.fun

the function for plotting a representation of the summary plot for (one chunk of) the data

weight.fun

the function for plotting a representation of the weights values of the sequences within one partition

chunks

the size of chunks the data is split into. The sum of the chunk sizes must not be greater than the number of data points in data; The default value of NULL corresponds to one chunk containing all data points

chunk.height

the (relative) height of the parts of the plot representing each of the chunks, one height for each chunk

summary.height

the (relative) height of the block summaries in the plot

minPercent

the minimum percentage of the (sub) data set that may constitute its own partition in the dependency logo

threshold

the threshold on the dependency value for further splits

numBestForSorting

the number of dependencies between position i and all other positions when computing the dependency value of position i

maxNum

the maximum number of splits allowed

sortByWeights

are partitions sorted by their average weight (descending)

dep.fun.legend

if TRUE, a legend of the color scale used for plotting the dependency values in dep.fun is added to the plot

show.dependency.pvals

is TRUE, p-values are used for plotting dependency values in dep.fun instead of mutual information values

axis.labels

labels for the x-axis, vector of the same length as the individual sequences

weight.ratio

the factor by which the plotting width for the main plot is larger than for weight.fun

partition.by

specify fixed positions to partition by

...

forwarded to the high-level plot that contains the blocks plotted by block.fun

Details

The function dep.fun provided for plotting the representation of dependencies is currently implemented in plotDeparcs and plotDepmatrix. Custom implementations must have the same signature as these functions and create a single plot without using layout (or similar).

The functions block.fun and summary.fun provided for plotting the representation of individual partitions of the data generated in dependency logos are currently implemented in deprects, colorchart, and logo. Custom implementations must have the same signature as these functions and create a single plot without using layout (or similar).

The function weight.fun for plotting a representation of the weights values of the sequences within one partition is currently implemented in subLines and subBoxes. Custom implementations must have the same signature as these functions and create a single plot without using layout (or similar).

Value

a list of DLData objects with the partitions created for the dependency logo

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1],weights = log1p(seqs[, 2]) )

# plot default dependency logo
plotDeplogo(data)

# refine threshold for clearer picture
plotDeplogo(data, threshold = 0.3)

# customize different parts of the plot
plotDeplogo(data, threshold = 0.3, dep.fun = plotDepmatrix, block.fun = colorchart)

# add plots of the weights
plotDeplogo(data, weight.fun = subBoxes)

Plots a matrix representation of dependency values

Description

Plots a representation of dependency values as a triangular matrix rotated by 45 degrees. Internally, dependency values are computed using getDeps on the data object.

Usage

plotDepmatrix(
  data,
  axis.at.bottom = TRUE,
  add.legend = TRUE,
  show.pvals = FALSE,
  axis.labels = NULL,
  threshold = 0.1
)

Arguments

data

the DLData object containing the data

axis.at.bottom

if TRUE, the x-axis is shown at the bottom (side=1) of the plot, and at the top (side=3) otherwise

add.legend

if TRUE a legend of the color scale is added to the plot

show.pvals

if TRUE, -log10 p-values (computed by pchisq) are shown instead of mutual information values

axis.labels

the labels of the x-axis

threshold

ignored

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

# plot using default parameters
plotDepmatrix(data)

# plot with axis at top, without a legend (color scale), and using p-values
plotDepmatrix(data, axis.at.bottom = FALSE, add.legend = FALSE, show.pvals = TRUE)

Replaces colors in DLData object

Description

Replaces colors in DLData object

Usage

replaceColors(data, colors)

## S3 method for class 'DLData'
replaceColors(data, colors)

Arguments

data

the data

colors

the new colors

Value

the modified DLData object

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

See Also

replaceColors

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

replaceColors(data, c("red", "green", "blue", "yellow"))

Reverse complement

Description

Determine the reverse complementary DLData object. Only works for DNA or RNA. Data may include gap symbols.

Usage

revcom(data)

## S3 method for class 'DLData'
revcom(data)

Arguments

data

the data

Value

the reverse complement

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

data <- DLData(c("ACGT", "ATTA"))
revcom(data)

Plots weights as boxplots

Description

Plots a representation of the weights of a list of DLData objects. Each entry of the list is shown as an independent boxplot.

Usage

subBoxes(sub.parts, range, axis.above = TRUE, axis.below = TRUE)

Arguments

sub.parts

a list of DLData objects

range

the range of values shown in the plot (i.e., the xlim value of the call to plot)

axis.above

if TRUE, an axis at the top of the plot (side=3) is shown

axis.below

if TRUE, an axis at the bottom of the plot (side=1) is shown

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "nrsf.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

# create dependency logo with plotted weights
plotDeplogo(data, threshold = 0.03, weight.fun = subBoxes)

Plots weights as lines

Description

Plots a representation of the weights of a list of DLData objects. Each entry of the list is shown as an independent line with the median value shown as a red vertical line. Plots of list entries are separated by horizontal grey lines.

Usage

subLines(sub.parts, range, axis.above = TRUE, axis.below = TRUE)

Arguments

sub.parts

a list of DLData objects

range

the range of values shown in the plot (i.e., the xlim value of the call to plot)

axis.above

if TRUE, an axis at the top of the plot (side=3) is shown

axis.below

if TRUE, an axis at the bottom of the plot (side=1) is shown

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "nrsf.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )

# create dependency logo with plotted weights
plotDeplogo(data, threshold = 0.03, weight.fun = subLines)

Suggests colors for symbols

Description

Suggests colors for the symbols in data based on the co-occurrence of symbols at common positions, weighted by the dependency values at those positions. The idea is to assign similar colors only to symbols that either mostly occur at different positions or that are present at positions with low inter-dependencies to other positions.

Usage

suggestColors(data)

## S3 method for class 'DLData'
suggestColors(data)

Arguments

data

the data

Value

the colors

Author(s)

Jan Grau <grau@informatik.uni-halle.de>

See Also

replaceColors

Examples

# read data and create DLData object
seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1] ,weights = log1p(seqs[, 2]) )

suggestColors(data)

Summarizing DLData objects

Description

summary method for class "DLData". The summary includes the number of sequences, the consensus sequence and the number of sequences in object that match the consensus.

Usage

## S3 method for class 'DLData'
summary(object, delete.gaps = FALSE, ...)

Arguments

object

an object of class "DLData"

delete.gaps

if gaps should be removed from the consensus

...

further arguments passed to or from other methods

Value

a list with elements members containing the number of sequences, consensus containing the consensus sequences, and equal.consensus containing the number of sequences in object that are identical to consensus

Author(s)

Jens Keilwagen, Jan Grau <grau@informatik.uni-halle.de>

Examples

seqs <- read.table(system.file("extdata", "cjun.txt", package = "DepLogo"), 
    stringsAsFactors = FALSE)
data <- DLData(sequences = seqs[, 1], weights = log1p(seqs[, 2]) )
summary(data)